Methods : Exploring Penalized Models for Handling High-Dimensional Survival Data
John Pauline Pineda
June 28, 2023
1. Table of Contents
This project explores different regularization methods for minimizing model complexity by promoting coefficient sparsity in high-dimensional survival data using various helpful packages in R. Using a Cox Proportional Hazards Regression model structure, penalty functions applied during coefficient estimation included the Least Absolute Shrinkage and Selection Operator, Elastic Net, Minimax Concave Penalty, Smoothly Clipped Absolute Deviation and Fused Least Absolute Shrinkage and Selection Operator. The predictive performance for each algorithm was evaluated using the time-dependent area under the receiver operating characteristic curve (AUROC) metric through both internal bootstrap and external validation methods. Model calibration was similarly assessed by plotting the predicted probabilities from the model versus the actual survival probabilities. The differences in survival time for different risk groups determined from the calibration analyses were additionally examined using the Kaplan-Meier survival curves.
Penalized regression is a form of variable selection method aimed at reducing model complexity by seeking a smaller subset of informative variables. This process keeps all the predictor variables in the model but constrains (regularizes) the regression coefficients by shrinking them toward zero, in addition to setting some coefficients exactly equal to zero, and is directly built as extensions of standard regression coefficient estimation by incorporating a penalty in the loss function. The algorithms applied in this study (mostly contained in the hdnom, ncvreg, survival, glmnet and penalizedpackages) attempt to evaluate various penalties for over-confidence in the parameter estimates and accept a slightly worse fit in order to have a more parsimonious model.
1.1 Sample Data
The peakVO2 dataset from the randomForestSRC package was used for this illustrated example.
Preliminary dataset assessment:
[A] 2231 rows (observations)
[B] 41 columns (variables)
[B.1] 1/41 survival event status = died variable (factor)
[B.1.1] Event = died=1
[B.1.2] Censored or Non-Event = died=0
[B.2] 1/41 survival time = ttodead variable (numeric)
[B.3] 39/41 predictors = All remaining variables (26/39 factor + 13/39 numeric)
Code Chunk | Output
##################################
# Loading R libraries
##################################
library(survival)
library(survminer)
library(mice)
library(foreign)
library(rms)
library(Hmisc)
library(VIM)
library(gridExtra)
library(finalfit)
library(knitr)
library(dplyr)
library(gtsummary)
library(tidyr)
library(purrr)
library(moments)
library(skimr)
library(caret)
library(corrplot)
library(randomForestSRC)
library(SurvMetrics)
library(pec)
library(hdnom)
library(ncvreg)
library(glmnet)
library(penalized)
##################################
# Loading source and
# formulating the train set
##################################
data(peakVO2)
peakVO2 <- as.data.frame(peakVO2)
##################################
# Converting survival time
# to discrete monthly data
##################################
peakVO2$ttodead <- ceiling(peakVO2$ttodead*12)
peakVO2.Source <- peakVO2
##################################
# Setting the appropriate data types
# for exploratory data analysis
##################################
peakVO2.Assessment <- (as.data.frame(sapply(peakVO2, function(x) max(x))))
names(peakVO2.Assessment) <- c("Max.Value")
peakVO2.Assessment$Variables <- rownames(peakVO2.Assessment)
peakVO2.Assessment## Max.Value Variables
## age 82.00000 age
## betablok 1.00000 betablok
## dilver 1.00000 dilver
## nifed 1.00000 nifed
## acei 1.00000 acei
## angioten.II 1.00000 angioten.II
## anti.arrhy 1.00000 anti.arrhy
## anti.coag 1.00000 anti.coag
## aspirin 1.00000 aspirin
## digoxin 1.00000 digoxin
## nitrates 1.00000 nitrates
## vasodilator 1.00000 vasodilator
## diuretic.loop 1.00000 diuretic.loop
## diuretic.thiazide 1.00000 diuretic.thiazide
## diuretic.potassium.spar 1.00000 diuretic.potassium.spar
## lipidrx.statin 1.00000 lipidrx.statin
## insulin 1.00000 insulin
## surgery.pacemaker 1.00000 surgery.pacemaker
## surgery.cabg 1.00000 surgery.cabg
## surgery.pci 1.00000 surgery.pci
## surgery.aicd.implant 1.00000 surgery.aicd.implant
## resting.systolic.bp 184.00000 resting.systolic.bp
## resting.hr 124.00000 resting.hr
## smknow 1.00000 smknow
## q.wave.mi 1.00000 q.wave.mi
## bmi 59.61593 bmi
## niddm 1.00000 niddm
## lvef.metabl 40.00000 lvef.metabl
## peak.rer 1.80000 peak.rer
## peak.vo2 43.80000 peak.vo2
## interval 1415.00000 interval
## cad 1.00000 cad
## died 1.00000 died
## ttodead 127.00000 ttodead
## bun 129.00000 bun
## sodium 152.00000 sodium
## hgb 19.20000 hgb
## glucose 424.00000 glucose
## male 1.00000 male
## black 1.00000 black
## crcl 385.84097 crcl(peakVO2.FactorNames <- peakVO2.Assessment[peakVO2.Assessment[,1]==1,2])## [1] "betablok" "dilver"
## [3] "nifed" "acei"
## [5] "angioten.II" "anti.arrhy"
## [7] "anti.coag" "aspirin"
## [9] "digoxin" "nitrates"
## [11] "vasodilator" "diuretic.loop"
## [13] "diuretic.thiazide" "diuretic.potassium.spar"
## [15] "lipidrx.statin" "insulin"
## [17] "surgery.pacemaker" "surgery.cabg"
## [19] "surgery.pci" "surgery.aicd.implant"
## [21] "smknow" "q.wave.mi"
## [23] "niddm" "cad"
## [25] "died" "male"
## [27] "black"peakVO2.Factor <- peakVO2[,peakVO2.FactorNames]
peakVO2.Factor <- as.data.frame(lapply(peakVO2.Factor, factor))
peakVO2.Numeric <- as.data.frame(peakVO2[,!names(peakVO2) %in% peakVO2.FactorNames])
peakVO2 <- data.frame(peakVO2.Numeric,peakVO2.Factor)
##################################
# Performing a general exploration of the data set
##################################
dim(peakVO2)## [1] 2231 41str(peakVO2)## 'data.frame': 2231 obs. of 41 variables:
## $ age : int 74 77 79 67 79 51 56 69 41 45 ...
## $ resting.systolic.bp : int 90 134 108 122 130 122 100 104 108 100 ...
## $ resting.hr : int 74 53 104 96 94 62 79 72 71 84 ...
## $ bmi : num 26.1 20.8 20.7 26.7 23.6 ...
## $ lvef.metabl : num 15 20 40 25 20 10 10 15 20 25 ...
## $ peak.rer : num 1.12 0.98 1.15 1.21 1.06 0.98 0.91 1.2 1.17 0.89 ...
## $ peak.vo2 : num 11.9 24 11.2 15.6 17.2 15.7 6 15.9 23.8 15 ...
## $ interval : int 374 755 225 405 580 635 145 480 840 453 ...
## $ ttodead : num 15 44 3 127 45 15 13 11 122 9 ...
## $ bun : num 25 20 29.7 27.9 39 ...
## $ sodium : num 141 138 140 139 143 ...
## $ hgb : num 14.6 13.1 12.9 13.2 12.5 ...
## $ glucose : num 89 90 98.7 136.9 104 ...
## $ crcl : num 49.9 60.4 33.6 69.3 44.7 ...
## $ betablok : Factor w/ 2 levels "0","1": 2 2 1 1 2 1 1 1 1 1 ...
## $ dilver : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ nifed : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ acei : Factor w/ 2 levels "0","1": 1 2 1 2 1 2 2 2 2 2 ...
## $ angioten.II : Factor w/ 2 levels "0","1": 2 1 1 1 2 1 1 1 1 1 ...
## $ anti.arrhy : Factor w/ 2 levels "0","1": 2 1 1 1 1 2 2 2 1 1 ...
## $ anti.coag : Factor w/ 2 levels "0","1": 2 1 1 2 1 1 2 1 1 2 ...
## $ aspirin : Factor w/ 2 levels "0","1": 2 2 2 1 2 2 1 2 1 1 ...
## $ digoxin : Factor w/ 2 levels "0","1": 1 2 2 2 1 2 2 2 2 2 ...
## $ nitrates : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 1 ...
## $ vasodilator : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ diuretic.loop : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 1 2 ...
## $ diuretic.thiazide : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ diuretic.potassium.spar: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 1 1 ...
## $ lipidrx.statin : Factor w/ 2 levels "0","1": 2 1 1 1 2 1 1 2 1 1 ...
## $ insulin : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ surgery.pacemaker : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 2 ...
## $ surgery.cabg : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
## $ surgery.pci : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
## $ surgery.aicd.implant : Factor w/ 2 levels "0","1": 2 1 1 1 1 2 1 2 1 1 ...
## $ smknow : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ q.wave.mi : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ niddm : Factor w/ 2 levels "0","1": 1 1 1 2 2 1 1 1 1 1 ...
## $ cad : Factor w/ 2 levels "0","1": 2 1 1 1 2 2 1 1 1 1 ...
## $ died : Factor w/ 2 levels "0","1": 1 1 2 1 2 2 2 2 1 2 ...
## $ male : Factor w/ 2 levels "0","1": 2 1 2 2 2 1 1 2 1 1 ...
## $ black : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 2 1 1 ...summary(peakVO2)## age resting.systolic.bp resting.hr bmi
## Min. :18.00 Min. : 80.0 Min. : 39.00 Min. :14.18
## 1st Qu.:48.00 1st Qu.: 98.0 1st Qu.: 66.00 1st Qu.:24.52
## Median :55.00 Median :110.0 Median : 75.00 Median :27.78
## Mean :54.22 Mean :110.5 Mean : 76.18 Mean :28.50
## 3rd Qu.:62.00 3rd Qu.:122.0 3rd Qu.: 86.00 3rd Qu.:31.89
## Max. :82.00 Max. :184.0 Max. :124.00 Max. :59.62
## lvef.metabl peak.rer peak.vo2 interval
## Min. : 5.00 Min. :0.079 Min. : 4.20 Min. : 21.0
## 1st Qu.:15.00 1st Qu.:1.020 1st Qu.:12.80 1st Qu.: 345.0
## Median :20.00 Median :1.100 Median :15.70 Median : 480.0
## Mean :20.26 Mean :1.079 Mean :16.27 Mean : 503.3
## 3rd Qu.:25.00 3rd Qu.:1.140 3rd Qu.:19.30 3rd Qu.: 641.0
## Max. :40.00 Max. :1.800 Max. :43.80 Max. :1415.0
## ttodead bun sodium hgb
## Min. : 1.00 Min. : 4.322 Min. :120.0 Min. : 7.20
## 1st Qu.: 30.00 1st Qu.: 17.000 1st Qu.:138.0 1st Qu.:12.80
## Median : 62.00 Median : 23.000 Median :139.8 Median :13.58
## Mean : 62.75 Mean : 25.278 Mean :139.5 Mean :13.54
## 3rd Qu.: 95.00 3rd Qu.: 29.334 3rd Qu.:141.0 3rd Qu.:14.40
## Max. :127.00 Max. :129.000 Max. :152.0 Max. :19.20
## glucose crcl betablok dilver nifed acei
## Min. : 37.00 Min. : 5.76 0: 802 0:2215 0:2132 0: 520
## 1st Qu.: 87.00 1st Qu.: 60.93 1:1429 1: 16 1: 99 1:1711
## Median : 96.93 Median : 83.13
## Mean :109.35 Mean : 90.71
## 3rd Qu.:114.88 3rd Qu.:110.33
## Max. :424.00 Max. :385.84
## angioten.II anti.arrhy anti.coag aspirin digoxin nitrates vasodilator
## 0:1941 0:1722 0:1332 0:1193 0: 661 0:1492 0:2095
## 1: 290 1: 509 1: 899 1:1038 1:1570 1: 739 1: 136
##
##
##
##
## diuretic.loop diuretic.thiazide diuretic.potassium.spar lipidrx.statin
## 0: 351 0:1952 0:1582 0:1381
## 1:1880 1: 279 1: 649 1: 850
##
##
##
##
## insulin surgery.pacemaker surgery.cabg surgery.pci surgery.aicd.implant
## 0:2016 0:1729 0:1637 0:1755 0:1584
## 1: 215 1: 502 1: 594 1: 476 1: 647
##
##
##
##
## smknow q.wave.mi niddm cad died male black
## 0:1772 0:1952 0:1881 0:1325 0:1505 0: 602 0:1965
## 1: 459 1: 279 1: 350 1: 906 1: 726 1:1629 1: 266
##
##
##
## ##################################
# Formulating a data type assessment summary
##################################
PDA <- peakVO2
(PDA.Summary <- data.frame(
Column.Index=c(1:length(names(PDA))),
Column.Name= names(PDA),
Column.Type=sapply(PDA, function(x) class(x)),
row.names=NULL)
)## Column.Index Column.Name Column.Type
## 1 1 age integer
## 2 2 resting.systolic.bp integer
## 3 3 resting.hr integer
## 4 4 bmi numeric
## 5 5 lvef.metabl numeric
## 6 6 peak.rer numeric
## 7 7 peak.vo2 numeric
## 8 8 interval integer
## 9 9 ttodead numeric
## 10 10 bun numeric
## 11 11 sodium numeric
## 12 12 hgb numeric
## 13 13 glucose numeric
## 14 14 crcl numeric
## 15 15 betablok factor
## 16 16 dilver factor
## 17 17 nifed factor
## 18 18 acei factor
## 19 19 angioten.II factor
## 20 20 anti.arrhy factor
## 21 21 anti.coag factor
## 22 22 aspirin factor
## 23 23 digoxin factor
## 24 24 nitrates factor
## 25 25 vasodilator factor
## 26 26 diuretic.loop factor
## 27 27 diuretic.thiazide factor
## 28 28 diuretic.potassium.spar factor
## 29 29 lipidrx.statin factor
## 30 30 insulin factor
## 31 31 surgery.pacemaker factor
## 32 32 surgery.cabg factor
## 33 33 surgery.pci factor
## 34 34 surgery.aicd.implant factor
## 35 35 smknow factor
## 36 36 q.wave.mi factor
## 37 37 niddm factor
## 38 38 cad factor
## 39 39 died factor
## 40 40 male factor
## 41 41 black factor1.2 Data Quality Assessment
Data quality assessment:
[A] No missing observations noted for any variable.
[B] Low variance observed for 10 variables with First.Second.Mode.Ratio>5.
[B.1] dilver variable (factor)
[B.2] nifed variable (factor)
[B.3] angioten.II variable (factor)
[B.4] vasodilator variable (factor)
[B.5] diuretic.loop variable (factor)
[B.6] diuretic.thiazide variable (factor)
[B.7] insulin variable (factor)
[B.8] q.wave.mi variable (factor)
[B.9] niddm variable (factor)
[B.10] black variable (factor)
[C] No low variance observed for any variable with Unique.Count.Ratio<0.01.
[D] No high skewness observed for any variable with Skewness>3 or Skewness<(-3).
Code Chunk | Output
##################################
# Loading dataset
##################################
DQA <- peakVO2
##################################
# Formulating an overall data quality assessment summary
##################################
(DQA.Summary <- data.frame(
Column.Index=c(1:length(names(DQA))),
Column.Name= names(DQA),
Column.Type=sapply(DQA, function(x) class(x)),
Row.Count=sapply(DQA, function(x) nrow(DQA)),
NA.Count=sapply(DQA,function(x)sum(is.na(x))),
Fill.Rate=sapply(DQA,function(x)format(round((sum(!is.na(x))/nrow(DQA)),3),nsmall=3)),
row.names=NULL)
)## Column.Index Column.Name Column.Type Row.Count NA.Count
## 1 1 age integer 2231 0
## 2 2 resting.systolic.bp integer 2231 0
## 3 3 resting.hr integer 2231 0
## 4 4 bmi numeric 2231 0
## 5 5 lvef.metabl numeric 2231 0
## 6 6 peak.rer numeric 2231 0
## 7 7 peak.vo2 numeric 2231 0
## 8 8 interval integer 2231 0
## 9 9 ttodead numeric 2231 0
## 10 10 bun numeric 2231 0
## 11 11 sodium numeric 2231 0
## 12 12 hgb numeric 2231 0
## 13 13 glucose numeric 2231 0
## 14 14 crcl numeric 2231 0
## 15 15 betablok factor 2231 0
## 16 16 dilver factor 2231 0
## 17 17 nifed factor 2231 0
## 18 18 acei factor 2231 0
## 19 19 angioten.II factor 2231 0
## 20 20 anti.arrhy factor 2231 0
## 21 21 anti.coag factor 2231 0
## 22 22 aspirin factor 2231 0
## 23 23 digoxin factor 2231 0
## 24 24 nitrates factor 2231 0
## 25 25 vasodilator factor 2231 0
## 26 26 diuretic.loop factor 2231 0
## 27 27 diuretic.thiazide factor 2231 0
## 28 28 diuretic.potassium.spar factor 2231 0
## 29 29 lipidrx.statin factor 2231 0
## 30 30 insulin factor 2231 0
## 31 31 surgery.pacemaker factor 2231 0
## 32 32 surgery.cabg factor 2231 0
## 33 33 surgery.pci factor 2231 0
## 34 34 surgery.aicd.implant factor 2231 0
## 35 35 smknow factor 2231 0
## 36 36 q.wave.mi factor 2231 0
## 37 37 niddm factor 2231 0
## 38 38 cad factor 2231 0
## 39 39 died factor 2231 0
## 40 40 male factor 2231 0
## 41 41 black factor 2231 0
## Fill.Rate
## 1 1.000
## 2 1.000
## 3 1.000
## 4 1.000
## 5 1.000
## 6 1.000
## 7 1.000
## 8 1.000
## 9 1.000
## 10 1.000
## 11 1.000
## 12 1.000
## 13 1.000
## 14 1.000
## 15 1.000
## 16 1.000
## 17 1.000
## 18 1.000
## 19 1.000
## 20 1.000
## 21 1.000
## 22 1.000
## 23 1.000
## 24 1.000
## 25 1.000
## 26 1.000
## 27 1.000
## 28 1.000
## 29 1.000
## 30 1.000
## 31 1.000
## 32 1.000
## 33 1.000
## 34 1.000
## 35 1.000
## 36 1.000
## 37 1.000
## 38 1.000
## 39 1.000
## 40 1.000
## 41 1.000##################################
# Listing all descriptors
##################################
DQA.Descriptors <- DQA
##################################
# Listing all numeric Descriptors
##################################
DQA.Descriptors.Numeric <- DQA.Descriptors[,sapply(DQA.Descriptors, is.numeric)]
if (length(names(DQA.Descriptors.Numeric))>0) {
print(paste0("There are ",
(length(names(DQA.Descriptors.Numeric))),
" numeric descriptor variable(s)."))
} else {
print("There are no numeric descriptor variables.")
}## [1] "There are 14 numeric descriptor variable(s)."##################################
# Listing all factor Descriptors
##################################
DQA.Descriptors.Factor <- DQA.Descriptors[,sapply(DQA.Descriptors, is.factor)]
if (length(names(DQA.Descriptors.Factor))>0) {
print(paste0("There are ",
(length(names(DQA.Descriptors.Factor))),
" factor descriptor variable(s)."))
} else {
print("There are no factor descriptor variables.")
}## [1] "There are 27 factor descriptor variable(s)."##################################
# Formulating a data quality assessment summary for factor Descriptors
##################################
if (length(names(DQA.Descriptors.Factor))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = x[!(x %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return("x"),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Descriptors.Factor.Summary <- data.frame(
Column.Name= names(DQA.Descriptors.Factor),
Column.Type=sapply(DQA.Descriptors.Factor, function(x) class(x)),
Unique.Count=sapply(DQA.Descriptors.Factor, function(x) length(unique(x))),
First.Mode.Value=sapply(DQA.Descriptors.Factor, function(x) as.character(FirstModes(x)[1])),
Second.Mode.Value=sapply(DQA.Descriptors.Factor, function(x) as.character(SecondModes(x)[1])),
First.Mode.Count=sapply(DQA.Descriptors.Factor, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Descriptors.Factor, function(x) sum(na.omit(x) == SecondModes(x)[1])),
Unique.Count.Ratio=sapply(DQA.Descriptors.Factor, function(x) format(round((length(unique(x))/nrow(DQA.Descriptors.Factor)),3), nsmall=3)),
First.Second.Mode.Ratio=sapply(DQA.Descriptors.Factor, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
row.names=NULL)
)
}## Column.Name Column.Type Unique.Count First.Mode.Value
## 1 betablok factor 2 1
## 2 dilver factor 2 0
## 3 nifed factor 2 0
## 4 acei factor 2 1
## 5 angioten.II factor 2 0
## 6 anti.arrhy factor 2 0
## 7 anti.coag factor 2 0
## 8 aspirin factor 2 0
## 9 digoxin factor 2 1
## 10 nitrates factor 2 0
## 11 vasodilator factor 2 0
## 12 diuretic.loop factor 2 1
## 13 diuretic.thiazide factor 2 0
## 14 diuretic.potassium.spar factor 2 0
## 15 lipidrx.statin factor 2 0
## 16 insulin factor 2 0
## 17 surgery.pacemaker factor 2 0
## 18 surgery.cabg factor 2 0
## 19 surgery.pci factor 2 0
## 20 surgery.aicd.implant factor 2 0
## 21 smknow factor 2 0
## 22 q.wave.mi factor 2 0
## 23 niddm factor 2 0
## 24 cad factor 2 0
## 25 died factor 2 0
## 26 male factor 2 1
## 27 black factor 2 0
## Second.Mode.Value First.Mode.Count Second.Mode.Count Unique.Count.Ratio
## 1 0 1429 802 0.001
## 2 1 2215 16 0.001
## 3 1 2132 99 0.001
## 4 0 1711 520 0.001
## 5 1 1941 290 0.001
## 6 1 1722 509 0.001
## 7 1 1332 899 0.001
## 8 1 1193 1038 0.001
## 9 0 1570 661 0.001
## 10 1 1492 739 0.001
## 11 1 2095 136 0.001
## 12 0 1880 351 0.001
## 13 1 1952 279 0.001
## 14 1 1582 649 0.001
## 15 1 1381 850 0.001
## 16 1 2016 215 0.001
## 17 1 1729 502 0.001
## 18 1 1637 594 0.001
## 19 1 1755 476 0.001
## 20 1 1584 647 0.001
## 21 1 1772 459 0.001
## 22 1 1952 279 0.001
## 23 1 1881 350 0.001
## 24 1 1325 906 0.001
## 25 1 1505 726 0.001
## 26 0 1629 602 0.001
## 27 1 1965 266 0.001
## First.Second.Mode.Ratio
## 1 1.782
## 2 138.438
## 3 21.535
## 4 3.290
## 5 6.693
## 6 3.383
## 7 1.482
## 8 1.149
## 9 2.375
## 10 2.019
## 11 15.404
## 12 5.356
## 13 6.996
## 14 2.438
## 15 1.625
## 16 9.377
## 17 3.444
## 18 2.756
## 19 3.687
## 20 2.448
## 21 3.861
## 22 6.996
## 23 5.374
## 24 1.462
## 25 2.073
## 26 2.706
## 27 7.387##################################
# Formulating a data quality assessment summary for numeric Descriptors
##################################
if (length(names(DQA.Descriptors.Numeric))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = na.omit(x)[!(na.omit(x) %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return(0.00001),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Descriptors.Numeric.Summary <- data.frame(
Column.Name= names(DQA.Descriptors.Numeric),
Column.Type=sapply(DQA.Descriptors.Numeric, function(x) class(x)),
Unique.Count=sapply(DQA.Descriptors.Numeric, function(x) length(unique(x))),
Unique.Count.Ratio=sapply(DQA.Descriptors.Numeric, function(x) format(round((length(unique(x))/nrow(DQA.Descriptors.Numeric)),3), nsmall=3)),
First.Mode.Value=sapply(DQA.Descriptors.Numeric, function(x) format(round((FirstModes(x)[1]),3),nsmall=3)),
Second.Mode.Value=sapply(DQA.Descriptors.Numeric, function(x) format(round((SecondModes(x)[1]),3),nsmall=3)),
First.Mode.Count=sapply(DQA.Descriptors.Numeric, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Descriptors.Numeric, function(x) sum(na.omit(x) == SecondModes(x)[1])),
First.Second.Mode.Ratio=sapply(DQA.Descriptors.Numeric, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
Minimum=sapply(DQA.Descriptors.Numeric, function(x) format(round(min(x,na.rm = TRUE),3), nsmall=3)),
Mean=sapply(DQA.Descriptors.Numeric, function(x) format(round(mean(x,na.rm = TRUE),3), nsmall=3)),
Median=sapply(DQA.Descriptors.Numeric, function(x) format(round(median(x,na.rm = TRUE),3), nsmall=3)),
Maximum=sapply(DQA.Descriptors.Numeric, function(x) format(round(max(x,na.rm = TRUE),3), nsmall=3)),
Skewness=sapply(DQA.Descriptors.Numeric, function(x) format(round(skewness(x,na.rm = TRUE),3), nsmall=3)),
Kurtosis=sapply(DQA.Descriptors.Numeric, function(x) format(round(kurtosis(x,na.rm = TRUE),3), nsmall=3)),
Percentile25th=sapply(DQA.Descriptors.Numeric, function(x) format(round(quantile(x,probs=0.25,na.rm = TRUE),3), nsmall=3)),
Percentile75th=sapply(DQA.Descriptors.Numeric, function(x) format(round(quantile(x,probs=0.75,na.rm = TRUE),3), nsmall=3)),
row.names=NULL)
)
}## Column.Name Column.Type Unique.Count Unique.Count.Ratio
## 1 age integer 65 0.029
## 2 resting.systolic.bp integer 55 0.025
## 3 resting.hr integer 82 0.037
## 4 bmi numeric 2144 0.961
## 5 lvef.metabl numeric 38 0.017
## 6 peak.rer numeric 75 0.034
## 7 peak.vo2 numeric 252 0.113
## 8 interval integer 594 0.266
## 9 ttodead numeric 127 0.057
## 10 bun numeric 936 0.420
## 11 sodium numeric 881 0.395
## 12 hgb numeric 1004 0.450
## 13 glucose numeric 1060 0.475
## 14 crcl numeric 2213 0.992
## First.Mode.Value Second.Mode.Value First.Mode.Count Second.Mode.Count
## 1 55.000 60.000 94 90
## 2 110.000 100.000 174 173
## 3 70.000 75.000 87 81
## 4 27.070 25.417 3 2
## 5 20.000 15.000 565 529
## 6 1.100 1.120 173 157
## 7 14.800 16.000 28 25
## 8 480.000 600.000 146 129
## 9 14.000 96.000 30 29
## 10 18.000 14.000 78 76
## 11 139.000 140.000 173 163
## 12 13.500 14.300 45 34
## 13 94.000 87.000 40 34
## 14 94.444 104.833 3 2
## First.Second.Mode.Ratio Minimum Mean Median Maximum Skewness Kurtosis
## 1 1.044 18.000 54.223 55.000 82.000 -0.428 3.045
## 2 1.006 80.000 110.509 110.000 184.000 0.650 3.336
## 3 1.074 39.000 76.182 75.000 124.000 0.352 2.887
## 4 1.500 14.183 28.499 27.777 59.616 0.782 4.113
## 5 1.068 5.000 20.261 20.000 40.000 0.655 3.067
## 6 1.102 0.079 1.079 1.100 1.800 -1.490 12.741
## 7 1.120 4.200 16.275 15.700 43.800 0.732 4.113
## 8 1.132 21.000 503.255 480.000 1415.000 0.514 3.219
## 9 1.034 1.000 62.750 62.000 127.000 0.049 1.771
## 10 1.026 4.322 25.278 23.000 129.000 2.250 11.535
## 11 1.061 120.000 139.451 139.804 152.000 -0.834 7.204
## 12 1.324 7.200 13.542 13.585 19.200 -0.395 4.308
## 13 1.176 37.000 109.352 96.929 424.000 2.814 13.907
## 14 1.500 5.760 90.709 83.131 385.841 1.453 6.734
## Percentile25th Percentile75th
## 1 48.000 62.000
## 2 98.000 122.000
## 3 66.000 86.000
## 4 24.523 31.888
## 5 15.000 25.000
## 6 1.020 1.140
## 7 12.800 19.300
## 8 345.000 641.000
## 9 30.000 95.000
## 10 17.000 29.334
## 11 138.000 141.000
## 12 12.803 14.400
## 13 87.000 114.880
## 14 60.935 110.333##################################
# Identifying potential data quality issues
##################################
##################################
# Checking for missing observations
##################################
if ((nrow(DQA.Summary[DQA.Summary$NA.Count>0,]))>0){
print(paste0("Missing observations noted for ",
(nrow(DQA.Summary[DQA.Summary$NA.Count>0,])),
" variable(s) with NA.Count>0 and Fill.Rate<1.0."))
DQA.Summary[DQA.Summary$NA.Count>0,]
} else {
print("No missing observations noted.")
}## [1] "No missing observations noted."##################################
# Checking for zero or near-zero variance Descriptors
##################################
if (length(names(DQA.Descriptors.Factor))==0) {
print("No factor descriptors noted.")
} else if (nrow(DQA.Descriptors.Factor.Summary[as.numeric(as.character(DQA.Descriptors.Factor.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Descriptors.Factor.Summary[as.numeric(as.character(DQA.Descriptors.Factor.Summary$First.Second.Mode.Ratio))>5,])),
" factor variable(s) with First.Second.Mode.Ratio>5."))
DQA.Descriptors.Factor.Summary[as.numeric(as.character(DQA.Descriptors.Factor.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance factor descriptors due to high first-second mode ratio noted.")
}## [1] "Low variance observed for 10 factor variable(s) with First.Second.Mode.Ratio>5."## Column.Name Column.Type Unique.Count First.Mode.Value
## 2 dilver factor 2 0
## 3 nifed factor 2 0
## 5 angioten.II factor 2 0
## 11 vasodilator factor 2 0
## 12 diuretic.loop factor 2 1
## 13 diuretic.thiazide factor 2 0
## 16 insulin factor 2 0
## 22 q.wave.mi factor 2 0
## 23 niddm factor 2 0
## 27 black factor 2 0
## Second.Mode.Value First.Mode.Count Second.Mode.Count Unique.Count.Ratio
## 2 1 2215 16 0.001
## 3 1 2132 99 0.001
## 5 1 1941 290 0.001
## 11 1 2095 136 0.001
## 12 0 1880 351 0.001
## 13 1 1952 279 0.001
## 16 1 2016 215 0.001
## 22 1 1952 279 0.001
## 23 1 1881 350 0.001
## 27 1 1965 266 0.001
## First.Second.Mode.Ratio
## 2 138.438
## 3 21.535
## 5 6.693
## 11 15.404
## 12 5.356
## 13 6.996
## 16 9.377
## 22 6.996
## 23 5.374
## 27 7.387if (length(names(DQA.Descriptors.Numeric))==0) {
print("No numeric descriptors noted.")
} else if (nrow(DQA.Descriptors.Numeric.Summary[as.numeric(as.character(DQA.Descriptors.Numeric.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Descriptors.Numeric.Summary[as.numeric(as.character(DQA.Descriptors.Numeric.Summary$First.Second.Mode.Ratio))>5,])),
" numeric variable(s) with First.Second.Mode.Ratio>5."))
DQA.Descriptors.Numeric.Summary[as.numeric(as.character(DQA.Descriptors.Numeric.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance numeric descriptors due to high first-second mode ratio noted.")
}## [1] "No low variance numeric descriptors due to high first-second mode ratio noted."if (length(names(DQA.Descriptors.Numeric))==0) {
print("No numeric descriptors noted.")
} else if (nrow(DQA.Descriptors.Numeric.Summary[as.numeric(as.character(DQA.Descriptors.Numeric.Summary$Unique.Count.Ratio))<0.01,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Descriptors.Numeric.Summary[as.numeric(as.character(DQA.Descriptors.Numeric.Summary$Unique.Count.Ratio))<0.01,])),
" numeric variable(s) with Unique.Count.Ratio<0.01."))
DQA.Descriptors.Numeric.Summary[as.numeric(as.character(DQA.Descriptors.Numeric.Summary$Unique.Count.Ratio))<0.01,]
} else {
print("No low variance numeric descriptors due to low unique count ratio noted.")
}## [1] "No low variance numeric descriptors due to low unique count ratio noted."##################################
# Checking for skewed Descriptors
##################################
if (length(names(DQA.Descriptors.Numeric))==0) {
print("No numeric descriptors noted.")
} else if (nrow(DQA.Descriptors.Numeric.Summary[as.numeric(as.character(DQA.Descriptors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Descriptors.Numeric.Summary$Skewness))<(-3),])>0){
print(paste0("High skewness observed for ",
(nrow(DQA.Descriptors.Numeric.Summary[as.numeric(as.character(DQA.Descriptors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Descriptors.Numeric.Summary$Skewness))<(-3),])),
" numeric variable(s) with Skewness>3 or Skewness<(-3)."))
DQA.Descriptors.Numeric.Summary[as.numeric(as.character(DQA.Descriptors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Descriptors.Numeric.Summary$Skewness))<(-3),]
} else {
print("No skewed numeric descriptors noted.")
}## [1] "No skewed numeric descriptors noted."1.3 Data Preprocessing
1.3.1 Outlier
Outlier data assessment:
[A] Outliers noted for 12 variables with the numeric data visualized through a boxplot including observations classified as suspected outliers using the IQR criterion. The IQR criterion means that all observations above the (75th percentile + 1.5 x IQR) or below the (25th percentile - 1.5 x IQR) are suspected outliers, where IQR is the difference between the third quartile (75th percentile) and first quartile (25th percentile). Outlier treatment for numerical stability remains optional depending on potential model requirements for the subsequent steps.
[A.1] age variable (33 outliers detected)
[A.2] resting.systolic.bp variable (26 outliers detected)
[A.3] resting.hr variable (9 outliers detected)
[A.4] bmi variable (34 outliers detected)
[A.5] peak.rer variable (102 outliers detected)
[A.6] peak.vo2 variable (40 outliers detected)
[A.7] interval variable (25 outliers detected)
[A.8] bun variable (116 outliers detected)
[A.9] sodium variable (140 outliers detected)
[A.10] hgb variable (83 outliers detected)
[A.11] glucose variable (222 outliers detected)
[A.12] crcl variable (82 outliers detected)
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- peakVO2
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("died","ttodead")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
##################################
# Identifying outliers for the numeric predictors
##################################
OutlierCountList <- c()
for (i in 1:ncol(DPA.Predictors.Numeric)) {
Outliers <- boxplot.stats(DPA.Predictors.Numeric[,i])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA.Predictors.Numeric[,i] %in% c(Outliers))
boxplot(DPA.Predictors.Numeric[,i],
ylab = names(DPA.Predictors.Numeric)[i],
main = names(DPA.Predictors.Numeric)[i],
horizontal=TRUE)
mtext(paste0(OutlierCount, " Outlier(s) Detected"))
}
OutlierCountSummary <- as.data.frame(cbind(names(DPA.Predictors.Numeric),(OutlierCountList)))
names(OutlierCountSummary) <- c("NumericPredictors","OutlierCount")
OutlierCountSummary$OutlierCount <- as.numeric(as.character(OutlierCountSummary$OutlierCount))
NumericPredictorWithOutlierCount <- nrow(OutlierCountSummary[OutlierCountSummary$OutlierCount>0,])
print(paste0(NumericPredictorWithOutlierCount, " numeric variable(s) were noted with outlier(s)." ))## [1] "12 numeric variable(s) were noted with outlier(s)."##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA.Predictors.Numeric))| Name | DPA.Predictors.Numeric |
| Number of rows | 2231 |
| Number of columns | 13 |
| _______________________ | |
| Column type frequency: | |
| numeric | 13 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| age | 0 | 1 | 54.22 | 11.03 | 18.00 | 48.00 | 55.00 | 62.00 | 82.00 | ▁▃▇▇▂ |
| resting.systolic.bp | 0 | 1 | 110.51 | 17.93 | 80.00 | 98.00 | 110.00 | 122.00 | 184.00 | ▇▇▅▁▁ |
| resting.hr | 0 | 1 | 76.18 | 14.39 | 39.00 | 66.00 | 75.00 | 86.00 | 124.00 | ▂▇▇▃▁ |
| bmi | 0 | 1 | 28.50 | 5.69 | 14.18 | 24.52 | 27.78 | 31.89 | 59.62 | ▂▇▃▁▁ |
| lvef.metabl | 0 | 1 | 20.26 | 7.34 | 5.00 | 15.00 | 20.00 | 25.00 | 40.00 | ▂▅▇▂▁ |
| peak.rer | 0 | 1 | 1.08 | 0.12 | 0.08 | 1.02 | 1.10 | 1.14 | 1.80 | ▁▁▇▆▁ |
| peak.vo2 | 0 | 1 | 16.27 | 5.03 | 4.20 | 12.80 | 15.70 | 19.30 | 43.80 | ▃▇▂▁▁ |
| interval | 0 | 1 | 503.26 | 220.86 | 21.00 | 345.00 | 480.00 | 641.00 | 1415.00 | ▃▇▅▁▁ |
| bun | 0 | 1 | 25.28 | 12.57 | 4.32 | 17.00 | 23.00 | 29.33 | 129.00 | ▇▂▁▁▁ |
| sodium | 0 | 1 | 139.45 | 3.09 | 120.00 | 138.00 | 139.80 | 141.00 | 152.00 | ▁▁▆▇▁ |
| hgb | 0 | 1 | 13.54 | 1.41 | 7.20 | 12.80 | 13.58 | 14.40 | 19.20 | ▁▂▇▃▁ |
| glucose | 0 | 1 | 109.35 | 42.56 | 37.00 | 87.00 | 96.93 | 114.88 | 424.00 | ▇▂▁▁▁ |
| crcl | 0 | 1 | 90.71 | 43.05 | 5.76 | 60.93 | 83.13 | 110.33 | 385.84 | ▇▇▁▁▁ |
###################################
# Verifying the data dimensions
###################################
dim(DPA.Predictors.Numeric)## [1] 2231 131.3.2 Zero and Near-Zero Variance
Zero and near-zero variance data assessment:
[A] Low variance noted for 10 variables from the previous data quality assessment using a lower threshold.
[B] Low variance noted for 2 variables using a preprocessing summary from the caret package. The nearZeroVar method using both the freqCut and uniqueCut criteria set at 95/5 and 10, respectively, were applied on the dataset.
[B.1] dilver variable (factor)
[B.2] nifed variable (factor)
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- peakVO2
##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA))| Name | DPA |
| Number of rows | 2231 |
| Number of columns | 41 |
| _______________________ | |
| Column type frequency: | |
| factor | 27 |
| numeric | 14 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| betablok | 0 | 1 | FALSE | 2 | 1: 1429, 0: 802 |
| dilver | 0 | 1 | FALSE | 2 | 0: 2215, 1: 16 |
| nifed | 0 | 1 | FALSE | 2 | 0: 2132, 1: 99 |
| acei | 0 | 1 | FALSE | 2 | 1: 1711, 0: 520 |
| angioten.II | 0 | 1 | FALSE | 2 | 0: 1941, 1: 290 |
| anti.arrhy | 0 | 1 | FALSE | 2 | 0: 1722, 1: 509 |
| anti.coag | 0 | 1 | FALSE | 2 | 0: 1332, 1: 899 |
| aspirin | 0 | 1 | FALSE | 2 | 0: 1193, 1: 1038 |
| digoxin | 0 | 1 | FALSE | 2 | 1: 1570, 0: 661 |
| nitrates | 0 | 1 | FALSE | 2 | 0: 1492, 1: 739 |
| vasodilator | 0 | 1 | FALSE | 2 | 0: 2095, 1: 136 |
| diuretic.loop | 0 | 1 | FALSE | 2 | 1: 1880, 0: 351 |
| diuretic.thiazide | 0 | 1 | FALSE | 2 | 0: 1952, 1: 279 |
| diuretic.potassium.spar | 0 | 1 | FALSE | 2 | 0: 1582, 1: 649 |
| lipidrx.statin | 0 | 1 | FALSE | 2 | 0: 1381, 1: 850 |
| insulin | 0 | 1 | FALSE | 2 | 0: 2016, 1: 215 |
| surgery.pacemaker | 0 | 1 | FALSE | 2 | 0: 1729, 1: 502 |
| surgery.cabg | 0 | 1 | FALSE | 2 | 0: 1637, 1: 594 |
| surgery.pci | 0 | 1 | FALSE | 2 | 0: 1755, 1: 476 |
| surgery.aicd.implant | 0 | 1 | FALSE | 2 | 0: 1584, 1: 647 |
| smknow | 0 | 1 | FALSE | 2 | 0: 1772, 1: 459 |
| q.wave.mi | 0 | 1 | FALSE | 2 | 0: 1952, 1: 279 |
| niddm | 0 | 1 | FALSE | 2 | 0: 1881, 1: 350 |
| cad | 0 | 1 | FALSE | 2 | 0: 1325, 1: 906 |
| died | 0 | 1 | FALSE | 2 | 0: 1505, 1: 726 |
| male | 0 | 1 | FALSE | 2 | 1: 1629, 0: 602 |
| black | 0 | 1 | FALSE | 2 | 0: 1965, 1: 266 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| age | 0 | 1 | 54.22 | 11.03 | 18.00 | 48.00 | 55.00 | 62.00 | 82.00 | ▁▃▇▇▂ |
| resting.systolic.bp | 0 | 1 | 110.51 | 17.93 | 80.00 | 98.00 | 110.00 | 122.00 | 184.00 | ▇▇▅▁▁ |
| resting.hr | 0 | 1 | 76.18 | 14.39 | 39.00 | 66.00 | 75.00 | 86.00 | 124.00 | ▂▇▇▃▁ |
| bmi | 0 | 1 | 28.50 | 5.69 | 14.18 | 24.52 | 27.78 | 31.89 | 59.62 | ▂▇▃▁▁ |
| lvef.metabl | 0 | 1 | 20.26 | 7.34 | 5.00 | 15.00 | 20.00 | 25.00 | 40.00 | ▂▅▇▂▁ |
| peak.rer | 0 | 1 | 1.08 | 0.12 | 0.08 | 1.02 | 1.10 | 1.14 | 1.80 | ▁▁▇▆▁ |
| peak.vo2 | 0 | 1 | 16.27 | 5.03 | 4.20 | 12.80 | 15.70 | 19.30 | 43.80 | ▃▇▂▁▁ |
| interval | 0 | 1 | 503.26 | 220.86 | 21.00 | 345.00 | 480.00 | 641.00 | 1415.00 | ▃▇▅▁▁ |
| ttodead | 0 | 1 | 62.75 | 36.77 | 1.00 | 30.00 | 62.00 | 95.00 | 127.00 | ▇▇▇▇▇ |
| bun | 0 | 1 | 25.28 | 12.57 | 4.32 | 17.00 | 23.00 | 29.33 | 129.00 | ▇▂▁▁▁ |
| sodium | 0 | 1 | 139.45 | 3.09 | 120.00 | 138.00 | 139.80 | 141.00 | 152.00 | ▁▁▆▇▁ |
| hgb | 0 | 1 | 13.54 | 1.41 | 7.20 | 12.80 | 13.58 | 14.40 | 19.20 | ▁▂▇▃▁ |
| glucose | 0 | 1 | 109.35 | 42.56 | 37.00 | 87.00 | 96.93 | 114.88 | 424.00 | ▇▂▁▁▁ |
| crcl | 0 | 1 | 90.71 | 43.05 | 5.76 | 60.93 | 83.13 | 110.33 | 385.84 | ▇▇▁▁▁ |
##################################
# Identifying columns with low variance
###################################
DPA_LowVariance <- nearZeroVar(DPA,
freqCut = 95/5,
uniqueCut = 10,
saveMetrics= TRUE)
(DPA_LowVariance[DPA_LowVariance$nzv,])## freqRatio percentUnique zeroVar nzv
## dilver 138.43750 0.0896459 FALSE TRUE
## nifed 21.53535 0.0896459 FALSE TRUEif ((nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))==0){
print("No low variance predictors noted.")
} else {
print(paste0("Low variance observed for ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."))
DPA_LowVarianceForRemoval <- (nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))
print(paste0("Low variance can be resolved by removing ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s)."))
for (j in 1:DPA_LowVarianceForRemoval) {
DPA_LowVarianceRemovedVariable <- rownames(DPA_LowVariance[DPA_LowVariance$nzv,])[j]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LowVarianceRemovedVariable))
}
DPA %>%
skim() %>%
dplyr::filter(skim_variable %in% rownames(DPA_LowVariance[DPA_LowVariance$nzv,]))
##################################
# Filtering out columns with low variance
#################################
DPA_ExcludedLowVariance <- DPA[,!names(DPA) %in% rownames(DPA_LowVariance[DPA_LowVariance$nzv,])]
##################################
# Gathering descriptive statistics
##################################
(DPA_ExcludedLowVariance_Skimmed <- skim(DPA_ExcludedLowVariance))
}## [1] "Low variance observed for 2 numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."
## [1] "Low variance can be resolved by removing 2 numeric variable(s)."
## [1] "Variable 1 for removal: dilver"
## [1] "Variable 2 for removal: nifed"| Name | DPA_ExcludedLowVariance |
| Number of rows | 2231 |
| Number of columns | 39 |
| _______________________ | |
| Column type frequency: | |
| factor | 25 |
| numeric | 14 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| betablok | 0 | 1 | FALSE | 2 | 1: 1429, 0: 802 |
| acei | 0 | 1 | FALSE | 2 | 1: 1711, 0: 520 |
| angioten.II | 0 | 1 | FALSE | 2 | 0: 1941, 1: 290 |
| anti.arrhy | 0 | 1 | FALSE | 2 | 0: 1722, 1: 509 |
| anti.coag | 0 | 1 | FALSE | 2 | 0: 1332, 1: 899 |
| aspirin | 0 | 1 | FALSE | 2 | 0: 1193, 1: 1038 |
| digoxin | 0 | 1 | FALSE | 2 | 1: 1570, 0: 661 |
| nitrates | 0 | 1 | FALSE | 2 | 0: 1492, 1: 739 |
| vasodilator | 0 | 1 | FALSE | 2 | 0: 2095, 1: 136 |
| diuretic.loop | 0 | 1 | FALSE | 2 | 1: 1880, 0: 351 |
| diuretic.thiazide | 0 | 1 | FALSE | 2 | 0: 1952, 1: 279 |
| diuretic.potassium.spar | 0 | 1 | FALSE | 2 | 0: 1582, 1: 649 |
| lipidrx.statin | 0 | 1 | FALSE | 2 | 0: 1381, 1: 850 |
| insulin | 0 | 1 | FALSE | 2 | 0: 2016, 1: 215 |
| surgery.pacemaker | 0 | 1 | FALSE | 2 | 0: 1729, 1: 502 |
| surgery.cabg | 0 | 1 | FALSE | 2 | 0: 1637, 1: 594 |
| surgery.pci | 0 | 1 | FALSE | 2 | 0: 1755, 1: 476 |
| surgery.aicd.implant | 0 | 1 | FALSE | 2 | 0: 1584, 1: 647 |
| smknow | 0 | 1 | FALSE | 2 | 0: 1772, 1: 459 |
| q.wave.mi | 0 | 1 | FALSE | 2 | 0: 1952, 1: 279 |
| niddm | 0 | 1 | FALSE | 2 | 0: 1881, 1: 350 |
| cad | 0 | 1 | FALSE | 2 | 0: 1325, 1: 906 |
| died | 0 | 1 | FALSE | 2 | 0: 1505, 1: 726 |
| male | 0 | 1 | FALSE | 2 | 1: 1629, 0: 602 |
| black | 0 | 1 | FALSE | 2 | 0: 1965, 1: 266 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| age | 0 | 1 | 54.22 | 11.03 | 18.00 | 48.00 | 55.00 | 62.00 | 82.00 | ▁▃▇▇▂ |
| resting.systolic.bp | 0 | 1 | 110.51 | 17.93 | 80.00 | 98.00 | 110.00 | 122.00 | 184.00 | ▇▇▅▁▁ |
| resting.hr | 0 | 1 | 76.18 | 14.39 | 39.00 | 66.00 | 75.00 | 86.00 | 124.00 | ▂▇▇▃▁ |
| bmi | 0 | 1 | 28.50 | 5.69 | 14.18 | 24.52 | 27.78 | 31.89 | 59.62 | ▂▇▃▁▁ |
| lvef.metabl | 0 | 1 | 20.26 | 7.34 | 5.00 | 15.00 | 20.00 | 25.00 | 40.00 | ▂▅▇▂▁ |
| peak.rer | 0 | 1 | 1.08 | 0.12 | 0.08 | 1.02 | 1.10 | 1.14 | 1.80 | ▁▁▇▆▁ |
| peak.vo2 | 0 | 1 | 16.27 | 5.03 | 4.20 | 12.80 | 15.70 | 19.30 | 43.80 | ▃▇▂▁▁ |
| interval | 0 | 1 | 503.26 | 220.86 | 21.00 | 345.00 | 480.00 | 641.00 | 1415.00 | ▃▇▅▁▁ |
| ttodead | 0 | 1 | 62.75 | 36.77 | 1.00 | 30.00 | 62.00 | 95.00 | 127.00 | ▇▇▇▇▇ |
| bun | 0 | 1 | 25.28 | 12.57 | 4.32 | 17.00 | 23.00 | 29.33 | 129.00 | ▇▂▁▁▁ |
| sodium | 0 | 1 | 139.45 | 3.09 | 120.00 | 138.00 | 139.80 | 141.00 | 152.00 | ▁▁▆▇▁ |
| hgb | 0 | 1 | 13.54 | 1.41 | 7.20 | 12.80 | 13.58 | 14.40 | 19.20 | ▁▂▇▃▁ |
| glucose | 0 | 1 | 109.35 | 42.56 | 37.00 | 87.00 | 96.93 | 114.88 | 424.00 | ▇▂▁▁▁ |
| crcl | 0 | 1 | 90.71 | 43.05 | 5.76 | 60.93 | 83.13 | 110.33 | 385.84 | ▇▇▁▁▁ |
1.3.3 Collinearity
High collinearity data assessment:
[A] No high correlation > 95% noted for any variable pairs as confirmed using the preprocessing summaries from the caret and lares packages.
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- peakVO2
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("died","ttodead")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
##################################
# Visualizing pairwise correlation between predictors
##################################
DPA_CorrelationTest <- cor.mtest(DPA.Predictors.Numeric,
method = "pearson",
conf.level = .95)
corrplot(cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"),
method = "circle",
type = "upper",
order = "original",
tl.col = "black",
tl.cex = 0.75,
tl.srt = 90,
sig.level = 0.05,
p.mat = DPA_CorrelationTest$p,
insig = "blank")
##################################
# Identifying the highly correlated variables
##################################
DPA_Correlation <- cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs")
(DPA_HighlyCorrelatedCount <- sum(abs(DPA_Correlation[upper.tri(DPA_Correlation)]) > 0.95))## [1] 0if (DPA_HighlyCorrelatedCount == 0) {
print("No highly correlated predictors noted.")
} else {
print(paste0("High correlation observed for ",
(DPA_HighlyCorrelatedCount),
" pairs of numeric variable(s) with Correlation.Coefficient>0.95."))
(DPA_HighlyCorrelatedPairs <- corr_cross(DPA.Predictors.Numeric,
max_pvalue = 0.05,
top = DPA_HighlyCorrelatedCount,
rm.na = TRUE,
grid = FALSE
))
}## [1] "No highly correlated predictors noted."if (DPA_HighlyCorrelatedCount > 0) {
DPA_HighlyCorrelated <- findCorrelation(DPA_Correlation, cutoff = 0.95)
(DPA_HighlyCorrelatedForRemoval <- length(DPA_HighlyCorrelated))
print(paste0("High correlation can be resolved by removing ",
(DPA_HighlyCorrelatedForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_HighlyCorrelatedForRemoval) {
DPA_HighlyCorrelatedRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_HighlyCorrelated[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_HighlyCorrelatedRemovedVariable))
}
##################################
# Filtering out columns with high correlation
#################################
DPA_ExcludedHighCorrelation <- DPA[,-DPA_HighlyCorrelated]
##################################
# Gathering descriptive statistics
##################################
(DPA_ExcludedHighCorrelation_Skimmed <- skim(DPA_ExcludedHighCorrelation))
}1.3.4 Linear Dependencies
Linear dependency data assessment:
[A] No linear dependencies noted for any subset of variables using the preprocessing summary from the caret package applying the findLinearCombos method which utilizes the QR decomposition of a matrix to enumerate sets of linear combinations (if they exist).
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- peakVO2
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("died","ttodead")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
##################################
# Identifying the linearly dependent variables
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
(DPA_LinearlyDependentCount <- length(DPA_LinearlyDependent$linearCombos))## [1] 0if (DPA_LinearlyDependentCount == 0) {
print("No linearly dependent predictors noted.")
} else {
print(paste0("Linear dependency observed for ",
(DPA_LinearlyDependentCount),
" subset(s) of numeric variable(s)."))
for (i in 1:DPA_LinearlyDependentCount) {
DPA_LinearlyDependentSubset <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$linearCombos[[i]]]
print(paste0("Linear dependent variable(s) for subset ",
i,
" include: ",
DPA_LinearlyDependentSubset))
}
}## [1] "No linearly dependent predictors noted."##################################
# Identifying the linearly dependent variables for removal
##################################
if (DPA_LinearlyDependentCount > 0) {
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
DPA_LinearlyDependentForRemoval <- length(DPA_LinearlyDependent$remove)
print(paste0("Linear dependency can be resolved by removing ",
(DPA_LinearlyDependentForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_LinearlyDependentForRemoval) {
DPA_LinearlyDependentRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$remove[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LinearlyDependentRemovedVariable))
}
##################################
# Filtering out columns with linear dependency
#################################
DPA_ExcludedLinearlyDependent <- DPA[,-DPA_LinearlyDependent$remove]
##################################
# Gathering descriptive statistics
##################################
(DPA_ExcludedLinearlyDependent_Skimmed <- skim(DPA_ExcludedLinearlyDependent))
}1.3.5 Univariate Filter
[A] 25 variables were evaluated as statistically significant predictors from the univariate Cox Proportional Hazards modelling analysis with p-value=<0.05.
[A.1] age variable (numeric)
[A.2] betablok variable (factor)
[A.3] acei variable (factor)
[A.4] angioten.II variable (factor)
[A.5] anti.arrhy variable (factor)
[A.6] anti.coag variable (factor)
[A.7] aspirin variable (factor)
[A.8] vasodilator variable (factor)
[A.9] diuretic.loop variable (factor)
[A.10] diuretic.thiazide variable (factor)
[A.11] insulin variable (factor)
[A.12] surgery.pacemaker variable (factor)
[A.13] surgery.cabg variable (factor)
[A.14] resting.systolic.bp variable (numeric)
[A.15] resting.hr variable (numeric)
[A.16] lvef.metabl variable (numeric)
[A.17] peak.vo2 variable (numeric)
[A.18] interval variable (numeric)
[A.19] cad variable (factor)
[A.20] bun variable (numeric)
[A.21] sodium variable (numeric)
[A.22] hgb variable (numeric)
[A.23] glucose variable (numeric)
[A.24] male variable (factor)
[A.25] crcl variable (numeric)
[B] 14 variables were evaluated as not statistically significant predictors from the univariate Cox Proportional Hazards modelling analysis with p-value>0.05.
[B.1] dilver variable (factor)
[B.2] nifed variable (factor)
[B.3] digoxin variable (factor)
[B.4] nitrates variable (factor)
[B.5] diuretic.potassium.spar variable (factor)
[B.6] lipidrx.statin variable (factor)
[B.7] surgery.pci variable (factor)
[B.8] surgery.aicd.implant variable (factor)
[B.9] smknow variable (factor)
[B.10] q.wave.mi variable (factor)
[B.11] bmi variable (numeric)
[B.12] niddm variable (factor)
[B.13] peak.rer variable (numeric)
[B.14] black variable (factor)
Code Chunk | Output
##################################
# Formulating the univariate Cox PH models
# to assess the predictive power
# of the individual predictors
##################################
##################################
# Loading dataset
##################################
UF <- peakVO2.Source
##################################
# Formulating the response
# and predictor variables
##################################
UF_Response <- Surv(time=UF$ttodead, event=UF$died)
UF_Predictors <- names(UF)[!names(UF) %in% c("ttodead","died")]
##################################
# Formulating the response
# and predictor variables
##################################
(UF_CoxPHFormula <- sapply(UF_Predictors,
function(x) as.formula(paste('Surv(ttodead,died)~', x))))## $age
## Surv(ttodead, died) ~ age
## <environment: 0x0000000030e2fa00>
##
## $betablok
## Surv(ttodead, died) ~ betablok
## <environment: 0x0000000030e35300>
##
## $dilver
## Surv(ttodead, died) ~ dilver
## <environment: 0x000000003118fb58>
##
## $nifed
## Surv(ttodead, died) ~ nifed
## <environment: 0x00000000311920d0>
##
## $acei
## Surv(ttodead, died) ~ acei
## <environment: 0x0000000031194648>
##
## $angioten.II
## Surv(ttodead, died) ~ angioten.II
## <environment: 0x0000000031192d90>
##
## $anti.arrhy
## Surv(ttodead, died) ~ anti.arrhy
## <environment: 0x0000000031195308>
##
## $anti.coag
## Surv(ttodead, died) ~ anti.coag
## <environment: 0x0000000031197880>
##
## $aspirin
## Surv(ttodead, died) ~ aspirin
## <environment: 0x0000000031199df8>
##
## $digoxin
## Surv(ttodead, died) ~ digoxin
## <environment: 0x000000003119c370>
##
## $nitrates
## Surv(ttodead, died) ~ nitrates
## <environment: 0x000000003119aab8>
##
## $vasodilator
## Surv(ttodead, died) ~ vasodilator
## <environment: 0x000000003119d030>
##
## $diuretic.loop
## Surv(ttodead, died) ~ diuretic.loop
## <environment: 0x000000003119f5a8>
##
## $diuretic.thiazide
## Surv(ttodead, died) ~ diuretic.thiazide
## <environment: 0x00000000311a3a40>
##
## $diuretic.potassium.spar
## Surv(ttodead, died) ~ diuretic.potassium.spar
## <environment: 0x00000000311a5fb8>
##
## $lipidrx.statin
## Surv(ttodead, died) ~ lipidrx.statin
## <environment: 0x00000000311a4700>
##
## $insulin
## Surv(ttodead, died) ~ insulin
## <environment: 0x00000000311a6c78>
##
## $surgery.pacemaker
## Surv(ttodead, died) ~ surgery.pacemaker
## <environment: 0x00000000311a91f0>
##
## $surgery.cabg
## Surv(ttodead, died) ~ surgery.cabg
## <environment: 0x00000000311ad688>
##
## $surgery.pci
## Surv(ttodead, died) ~ surgery.pci
## <environment: 0x00000000311afc00>
##
## $surgery.aicd.implant
## Surv(ttodead, died) ~ surgery.aicd.implant
## <environment: 0x00000000311ae348>
##
## $resting.systolic.bp
## Surv(ttodead, died) ~ resting.systolic.bp
## <environment: 0x00000000311b08c0>
##
## $resting.hr
## Surv(ttodead, died) ~ resting.hr
## <environment: 0x00000000311b2e38>
##
## $smknow
## Surv(ttodead, died) ~ smknow
## <environment: 0x00000000311b53b0>
##
## $q.wave.mi
## Surv(ttodead, died) ~ q.wave.mi
## <environment: 0x00000000311b3af8>
##
## $bmi
## Surv(ttodead, died) ~ bmi
## <environment: 0x00000000311b6070>
##
## $niddm
## Surv(ttodead, died) ~ niddm
## <environment: 0x00000000311b85e8>
##
## $lvef.metabl
## Surv(ttodead, died) ~ lvef.metabl
## <environment: 0x0000000030d18e20>
##
## $peak.rer
## Surv(ttodead, died) ~ peak.rer
## <environment: 0x0000000030d1b398>
##
## $peak.vo2
## Surv(ttodead, died) ~ peak.vo2
## <environment: 0x0000000030d19ae0>
##
## $interval
## Surv(ttodead, died) ~ interval
## <environment: 0x0000000030d1c058>
##
## $cad
## Surv(ttodead, died) ~ cad
## <environment: 0x0000000030d1e5d0>
##
## $bun
## Surv(ttodead, died) ~ bun
## <environment: 0x0000000030d20b48>
##
## $sodium
## Surv(ttodead, died) ~ sodium
## <environment: 0x0000000030d230c0>
##
## $hgb
## Surv(ttodead, died) ~ hgb
## <environment: 0x0000000030d21808>
##
## $glucose
## Surv(ttodead, died) ~ glucose
## <environment: 0x0000000030d23d80>
##
## $male
## Surv(ttodead, died) ~ male
## <environment: 0x0000000030d262f8>
##
## $black
## Surv(ttodead, died) ~ black
## <environment: 0x0000000030d28870>
##
## $crcl
## Surv(ttodead, died) ~ crcl
## <environment: 0x0000000030d2ade8>(UF_CoxPHModel <- lapply(UF_CoxPHFormula, function(x){coxph(x, data=UF)}))## $age
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## age 0.025359 1.025684 0.003585 7.074 1.51e-12
##
## Likelihood ratio test=51.92 on 1 df, p=5.784e-13
## n= 2231, number of events= 726
##
## $betablok
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## betablok -0.36995 0.69077 0.07482 -4.945 7.62e-07
##
## Likelihood ratio test=24.2 on 1 df, p=8.693e-07
## n= 2231, number of events= 726
##
## $dilver
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## dilver -0.2607 0.7705 0.4488 -0.581 0.561
##
## Likelihood ratio test=0.37 on 1 df, p=0.544
## n= 2231, number of events= 726
##
## $nifed
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## nifed 0.1629 1.1770 0.1710 0.953 0.341
##
## Likelihood ratio test=0.87 on 1 df, p=0.3523
## n= 2231, number of events= 726
##
## $acei
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## acei -0.20395 0.81550 0.08637 -2.361 0.0182
##
## Likelihood ratio test=5.38 on 1 df, p=0.02035
## n= 2231, number of events= 726
##
## $angioten.II
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## angioten.II 0.2698 1.3097 0.1071 2.519 0.0118
##
## Likelihood ratio test=5.95 on 1 df, p=0.01472
## n= 2231, number of events= 726
##
## $anti.arrhy
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## anti.arrhy 0.29948 1.34916 0.08375 3.576 0.000349
##
## Likelihood ratio test=12.18 on 1 df, p=0.0004822
## n= 2231, number of events= 726
##
## $anti.coag
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## anti.coag 0.27124 1.31159 0.07445 3.643 0.000269
##
## Likelihood ratio test=13.14 on 1 df, p=0.000289
## n= 2231, number of events= 726
##
## $aspirin
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## aspirin -0.15459 0.85677 0.07567 -2.043 0.0411
##
## Likelihood ratio test=4.21 on 1 df, p=0.04021
## n= 2231, number of events= 726
##
## $digoxin
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## digoxin 0.35952 1.43264 0.09184 3.915 9.05e-05
##
## Likelihood ratio test=16.39 on 1 df, p=5.156e-05
## n= 2231, number of events= 726
##
## $nitrates
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## nitrates 0.13660 1.14637 0.07728 1.768 0.0771
##
## Likelihood ratio test=3.08 on 1 df, p=0.07912
## n= 2231, number of events= 726
##
## $vasodilator
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## vasodilator 0.3126 1.3670 0.1440 2.171 0.0299
##
## Likelihood ratio test=4.32 on 1 df, p=0.03775
## n= 2231, number of events= 726
##
## $diuretic.loop
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## diuretic.loop 0.4546 1.5755 0.1173 3.877 0.000106
##
## Likelihood ratio test=16.89 on 1 df, p=3.963e-05
## n= 2231, number of events= 726
##
## $diuretic.thiazide
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## diuretic.thiazide 0.50622 1.65900 0.09834 5.148 2.64e-07
##
## Likelihood ratio test=23.67 on 1 df, p=1.141e-06
## n= 2231, number of events= 726
##
## $diuretic.potassium.spar
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## diuretic.potassium.spar -0.02909 0.97133 0.08550 -0.34 0.734
##
## Likelihood ratio test=0.12 on 1 df, p=0.7331
## n= 2231, number of events= 726
##
## $lipidrx.statin
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## lipidrx.statin 0.004143 1.004152 0.077788 0.053 0.958
##
## Likelihood ratio test=0 on 1 df, p=0.9575
## n= 2231, number of events= 726
##
## $insulin
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## insulin 0.5506 1.7342 0.1103 4.993 5.93e-07
##
## Likelihood ratio test=21.79 on 1 df, p=3.042e-06
## n= 2231, number of events= 726
##
## $surgery.pacemaker
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## surgery.pacemaker 0.19372 1.21376 0.08859 2.187 0.0288
##
## Likelihood ratio test=4.62 on 1 df, p=0.03168
## n= 2231, number of events= 726
##
## $surgery.cabg
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## surgery.cabg 0.43581 1.54622 0.07933 5.494 3.93e-08
##
## Likelihood ratio test=28.53 on 1 df, p=9.233e-08
## n= 2231, number of events= 726
##
## $surgery.pci
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## surgery.pci 0.09777 1.10271 0.09066 1.078 0.281
##
## Likelihood ratio test=1.14 on 1 df, p=0.2853
## n= 2231, number of events= 726
##
## $surgery.aicd.implant
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## surgery.aicd.implant 0.15077 1.16273 0.08497 1.774 0.076
##
## Likelihood ratio test=3.07 on 1 df, p=0.07955
## n= 2231, number of events= 726
##
## $resting.systolic.bp
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## resting.systolic.bp -0.007964 0.992068 0.002152 -3.701 0.000215
##
## Likelihood ratio test=14.13 on 1 df, p=0.000171
## n= 2231, number of events= 726
##
## $resting.hr
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## resting.hr 0.011461 1.011527 0.002463 4.653 3.27e-06
##
## Likelihood ratio test=21.27 on 1 df, p=3.98e-06
## n= 2231, number of events= 726
##
## $smknow
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## smknow -0.14929 0.86132 0.09513 -1.569 0.117
##
## Likelihood ratio test=2.54 on 1 df, p=0.111
## n= 2231, number of events= 726
##
## $q.wave.mi
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## q.wave.mi 0.1547 1.1673 0.1073 1.443 0.149
##
## Likelihood ratio test=2 on 1 df, p=0.1568
## n= 2231, number of events= 726
##
## $bmi
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## bmi -0.009681 0.990366 0.006712 -1.442 0.149
##
## Likelihood ratio test=2.11 on 1 df, p=0.1464
## n= 2231, number of events= 726
##
## $niddm
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## niddm 0.14548 1.15659 0.09833 1.479 0.139
##
## Likelihood ratio test=2.12 on 1 df, p=0.1454
## n= 2231, number of events= 726
##
## $lvef.metabl
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## lvef.metabl -0.028179 0.972214 0.005436 -5.183 2.18e-07
##
## Likelihood ratio test=28.17 on 1 df, p=1.111e-07
## n= 2231, number of events= 726
##
## $peak.rer
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## peak.rer 0.03624 1.03690 0.32939 0.11 0.912
##
## Likelihood ratio test=0.01 on 1 df, p=0.9123
## n= 2231, number of events= 726
##
## $peak.vo2
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## peak.vo2 -0.129074 0.878909 0.009008 -14.33 <2e-16
##
## Likelihood ratio test=230 on 1 df, p=< 2.2e-16
## n= 2231, number of events= 726
##
## $interval
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## interval -0.0026699 0.9973337 0.0001923 -13.89 <2e-16
##
## Likelihood ratio test=211 on 1 df, p=< 2.2e-16
## n= 2231, number of events= 726
##
## $cad
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## cad 0.40104 1.49338 0.07433 5.396 6.83e-08
##
## Likelihood ratio test=28.76 on 1 df, p=8.182e-08
## n= 2231, number of events= 726
##
## $bun
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## bun 0.028950 1.029373 0.001937 14.95 <2e-16
##
## Likelihood ratio test=150.5 on 1 df, p=< 2.2e-16
## n= 2231, number of events= 726
##
## $sodium
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## sodium -0.07327 0.92935 0.01146 -6.393 1.63e-10
##
## Likelihood ratio test=36.97 on 1 df, p=1.202e-09
## n= 2231, number of events= 726
##
## $hgb
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## hgb -0.17772 0.83718 0.02643 -6.725 1.76e-11
##
## Likelihood ratio test=42.91 on 1 df, p=5.727e-11
## n= 2231, number of events= 726
##
## $glucose
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## glucose 0.0024485 1.0024515 0.0007766 3.153 0.00162
##
## Likelihood ratio test=8.97 on 1 df, p=0.002749
## n= 2231, number of events= 726
##
## $male
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## male 0.44320 1.55768 0.09192 4.822 1.42e-06
##
## Likelihood ratio test=25.26 on 1 df, p=5.009e-07
## n= 2231, number of events= 726
##
## $black
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## black 0.06476 1.06690 0.11377 0.569 0.569
##
## Likelihood ratio test=0.32 on 1 df, p=0.5724
## n= 2231, number of events= 726
##
## $crcl
## Call:
## coxph(formula = x, data = UF)
##
## coef exp(coef) se(coef) z p
## crcl -0.008557 0.991480 0.001080 -7.923 2.32e-15
##
## Likelihood ratio test=72.01 on 1 df, p=< 2.2e-16
## n= 2231, number of events= 726UF_CoxPHModelResults <- lapply(UF_CoxPHModel,
function(x){
x <- summary(x)
p.value<-round(x$wald["pvalue"], digits=5)
wald.test<-round(x$wald["test"], digits=5)
######################################
# Extracting the concordance values
######################################
concordance<-round(x$concordance[[1]],digits=5)
######################################
# Extracting the beta coefficients
######################################
beta<-round(x$coef[1], digits=5)
z<-round(x$coefficients[[4]], digits=5)
######################################
# Extracting the exponentiated coefficients
######################################
HR <-round(x$coef[2], digits=5)
HR.confint.lower <- round(x$conf.int[,"lower .95"],5)
HR.confint.upper <- round(x$conf.int[,"upper .95"],5)
HR <- paste0(HR, " (",
HR.confint.lower, "-", HR.confint.upper, ")")
######################################
# Consolidating the model results
######################################
res<-c(concordance,beta, HR, z, p.value, wald.test)
names(res)<-c("Concordance",
"Beta",
"HR (95% CI for HR)",
"z",
"P-Value",
"Wald Test")
return(res)
})
UF_CoxPHModelResults <- t(as.data.frame(UF_CoxPHModelResults, check.names = FALSE))
######################################
# Summarizing the results
# for all variables
######################################
UF_CoxPHModelSummary <- as.data.frame(UF_CoxPHModelResults)
######################################
# Gathering only
# the significant variables
# from the univariate Cox PH analysis
######################################
(UF_CoxPHModelSummary_Sig <- UF_CoxPHModelSummary[UF_CoxPHModelSummary$'P-Value'==0.05 |
UF_CoxPHModelSummary$'P-Value'<0.05,])## Concordance Beta HR (95% CI for HR) z
## age 0.58441 0.02536 1.02568 (1.0185-1.03292) 7.0738
## betablok 0.55325 -0.36995 0.69077 (0.59656-0.79987) -4.94481
## acei 0.51752 -0.20395 0.8155 (0.6885-0.96594) -2.3612
## angioten.II 0.5116 0.26978 1.30968 (1.06167-1.61561) 2.5187
## anti.arrhy 0.53265 0.29948 1.34916 (1.14492-1.58983) 3.57595
## anti.coag 0.53348 0.27124 1.31159 (1.13351-1.51766) 3.6431
## aspirin 0.52474 -0.15459 0.85677 (0.73867-0.99374) -2.04294
## vasodilator 0.51 0.31262 1.36701 (1.03088-1.81273) 2.17118
## diuretic.loop 0.52758 0.45458 1.57551 (1.25201-1.98259) 3.87668
## diuretic.thiazide 0.5367 0.50622 1.659 (1.36816-2.01167) 5.14751
## insulin 0.52329 0.55057 1.73423 (1.39719-2.15258) 4.99346
## surgery.pacemaker 0.51926 0.19372 1.21376 (1.0203-1.4439) 2.18679
## surgery.cabg 0.55029 0.43581 1.54622 (1.32357-1.80632) 5.49381
## resting.systolic.bp 0.55211 -0.00796 0.99207 (0.98789-0.99626) -3.70069
## resting.hr 0.55122 0.01146 1.01153 (1.00666-1.01642) 4.65287
## lvef.metabl 0.5631 -0.02818 0.97221 (0.96191-0.98263) -5.18339
## peak.vo2 0.67529 -0.12907 0.87891 (0.86353-0.89456) -14.32822
## interval 0.66399 -0.00267 0.99733 (0.99696-0.99771) -13.88554
## cad 0.55102 0.40104 1.49338 (1.29093-1.72758) 5.39554
## bun 0.65486 0.02895 1.02937 (1.02547-1.03329) 14.94786
## sodium 0.57751 -0.07327 0.92935 (0.90871-0.95047) -6.39278
## hgb 0.5851 -0.17772 0.83718 (0.79492-0.88168) -6.72457
## glucose 0.52923 0.00245 1.00245 (1.00093-1.00398) 3.15297
## male 0.54481 0.4432 1.55768 (1.30089-1.86517) 4.82184
## crcl 0.60889 -0.00856 0.99148 (0.98938-0.99358) -7.92298
## P-Value Wald Test
## age 0 50.04
## betablok 0 24.45
## acei 0.01822 5.58
## angioten.II 0.01178 6.34
## anti.arrhy 0.00035 12.79
## anti.coag 0.00027 13.27
## aspirin 0.04106 4.17
## vasodilator 0.02992 4.71
## diuretic.loop 0.00011 15.03
## diuretic.thiazide 0 26.5
## insulin 0 24.93
## surgery.pacemaker 0.02876 4.78
## surgery.cabg 0 30.18
## resting.systolic.bp 0.00022 13.7
## resting.hr 0 21.65
## lvef.metabl 0 26.87
## peak.vo2 0 205.3
## interval 0 192.81
## cad 0 29.11
## bun 0 223.44
## sodium 0 40.87
## hgb 0 45.22
## glucose 0.00162 9.94
## male 0 23.25
## crcl 0 62.77nrow(UF_CoxPHModelSummary_Sig)## [1] 25rownames(UF_CoxPHModelSummary_Sig)## [1] "age" "betablok" "acei"
## [4] "angioten.II" "anti.arrhy" "anti.coag"
## [7] "aspirin" "vasodilator" "diuretic.loop"
## [10] "diuretic.thiazide" "insulin" "surgery.pacemaker"
## [13] "surgery.cabg" "resting.systolic.bp" "resting.hr"
## [16] "lvef.metabl" "peak.vo2" "interval"
## [19] "cad" "bun" "sodium"
## [22] "hgb" "glucose" "male"
## [25] "crcl"UF_Sig <- peakVO2[,rownames(UF_CoxPHModelSummary_Sig)]
(data.frame(
Column.Index=c(1:length(names(UF_Sig))),
Column.Name= names(UF_Sig),
Column.Type=sapply(UF_Sig, function(x) class(x)),
row.names=NULL)
)## Column.Index Column.Name Column.Type
## 1 1 age integer
## 2 2 betablok factor
## 3 3 acei factor
## 4 4 angioten.II factor
## 5 5 anti.arrhy factor
## 6 6 anti.coag factor
## 7 7 aspirin factor
## 8 8 vasodilator factor
## 9 9 diuretic.loop factor
## 10 10 diuretic.thiazide factor
## 11 11 insulin factor
## 12 12 surgery.pacemaker factor
## 13 13 surgery.cabg factor
## 14 14 resting.systolic.bp integer
## 15 15 resting.hr integer
## 16 16 lvef.metabl numeric
## 17 17 peak.vo2 numeric
## 18 18 interval integer
## 19 19 cad factor
## 20 20 bun numeric
## 21 21 sodium numeric
## 22 22 hgb numeric
## 23 23 glucose numeric
## 24 24 male factor
## 25 25 crcl numeric######################################
# Gathering only
# the non-significant variables
# from the univariate Cox PH analysis
######################################
(UF_CoxPHModelSummary_NonSig <- UF_CoxPHModelSummary[UF_CoxPHModelSummary$'P-Value'>0.05,])## Concordance Beta HR (95% CI for HR) z
## dilver 0.50131 -0.2607 0.77051 (0.3197-1.85702) -0.58086
## nifed 0.50223 0.16295 1.17697 (0.84183-1.64553) 0.95301
## digoxin 0.53316 0.35952 1.43264 (1.19665-1.71517) 3.91482
## nitrates 0.5138 0.1366 1.14637 (0.98524-1.33385) 1.76755
## diuretic.potassium.spar 0.50193 -0.02909 0.97133 (0.82146-1.14855) -0.34019
## lipidrx.statin 0.50006 0.00414 1.00415 (0.86216-1.16953) 0.05326
## surgery.pci 0.50773 0.09777 1.10271 (0.9232-1.31714) 1.07849
## surgery.aicd.implant 0.51548 0.15077 1.16273 (0.98435-1.37342) 1.77437
## smknow 0.519 -0.14929 0.86132 (0.71481-1.03786) -1.56933
## q.wave.mi 0.50942 0.15472 1.16733 (0.94602-1.44043) 1.44255
## bmi 0.51885 -0.00968 0.99037 (0.97742-1.00348) -1.44241
## niddm 0.50382 0.14548 1.15659 (0.95385-1.40244) 1.47944
## peak.rer 0.50881 0.03624 1.0369 (0.5437-1.97749) 0.11002
## black 0.49951 0.06476 1.0669 (0.85366-1.3334) 0.56919
## P-Value Wald Test
## dilver 0.56133 0.34
## nifed 0.34059 0.91
## digoxin 9e-05 15.33
## nitrates 0.07714 3.12
## diuretic.potassium.spar 0.73371 0.12
## lipidrx.statin 0.95752 0
## surgery.pci 0.28081 1.16
## surgery.aicd.implant 0.076 3.15
## smknow 0.11657 2.46
## q.wave.mi 0.14915 2.08
## bmi 0.14919 2.08
## niddm 0.13902 2.19
## peak.rer 0.9124 0.01
## black 0.56923 0.32nrow(UF_CoxPHModelSummary_NonSig)## [1] 14rownames(UF_CoxPHModelSummary_NonSig)## [1] "dilver" "nifed"
## [3] "digoxin" "nitrates"
## [5] "diuretic.potassium.spar" "lipidrx.statin"
## [7] "surgery.pci" "surgery.aicd.implant"
## [9] "smknow" "q.wave.mi"
## [11] "bmi" "niddm"
## [13] "peak.rer" "black"UF_NonSig <- peakVO2[,rownames(UF_CoxPHModelSummary_NonSig)]
(data.frame(
Column.Index=c(1:length(names(UF_NonSig))),
Column.Name= names(UF_NonSig),
Column.Type=sapply(UF_NonSig, function(x) class(x)),
row.names=NULL)
)## Column.Index Column.Name Column.Type
## 1 1 dilver factor
## 2 2 nifed factor
## 3 3 digoxin factor
## 4 4 nitrates factor
## 5 5 diuretic.potassium.spar factor
## 6 6 lipidrx.statin factor
## 7 7 surgery.pci factor
## 8 8 surgery.aicd.implant factor
## 9 9 smknow factor
## 10 10 q.wave.mi factor
## 11 11 bmi numeric
## 12 12 niddm factor
## 13 13 peak.rer numeric
## 14 14 black factor1.3.6 Pre-Processed Dataset
Preliminary dataset assessment:
[A] 2231 rows (observations)
[B] 27 columns (variables)
[B.1] 1/27 survival event status = died variable (factor)
[B.1.1] Event = died=1
[B.1.2] Censored or Non-Event = died=0
[B.2] 1/27 survival time = ttodead variable (numeric)
[B.3] 25/27 predictors = All remaining variables (14/25 factor + 11/25 numeric)
[C] Pre-processing actions applied:
[C.1] No centering, scaling and shape transformation applied to maintain interpretability
[C.2] No outlier treatment applied since the high values noted were contextually valid and sensible
[C.3] 2 predictors removed due to zero or near-zero variance
[C.4] No predictors removed due to high correlation
[C.5] No predictors removed due to linear dependencies
[C.6] 14 predictors evaluated as not statistically significant predictors from the univariate Cox Proportional Hazards modelling analysis removed
Code Chunk | Output
##################################
# Filtering out columns noted with data quality issues including
# zero and near-zero variance,
# to create the pre-modelling dataset
##################################
PMA_ExcludedNonSig <- peakVO2[,!names(peakVO2) %in% rownames(UF_CoxPHModelSummary_NonSig)]
PMA_PreModelling_Train <- PMA_ExcludedNonSig
##################################
# Gathering descriptive statistics
##################################
(PMA_PreModelling_Train_Skimmed <- skim(PMA_PreModelling_Train))| Name | PMA_PreModelling_Train |
| Number of rows | 2231 |
| Number of columns | 27 |
| _______________________ | |
| Column type frequency: | |
| factor | 15 |
| numeric | 12 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| betablok | 0 | 1 | FALSE | 2 | 1: 1429, 0: 802 |
| acei | 0 | 1 | FALSE | 2 | 1: 1711, 0: 520 |
| angioten.II | 0 | 1 | FALSE | 2 | 0: 1941, 1: 290 |
| anti.arrhy | 0 | 1 | FALSE | 2 | 0: 1722, 1: 509 |
| anti.coag | 0 | 1 | FALSE | 2 | 0: 1332, 1: 899 |
| aspirin | 0 | 1 | FALSE | 2 | 0: 1193, 1: 1038 |
| vasodilator | 0 | 1 | FALSE | 2 | 0: 2095, 1: 136 |
| diuretic.loop | 0 | 1 | FALSE | 2 | 1: 1880, 0: 351 |
| diuretic.thiazide | 0 | 1 | FALSE | 2 | 0: 1952, 1: 279 |
| insulin | 0 | 1 | FALSE | 2 | 0: 2016, 1: 215 |
| surgery.pacemaker | 0 | 1 | FALSE | 2 | 0: 1729, 1: 502 |
| surgery.cabg | 0 | 1 | FALSE | 2 | 0: 1637, 1: 594 |
| cad | 0 | 1 | FALSE | 2 | 0: 1325, 1: 906 |
| died | 0 | 1 | FALSE | 2 | 0: 1505, 1: 726 |
| male | 0 | 1 | FALSE | 2 | 1: 1629, 0: 602 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| age | 0 | 1 | 54.22 | 11.03 | 18.00 | 48.00 | 55.00 | 62.00 | 82.00 | ▁▃▇▇▂ |
| resting.systolic.bp | 0 | 1 | 110.51 | 17.93 | 80.00 | 98.00 | 110.00 | 122.00 | 184.00 | ▇▇▅▁▁ |
| resting.hr | 0 | 1 | 76.18 | 14.39 | 39.00 | 66.00 | 75.00 | 86.00 | 124.00 | ▂▇▇▃▁ |
| lvef.metabl | 0 | 1 | 20.26 | 7.34 | 5.00 | 15.00 | 20.00 | 25.00 | 40.00 | ▂▅▇▂▁ |
| peak.vo2 | 0 | 1 | 16.27 | 5.03 | 4.20 | 12.80 | 15.70 | 19.30 | 43.80 | ▃▇▂▁▁ |
| interval | 0 | 1 | 503.26 | 220.86 | 21.00 | 345.00 | 480.00 | 641.00 | 1415.00 | ▃▇▅▁▁ |
| ttodead | 0 | 1 | 62.75 | 36.77 | 1.00 | 30.00 | 62.00 | 95.00 | 127.00 | ▇▇▇▇▇ |
| bun | 0 | 1 | 25.28 | 12.57 | 4.32 | 17.00 | 23.00 | 29.33 | 129.00 | ▇▂▁▁▁ |
| sodium | 0 | 1 | 139.45 | 3.09 | 120.00 | 138.00 | 139.80 | 141.00 | 152.00 | ▁▁▆▇▁ |
| hgb | 0 | 1 | 13.54 | 1.41 | 7.20 | 12.80 | 13.58 | 14.40 | 19.20 | ▁▂▇▃▁ |
| glucose | 0 | 1 | 109.35 | 42.56 | 37.00 | 87.00 | 96.93 | 114.88 | 424.00 | ▇▂▁▁▁ |
| crcl | 0 | 1 | 90.71 | 43.05 | 5.76 | 60.93 | 83.13 | 110.33 | 385.84 | ▇▇▁▁▁ |
###################################
# Verifying the data dimensions
# for the train set
###################################
dim(PMA_PreModelling_Train)## [1] 2231 271.4 Data Exploration
Exploratory data analysis:
[A] Numeric variables which demonstrated differential relationships with the ttodead and died survival time and event status variables, based on trend line correlation with survival time, include:
[A.1] peak.vo2 variable (numeric)
[A.2] interval variable (numeric)
[A.3] sodium variable (numeric)
[B] Factor variables which demonstrated differential relationships with the ttodead and died survival time and event status variables, based on the Log-Rank Test p-value and estimated Kaplan-Meier survival curve separations, include:
[B.1] betablk variable (factor)
[B.2] diuretic.thiazide variable (factor)
[B.3] cad variable (factor)
[B.4] diuretic.loop variable (factor)
[B.5] male variable (factor)
[B.6] surgery.cabg variable (factor)
[B.7] insulin variable (factor)
Code Chunk | Output
##################################
# Loading dataset
##################################
EDA <- PMA_PreModelling_Train
##################################
# Loading dataset
##################################
EDA.DescriptiveStatistics <- EDA
EDA.DescriptiveStatistics$died <- ifelse(EDA.DescriptiveStatistics$died=="0","Survived+Censored","Died")
##################################
# Formulating a preliminary summary
# of descriptive statistics
##################################
EDA.DescriptiveStatistics %>%
tbl_summary(
by = died,
type = all_continuous() ~ "continuous2",
statistic = all_continuous() ~ c("{N_nonmiss}",
"{mean} ({sd})",
"{median} ({p25}, {p75})",
"{min}, {max}"),
missing = "no") %>%
add_overall() %>%
add_p()| Characteristic | Overall, N = 2,2311 | Died, N = 7261 | Survived+Censored, N = 1,5051 | p-value2 |
|---|---|---|---|---|
| age | <0.001 | |||
| N | 2,231 | 726 | 1,505 | |
| Mean (SD) | 54 (11) | 56 (11) | 53 (11) | |
| Median (IQR) | 55 (48, 62) | 58 (50, 64) | 54 (46, 61) | |
| Range | 18, 82 | 18, 82 | 19, 82 | |
| resting.systolic.bp | <0.001 | |||
| N | 2,231 | 726 | 1,505 | |
| Mean (SD) | 111 (18) | 108 (18) | 112 (18) | |
| Median (IQR) | 110 (98, 122) | 108 (94, 120) | 110 (100, 122) | |
| Range | 80, 184 | 80, 184 | 80, 184 | |
| resting.hr | <0.001 | |||
| N | 2,231 | 726 | 1,505 | |
| Mean (SD) | 76 (14) | 79 (14) | 75 (14) | |
| Median (IQR) | 75 (66, 86) | 78 (68, 88) | 74 (65, 84) | |
| Range | 39, 124 | 41, 121 | 39, 124 | |
| lvef.metabl | <0.001 | |||
| N | 2,231 | 726 | 1,505 | |
| Mean (SD) | 20 (7) | 19 (7) | 21 (7) | |
| Median (IQR) | 20 (15, 25) | 20 (15, 25) | 20 (15, 25) | |
| Range | 5, 40 | 5, 40 | 5, 40 | |
| peak.vo2 | <0.001 | |||
| N | 2,231 | 726 | 1,505 | |
| Mean (SD) | 16.3 (5.0) | 14.3 (4.5) | 17.2 (5.0) | |
| Median (IQR) | 15.7 (12.8, 19.3) | 13.9 (11.2, 16.6) | 16.7 (13.6, 20.2) | |
| Range | 4.2, 43.8 | 5.2, 43.4 | 4.2, 43.8 | |
| interval | <0.001 | |||
| N | 2,231 | 726 | 1,505 | |
| Mean (SD) | 503 (221) | 424 (192) | 542 (224) | |
| Median (IQR) | 480 (345, 641) | 415 (282, 540) | 532 (375, 700) | |
| Range | 21, 1,415 | 35, 1,250 | 21, 1,415 | |
| ttodead | <0.001 | |||
| N | 2,231 | 726 | 1,505 | |
| Mean (SD) | 63 (37) | 37 (29) | 75 (34) | |
| Median (IQR) | 62 (30, 95) | 33 (12, 57) | 79 (48, 104) | |
| Range | 1, 127 | 1, 119 | 11, 127 | |
| bun | <0.001 | |||
| N | 2,231 | 726 | 1,505 | |
| Mean (SD) | 25 (13) | 30 (15) | 23 (11) | |
| Median (IQR) | 23 (17, 29) | 27 (20, 35) | 21 (16, 27) | |
| Range | 4, 129 | 6, 129 | 4, 104 | |
| sodium | <0.001 | |||
| N | 2,231 | 726 | 1,505 | |
| Mean (SD) | 139.5 (3.1) | 139.1 (3.1) | 139.6 (3.1) | |
| Median (IQR) | 139.8 (138.0, 141.0) | 139.4 (138.0, 140.6) | 140.0 (138.0, 141.0) | |
| Range | 120.0, 152.0 | 122.0, 152.0 | 120.0, 150.0 | |
| hgb | <0.001 | |||
| N | 2,231 | 726 | 1,505 | |
| Mean (SD) | 13.54 (1.41) | 13.28 (1.41) | 13.67 (1.39) | |
| Median (IQR) | 13.58 (12.80, 14.40) | 13.35 (12.60, 14.08) | 13.70 (12.96, 14.59) | |
| Range | 7.20, 19.20 | 7.90, 18.80 | 7.20, 19.20 | |
| glucose | 0.001 | |||
| N | 2,231 | 726 | 1,505 | |
| Mean (SD) | 109 (43) | 113 (47) | 108 (40) | |
| Median (IQR) | 97 (87, 115) | 98 (89, 122) | 96 (87, 112) | |
| Range | 37, 424 | 37, 424 | 42, 403 | |
| crcl | <0.001 | |||
| N | 2,231 | 726 | 1,505 | |
| Mean (SD) | 91 (43) | 80 (40) | 96 (44) | |
| Median (IQR) | 83 (61, 110) | 71 (53, 96) | 88 (66, 116) | |
| Range | 6, 386 | 8, 386 | 6, 329 | |
| betablok | <0.001 | |||
| 0 | 802 (36%) | 349 (48%) | 453 (30%) | |
| 1 | 1,429 (64%) | 377 (52%) | 1,052 (70%) | |
| acei | 0.3 | |||
| 0 | 520 (23%) | 178 (25%) | 342 (23%) | |
| 1 | 1,711 (77%) | 548 (75%) | 1,163 (77%) | |
| angioten.II | 0.3 | |||
| 0 | 1,941 (87%) | 624 (86%) | 1,317 (88%) | |
| 1 | 290 (13%) | 102 (14%) | 188 (12%) | |
| anti.arrhy | 0.002 | |||
| 0 | 1,722 (77%) | 531 (73%) | 1,191 (79%) | |
| 1 | 509 (23%) | 195 (27%) | 314 (21%) | |
| anti.coag | <0.001 | |||
| 0 | 1,332 (60%) | 391 (54%) | 941 (63%) | |
| 1 | 899 (40%) | 335 (46%) | 564 (37%) | |
| aspirin | <0.001 | |||
| 0 | 1,193 (53%) | 430 (59%) | 763 (51%) | |
| 1 | 1,038 (47%) | 296 (41%) | 742 (49%) | |
| vasodilator | 0.14 | |||
| 0 | 2,095 (94%) | 674 (93%) | 1,421 (94%) | |
| 1 | 136 (6.1%) | 52 (7.2%) | 84 (5.6%) | |
| diuretic.loop | <0.001 | |||
| 0 | 351 (16%) | 82 (11%) | 269 (18%) | |
| 1 | 1,880 (84%) | 644 (89%) | 1,236 (82%) | |
| diuretic.thiazide | <0.001 | |||
| 0 | 1,952 (87%) | 601 (83%) | 1,351 (90%) | |
| 1 | 279 (13%) | 125 (17%) | 154 (10%) | |
| insulin | <0.001 | |||
| 0 | 2,016 (90%) | 631 (87%) | 1,385 (92%) | |
| 1 | 215 (9.6%) | 95 (13%) | 120 (8.0%) | |
| surgery.pacemaker | 0.8 | |||
| 0 | 1,729 (77%) | 560 (77%) | 1,169 (78%) | |
| 1 | 502 (23%) | 166 (23%) | 336 (22%) | |
| surgery.cabg | <0.001 | |||
| 0 | 1,637 (73%) | 490 (67%) | 1,147 (76%) | |
| 1 | 594 (27%) | 236 (33%) | 358 (24%) | |
| cad | <0.001 | |||
| 0 | 1,325 (59%) | 378 (52%) | 947 (63%) | |
| 1 | 906 (41%) | 348 (48%) | 558 (37%) | |
| male | <0.001 | |||
| 0 | 602 (27%) | 149 (21%) | 453 (30%) | |
| 1 | 1,629 (73%) | 577 (79%) | 1,052 (70%) | |
| 1 n (%) | ||||
| 2 Wilcoxon rank sum test; Pearson's Chi-squared test | ||||
##################################
# Listing all predictors
##################################
EDA.Predictors <- EDA[,!names(EDA) %in% c("died","ttodead")]
##################################
# Listing all numeric predictors
##################################
EDA.Predictors.Numeric <- EDA.Predictors[,sapply(EDA.Predictors, is.numeric)]
ncol(EDA.Predictors.Numeric)## [1] 11names(EDA.Predictors.Numeric)## [1] "age" "resting.systolic.bp" "resting.hr"
## [4] "lvef.metabl" "peak.vo2" "interval"
## [7] "bun" "sodium" "hgb"
## [10] "glucose" "crcl"##################################
# Listing all factor predictors
##################################
EDA.Predictors.Factor <- EDA.Predictors[,sapply(EDA.Predictors, is.factor)]
ncol(EDA.Predictors.Factor)## [1] 14names(EDA.Predictors.Factor)## [1] "betablok" "acei" "angioten.II"
## [4] "anti.arrhy" "anti.coag" "aspirin"
## [7] "vasodilator" "diuretic.loop" "diuretic.thiazide"
## [10] "insulin" "surgery.pacemaker" "surgery.cabg"
## [13] "cad" "male"##################################
# Formulating the box plots
##################################
featurePlot(x = EDA.Predictors.Numeric,
y = EDA$died,
plot = "box",
scales = list(x = list(relation="free", rot = 90),
y = list(relation="free")),
adjust = 1.5,
pch = "|")
##################################
# Formulating the scatter plot
##################################
featurePlot(x = EDA.Predictors.Numeric,
y = EDA$ttodead,
plot = "scatter",
type = c("p", "smooth"),
span = .5)
##################################
# Restructuring the dataset for
# for barchart analysis
##################################
died <- EDA$died
EDA.Bar.Source <- as.data.frame(cbind(died,
EDA.Predictors.Factor))
ncol(EDA.Bar.Source)## [1] 15names(EDA.Bar.Source)## [1] "died" "betablok" "acei"
## [4] "angioten.II" "anti.arrhy" "anti.coag"
## [7] "aspirin" "vasodilator" "diuretic.loop"
## [10] "diuretic.thiazide" "insulin" "surgery.pacemaker"
## [13] "surgery.cabg" "cad" "male"##################################
# Creating a function to formulate
# the proportions table
##################################
EDA.PropTable.Function <- function(FactorVar) {
EDA.Bar.Source.FactorVar <- EDA.Bar.Source[,c("died",
FactorVar)]
EDA.Bar.Source.FactorVar.Prop <- as.data.frame(prop.table(table(EDA.Bar.Source.FactorVar), 2))
names(EDA.Bar.Source.FactorVar.Prop)[2] <- "Structure"
EDA.Bar.Source.FactorVar.Prop$Variable <- rep(FactorVar,nrow(EDA.Bar.Source.FactorVar.Prop))
return(EDA.Bar.Source.FactorVar.Prop)
}
EDA.Bar.Source.FactorVar.Prop.Group <- rbind(EDA.PropTable.Function(names(EDA.Bar.Source)[2]),
EDA.PropTable.Function(names(EDA.Bar.Source)[3]),
EDA.PropTable.Function(names(EDA.Bar.Source)[4]),
EDA.PropTable.Function(names(EDA.Bar.Source)[5]),
EDA.PropTable.Function(names(EDA.Bar.Source)[6]),
EDA.PropTable.Function(names(EDA.Bar.Source)[7]),
EDA.PropTable.Function(names(EDA.Bar.Source)[8]),
EDA.PropTable.Function(names(EDA.Bar.Source)[9]),
EDA.PropTable.Function(names(EDA.Bar.Source)[10]),
EDA.PropTable.Function(names(EDA.Bar.Source)[11]),
EDA.PropTable.Function(names(EDA.Bar.Source)[12]),
EDA.PropTable.Function(names(EDA.Bar.Source)[13]),
EDA.PropTable.Function(names(EDA.Bar.Source)[14]),
EDA.PropTable.Function(names(EDA.Bar.Source)[15]))
(EDA.Barchart.FactorVar <- barchart(EDA.Bar.Source.FactorVar.Prop.Group[,3] ~
EDA.Bar.Source.FactorVar.Prop.Group[,2] | EDA.Bar.Source.FactorVar.Prop.Group[,4],
data=EDA.Bar.Source.FactorVar.Prop.Group,
groups = EDA.Bar.Source.FactorVar.Prop.Group[,1],
stack=TRUE,
ylab = "Proportion",
xlab = "Structure",
auto.key = list(adj = 1),
layout=(c(5,3))))
##################################
# Formulating the Kaplan-Meier plots
# for the categorical variables
##################################
KM_PreModelling_Train <- PMA_PreModelling_Train
KM_PreModelling_Train$died <- as.numeric(KM_PreModelling_Train$died )
KM_betablok <- survfit(Surv(ttodead, died) ~ betablok,
data=KM_PreModelling_Train)
KMPlot_betablok <- ggsurvplot(KM_betablok,
xlab="Survival Time",
ylab="Survival Probability",
break.time.by=12,
ggtheme=theme_bw(),
fontsize=3,
pval.size=3,
pval=TRUE)
KM_aspirin <- survfit(Surv(ttodead, died) ~ aspirin,
data=KM_PreModelling_Train)
KMPlot_aspirin <- ggsurvplot(KM_aspirin,
xlab="Survival Time",
ylab="Survival Probability",
break.time.by=12,
ggtheme=theme_bw(),
fontsize=3,
pval.size=3,
pval=TRUE)
KM_anti.coag <- survfit(Surv(ttodead, died) ~ anti.coag,
data=KM_PreModelling_Train)
KMPlot_anti.coag <- ggsurvplot(KM_anti.coag,
xlab="Survival Time",
ylab="Survival Probability",
break.time.by=12,
ggtheme=theme_bw(),
fontsize=3,
pval.size=3,
pval=TRUE)
KM_anti.arrhy <- survfit(Surv(ttodead, died) ~ anti.arrhy,
data=KM_PreModelling_Train)
KMPlot_anti.arrhy <- ggsurvplot(KM_anti.arrhy,
xlab="Survival Time",
ylab="Survival Probability",
break.time.by=12,
ggtheme=theme_bw(),
fontsize=3,
pval.size=3,
pval=TRUE)
KM_angioten.II <- survfit(Surv(ttodead, died) ~ angioten.II,
data=KM_PreModelling_Train)
KMPlot_angioten.II <- ggsurvplot(KM_angioten.II,
xlab="Survival Time",
ylab="Survival Probability",
break.time.by=12,
ggtheme=theme_bw(),
fontsize=3,
pval.size=3,
pval=TRUE)
KM_acei <- survfit(Surv(ttodead, died) ~ acei,
data=KM_PreModelling_Train)
KMPlot_acei <- ggsurvplot(KM_acei,
xlab="Survival Time",
ylab="Survival Probability",
break.time.by=12,
ggtheme=theme_bw(),
fontsize=3,
pval.size=3,
pval=TRUE)
KM_diuretic.thiazide <- survfit(Surv(ttodead, died) ~ diuretic.thiazide,
data=KM_PreModelling_Train)
KMPlot_diuretic.thiazide <- ggsurvplot(KM_diuretic.thiazide,
xlab="Survival Time",
ylab="Survival Probability",
break.time.by=12,
ggtheme=theme_bw(),
fontsize=3,
pval.size=3,
pval=TRUE)
KM_diuretic.loop <- survfit(Surv(ttodead, died) ~ diuretic.loop,
data=KM_PreModelling_Train)
KMPlot_diuretic.loop <- ggsurvplot(KM_diuretic.loop,
xlab="Survival Time",
ylab="Survival Probability",
break.time.by=12,
ggtheme=theme_bw(),
fontsize=3,
pval.size=3,
pval=TRUE)
KM_cad <- survfit(Surv(ttodead, died) ~ cad,
data=KM_PreModelling_Train)
KMPlot_cad <- ggsurvplot(KM_cad,
xlab="Survival Time",
ylab="Survival Probability",
break.time.by=12,
ggtheme=theme_bw(),
fontsize=3,
pval.size=3,
pval=TRUE)
KM_male <- survfit(Surv(ttodead, died) ~ male,
data=KM_PreModelling_Train)
KMPlot_male <- ggsurvplot(KM_male,
xlab="Survival Time",
ylab="Survival Probability",
break.time.by=12,
ggtheme=theme_bw(),
fontsize=3,
pval.size=3,
pval=TRUE)
KM_insulin <- survfit(Surv(ttodead, died) ~ insulin,
data=KM_PreModelling_Train)
KMPlot_insulin <- ggsurvplot(KM_insulin,
xlab="Survival Time",
ylab="Survival Probability",
break.time.by=12,
ggtheme=theme_bw(),
fontsize=3,
pval.size=3,
pval=TRUE)
KM_vasodilator <- survfit(Surv(ttodead, died) ~ vasodilator,
data=KM_PreModelling_Train)
KMPlot_vasodilator <- ggsurvplot(KM_vasodilator,
xlab="Survival Time",
ylab="Survival Probability",
break.time.by=12,
ggtheme=theme_bw(),
fontsize=3,
pval.size=3,
pval=TRUE)
KM_surgery.pacemaker <- survfit(Surv(ttodead, died) ~ surgery.pacemaker,
data=KM_PreModelling_Train)
KMPlot_surgery.pacemaker <- ggsurvplot(KM_surgery.pacemaker,
xlab="Survival Time",
ylab="Survival Probability",
break.time.by=12,
ggtheme=theme_bw(),
fontsize=3,
pval.size=3,
pval=TRUE)
KM_surgery.cabg <- survfit(Surv(ttodead, died) ~ surgery.cabg,
data=KM_PreModelling_Train)
KMPlot_surgery.cabg <- ggsurvplot(KM_surgery.cabg,
xlab="Survival Time",
ylab="Survival Probability",
break.time.by=12,
ggtheme=theme_bw(),
fontsize=3,
pval.size=3,
pval=TRUE)
KMPlot_List <- list()
KMPlot_List[[1]] <- KMPlot_betablok
KMPlot_List[[2]] <- KMPlot_acei
KMPlot_List[[3]] <- KMPlot_angioten.II
KMPlot_List[[4]] <- KMPlot_anti.arrhy
KMPlot_List[[5]] <- KMPlot_anti.coag
KMPlot_List[[6]] <- KMPlot_aspirin
KMPlot_List[[7]] <- KMPlot_vasodilator
KMPlot_List[[8]] <- KMPlot_diuretic.loop
KMPlot_List[[9]] <- KMPlot_diuretic.thiazide
KMPlot_List[[10]] <- KMPlot_insulin
KMPlot_List[[11]] <- KMPlot_surgery.pacemaker
KMPlot_List[[12]] <- KMPlot_surgery.cabg
KMPlot_List[[13]] <- KMPlot_cad
KMPlot_List[[14]] <- KMPlot_male
arrange_ggsurvplots(KMPlot_List,
ncol=3,
nrow=5)
1.5 Penalized Survival Models
1.5.1 Least Absolute Shrinkage and Selection Operator (LASSO)
Least Absolute Shrinkage and Selection Operator Regression is a regularization technique aimed at encouraging sparsity in the model by imposing a penalty on the absolute values of the regression coefficients. The algorithm adds a penalty term (referred to as the L1 regularization) defined as the sum of the absolute values of the regression coefficients multiplied by a tuning parameter lambda which controls the amount of penalty applied. The method formulates an optimization problem that minimizes the negative log-partial likelihood of the Cox proportional hazards model subject to the penalty terms. The optimization problem is solved using coordinate descent or least-angle regression, by iteratively updating regression coefficients, while incorporating the LASSO penalty. This approach can be useful for feature selection, as it tends to shrink the coefficients of less important predictor variables to zero, which can help simplify the model and improve its interpretability.
[A] The multivariate Cox proportional hazards regression model from the survival package was implemented with the LASSO penalty function applied using the hdnom, penalized and glmnet packages.
[B] The method contains 2 hyperparameters:
[B.1] nfolds = fold numbers of cross-validation fixed at a value = 5
[B.2] rule = model selection criterion fixed at a setting of optimal lambda such that error is within 1 standard error of the minimum
[C] The method selected 5 out of the 25 predictors for the final Cox Proportional Hazards model as follows:
[C.1] betablok variable (factor)
[C.2] peak.vo2 variable (numeric)
[C.3] interval variable (numeric)
[C.4] bun variable (numeric)
[C.5] male variable (factor)
[D] Optimal model parameter was was determined as:
[D.1] Lambda = 0.061
[E] Internally validated Time-Dependent AUROC metrics using bootstrap resampling of the train data showed good performance overall except for the last few time periods and are summarized as follows:
[E.1] 12 months = 0.75935
[E.2] 24 months = 0.76467
[E.3] 36 months = 0.65817
[E.4] 48 months = 0.66612
[E.5] 60 months = 0.65673
[E.6] 72 months = 0.67133
[E.7] 84 months = 0.71603
[E.8] 96 months = 0.59719
[E.9] 108 months = 0.67066
[E.10] 120 months = 0.56227
[F] Externally validated Time-Dependent AUROC metrics using the test data showed good performance overall except for the last few time periods and are summarized as follows:
[F.1] 12 months = 0.75529
[F.2] 24 months = 0.74168
[F.3] 36 months = 0.63662
[F.4] 48 months = 0.62143
[F.5] 60 months = 0.65494
[F.6] 72 months = 0.67220
[F.7] 84 months = 0.65163
[F.8] 96 months = 0.73170
[F.9] 108 months = 0.34070
[F.10] 120 months = 0.32500
[G] Model predictions at time = 60 months were reasonably calibrated for both the train and test data. Kaplan-Meier survival curves for the three risk groups from the calibration data were differentially profiled with p-value < 0.05.
[H] Model predictions at time = 120 months were reasonably calibrated for both the train and test data. Kaplan-Meier survival curves for the three risk groups from the calibration data were differentially profiled with p-value < 0.05.
Code Chunk | Output
##################################
# Loading the preprocessed data
##################################
PMA_PreModelling_LASSO <- peakVO2.Source[,!names(peakVO2.Source) %in% rownames(UF_CoxPHModelSummary_NonSig)]
##################################
# Setting the appropriate variable types
##################################
PMA_PreModelling_LASSO$died <- as.factor(PMA_PreModelling_LASSO$died)
##################################
# Creating a local object
# for the train and test sets
##################################
set.seed(88888888)
TrainIndex <- createDataPartition(PMA_PreModelling_LASSO$died,
p = 0.8,
list = FALSE,
times = 1)
PMA_PreModelling_LASSO_Train <- PMA_PreModelling_LASSO[ TrainIndex,]
PMA_PreModelling_LASSO_Test <- PMA_PreModelling_LASSO[-TrainIndex,]
PMA_PreModelling_LASSO_Train$died <- as.numeric(PMA_PreModelling_LASSO_Train$died)
PMA_PreModelling_LASSO_Test$died <- as.numeric(PMA_PreModelling_LASSO_Test$died)
##################################
# Formulating the model components
# for the train data
##################################
LASSO_Train_Predictors <- as.matrix(
PMA_PreModelling_LASSO_Train[,!names(PMA_PreModelling_LASSO_Train)
%in% c("ttodead","died")])
LASSO_Train_Time <- PMA_PreModelling_LASSO_Train$ttodead
LASSO_Train_Status <- PMA_PreModelling_LASSO_Train$died
LASSO_Train_Response <- survival::Surv(LASSO_Train_Time, LASSO_Train_Status)
##################################
# Fitting a LASSO model
##################################
LASSO_Train_CoxPH_Fit <- fit_lasso(LASSO_Train_Predictors,
LASSO_Train_Response,
nfolds = 5,
rule = "lambda.1se",
seed = 88888888)
##################################
# Extracting the model objects and
# optimal hyperparameters
# from the LASSO model
##################################
(LASSO_Train_CoxPH_Model <- LASSO_Train_CoxPH_Fit$model)##
## Call: glmnet(x = x, y = y, family = "cox", alpha = 1, lambda = lambda_opt)
##
## Df %Dev Lambda
## 1 5 3.53 0.06069(LASSO_Train_CoxPH_Model <- LASSO_Train_CoxPH_Fit$model$beta)## 25 x 1 sparse Matrix of class "dgCMatrix"
## s0
## age .
## betablok -0.0377520101
## acei .
## angioten.II .
## anti.arrhy .
## anti.coag .
## aspirin .
## vasodilator .
## diuretic.loop .
## diuretic.thiazide .
## insulin .
## surgery.pacemaker .
## surgery.cabg .
## resting.systolic.bp .
## resting.hr .
## lvef.metabl .
## peak.vo2 -0.0368006806
## interval -0.0006522149
## cad .
## bun 0.0136907025
## sodium .
## hgb .
## glucose .
## male 0.0325561008
## crcl .(LASSO_Train_CoxPH_Lambda <- LASSO_Train_CoxPH_Fit$lambda)## [1] 0.06068989##################################
# Conducting an internal validation
# of the LASSO model
# using a 50-cycle bootstrap
##################################
LASSO_Train_CoxPH_Model_InternalValidation <- validate(LASSO_Train_Predictors,
LASSO_Train_Time,
LASSO_Train_Status,
model.type = "lasso",
alpha = 1,
lambda = LASSO_Train_CoxPH_Fit$lambda,
method = "bootstrap",
boot.times = 50,
tauc.type = "UNO",
tauc.time = seq(12, 120, 12),
seed = 88888888)## Start bootstrap sample 1
## Start bootstrap sample 2
## Start bootstrap sample 3
## Start bootstrap sample 4
## Start bootstrap sample 5
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## Start bootstrap sample 24
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## Start bootstrap sample 36
## Start bootstrap sample 37
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## Start bootstrap sample 41
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## Start bootstrap sample 45
## Start bootstrap sample 46
## Start bootstrap sample 47
## Start bootstrap sample 48
## Start bootstrap sample 49
## Start bootstrap sample 50print(LASSO_Train_CoxPH_Model_InternalValidation)## High-Dimensional Cox Model Validation Object
## Random seed: 88888888
## Validation method: bootstrap
## Bootstrap samples: 50
## Model type: lasso
## glmnet model alpha: 1
## glmnet model lambda: 0.06068989
## glmnet model penalty factor: not specified
## Time-dependent AUC type: UNO
## Evaluation time points for tAUC: 12 24 36 48 60 72 84 96 108 120summary(LASSO_Train_CoxPH_Model_InternalValidation)## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7591606 0.7651505 0.6598180 0.6668614 0.6573271 0.6717549 0.7167163
## Min 0.7468533 0.7528590 0.6375564 0.6564413 0.6428270 0.6515716 0.6950791
## 0.25 Qt. 0.7556877 0.7619354 0.6532144 0.6632977 0.6524156 0.6662698 0.7111428
## Median 0.7593593 0.7646676 0.6581791 0.6661259 0.6567368 0.6713395 0.7160384
## 0.75 Qt. 0.7622169 0.7691297 0.6652583 0.6695167 0.6626247 0.6769218 0.7239472
## Max 0.7693340 0.7782079 0.6837068 0.6789755 0.6707026 0.6948704 0.7344319
## 96 108 120
## Mean 0.5969193 0.6653184 0.5362073
## Min 0.5663553 0.5794224 0.2889561
## 0.25 Qt. 0.5832283 0.6466446 0.4818164
## Median 0.5971917 0.6706664 0.5622711
## 0.75 Qt. 0.6065677 0.6875236 0.5988114
## Max 0.6471559 0.7249818 0.7560349LASSO_Train_IV_Summary <- data.frame(matrix(ncol=4, nrow=10))
names(LASSO_Train_IV_Summary) <- c("Model","Validation","MedianTDAUROC","Time")
for (i in 1:10){
LASSO_Train_IV_Summary[i,1] <- "LASSO"
LASSO_Train_IV_Summary[i,2] <- "Cross-Validation"
LASSO_Train_IV_Summary[i,3] <- summary(LASSO_Train_CoxPH_Model_InternalValidation)[[4,i]]
LASSO_Train_IV_Summary[i,4] <- i*12
}## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7591606 0.7651505 0.6598180 0.6668614 0.6573271 0.6717549 0.7167163
## Min 0.7468533 0.7528590 0.6375564 0.6564413 0.6428270 0.6515716 0.6950791
## 0.25 Qt. 0.7556877 0.7619354 0.6532144 0.6632977 0.6524156 0.6662698 0.7111428
## Median 0.7593593 0.7646676 0.6581791 0.6661259 0.6567368 0.6713395 0.7160384
## 0.75 Qt. 0.7622169 0.7691297 0.6652583 0.6695167 0.6626247 0.6769218 0.7239472
## Max 0.7693340 0.7782079 0.6837068 0.6789755 0.6707026 0.6948704 0.7344319
## 96 108 120
## Mean 0.5969193 0.6653184 0.5362073
## Min 0.5663553 0.5794224 0.2889561
## 0.25 Qt. 0.5832283 0.6466446 0.4818164
## Median 0.5971917 0.6706664 0.5622711
## 0.75 Qt. 0.6065677 0.6875236 0.5988114
## Max 0.6471559 0.7249818 0.7560349
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7591606 0.7651505 0.6598180 0.6668614 0.6573271 0.6717549 0.7167163
## Min 0.7468533 0.7528590 0.6375564 0.6564413 0.6428270 0.6515716 0.6950791
## 0.25 Qt. 0.7556877 0.7619354 0.6532144 0.6632977 0.6524156 0.6662698 0.7111428
## Median 0.7593593 0.7646676 0.6581791 0.6661259 0.6567368 0.6713395 0.7160384
## 0.75 Qt. 0.7622169 0.7691297 0.6652583 0.6695167 0.6626247 0.6769218 0.7239472
## Max 0.7693340 0.7782079 0.6837068 0.6789755 0.6707026 0.6948704 0.7344319
## 96 108 120
## Mean 0.5969193 0.6653184 0.5362073
## Min 0.5663553 0.5794224 0.2889561
## 0.25 Qt. 0.5832283 0.6466446 0.4818164
## Median 0.5971917 0.6706664 0.5622711
## 0.75 Qt. 0.6065677 0.6875236 0.5988114
## Max 0.6471559 0.7249818 0.7560349
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7591606 0.7651505 0.6598180 0.6668614 0.6573271 0.6717549 0.7167163
## Min 0.7468533 0.7528590 0.6375564 0.6564413 0.6428270 0.6515716 0.6950791
## 0.25 Qt. 0.7556877 0.7619354 0.6532144 0.6632977 0.6524156 0.6662698 0.7111428
## Median 0.7593593 0.7646676 0.6581791 0.6661259 0.6567368 0.6713395 0.7160384
## 0.75 Qt. 0.7622169 0.7691297 0.6652583 0.6695167 0.6626247 0.6769218 0.7239472
## Max 0.7693340 0.7782079 0.6837068 0.6789755 0.6707026 0.6948704 0.7344319
## 96 108 120
## Mean 0.5969193 0.6653184 0.5362073
## Min 0.5663553 0.5794224 0.2889561
## 0.25 Qt. 0.5832283 0.6466446 0.4818164
## Median 0.5971917 0.6706664 0.5622711
## 0.75 Qt. 0.6065677 0.6875236 0.5988114
## Max 0.6471559 0.7249818 0.7560349
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7591606 0.7651505 0.6598180 0.6668614 0.6573271 0.6717549 0.7167163
## Min 0.7468533 0.7528590 0.6375564 0.6564413 0.6428270 0.6515716 0.6950791
## 0.25 Qt. 0.7556877 0.7619354 0.6532144 0.6632977 0.6524156 0.6662698 0.7111428
## Median 0.7593593 0.7646676 0.6581791 0.6661259 0.6567368 0.6713395 0.7160384
## 0.75 Qt. 0.7622169 0.7691297 0.6652583 0.6695167 0.6626247 0.6769218 0.7239472
## Max 0.7693340 0.7782079 0.6837068 0.6789755 0.6707026 0.6948704 0.7344319
## 96 108 120
## Mean 0.5969193 0.6653184 0.5362073
## Min 0.5663553 0.5794224 0.2889561
## 0.25 Qt. 0.5832283 0.6466446 0.4818164
## Median 0.5971917 0.6706664 0.5622711
## 0.75 Qt. 0.6065677 0.6875236 0.5988114
## Max 0.6471559 0.7249818 0.7560349
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7591606 0.7651505 0.6598180 0.6668614 0.6573271 0.6717549 0.7167163
## Min 0.7468533 0.7528590 0.6375564 0.6564413 0.6428270 0.6515716 0.6950791
## 0.25 Qt. 0.7556877 0.7619354 0.6532144 0.6632977 0.6524156 0.6662698 0.7111428
## Median 0.7593593 0.7646676 0.6581791 0.6661259 0.6567368 0.6713395 0.7160384
## 0.75 Qt. 0.7622169 0.7691297 0.6652583 0.6695167 0.6626247 0.6769218 0.7239472
## Max 0.7693340 0.7782079 0.6837068 0.6789755 0.6707026 0.6948704 0.7344319
## 96 108 120
## Mean 0.5969193 0.6653184 0.5362073
## Min 0.5663553 0.5794224 0.2889561
## 0.25 Qt. 0.5832283 0.6466446 0.4818164
## Median 0.5971917 0.6706664 0.5622711
## 0.75 Qt. 0.6065677 0.6875236 0.5988114
## Max 0.6471559 0.7249818 0.7560349
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7591606 0.7651505 0.6598180 0.6668614 0.6573271 0.6717549 0.7167163
## Min 0.7468533 0.7528590 0.6375564 0.6564413 0.6428270 0.6515716 0.6950791
## 0.25 Qt. 0.7556877 0.7619354 0.6532144 0.6632977 0.6524156 0.6662698 0.7111428
## Median 0.7593593 0.7646676 0.6581791 0.6661259 0.6567368 0.6713395 0.7160384
## 0.75 Qt. 0.7622169 0.7691297 0.6652583 0.6695167 0.6626247 0.6769218 0.7239472
## Max 0.7693340 0.7782079 0.6837068 0.6789755 0.6707026 0.6948704 0.7344319
## 96 108 120
## Mean 0.5969193 0.6653184 0.5362073
## Min 0.5663553 0.5794224 0.2889561
## 0.25 Qt. 0.5832283 0.6466446 0.4818164
## Median 0.5971917 0.6706664 0.5622711
## 0.75 Qt. 0.6065677 0.6875236 0.5988114
## Max 0.6471559 0.7249818 0.7560349
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7591606 0.7651505 0.6598180 0.6668614 0.6573271 0.6717549 0.7167163
## Min 0.7468533 0.7528590 0.6375564 0.6564413 0.6428270 0.6515716 0.6950791
## 0.25 Qt. 0.7556877 0.7619354 0.6532144 0.6632977 0.6524156 0.6662698 0.7111428
## Median 0.7593593 0.7646676 0.6581791 0.6661259 0.6567368 0.6713395 0.7160384
## 0.75 Qt. 0.7622169 0.7691297 0.6652583 0.6695167 0.6626247 0.6769218 0.7239472
## Max 0.7693340 0.7782079 0.6837068 0.6789755 0.6707026 0.6948704 0.7344319
## 96 108 120
## Mean 0.5969193 0.6653184 0.5362073
## Min 0.5663553 0.5794224 0.2889561
## 0.25 Qt. 0.5832283 0.6466446 0.4818164
## Median 0.5971917 0.6706664 0.5622711
## 0.75 Qt. 0.6065677 0.6875236 0.5988114
## Max 0.6471559 0.7249818 0.7560349
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7591606 0.7651505 0.6598180 0.6668614 0.6573271 0.6717549 0.7167163
## Min 0.7468533 0.7528590 0.6375564 0.6564413 0.6428270 0.6515716 0.6950791
## 0.25 Qt. 0.7556877 0.7619354 0.6532144 0.6632977 0.6524156 0.6662698 0.7111428
## Median 0.7593593 0.7646676 0.6581791 0.6661259 0.6567368 0.6713395 0.7160384
## 0.75 Qt. 0.7622169 0.7691297 0.6652583 0.6695167 0.6626247 0.6769218 0.7239472
## Max 0.7693340 0.7782079 0.6837068 0.6789755 0.6707026 0.6948704 0.7344319
## 96 108 120
## Mean 0.5969193 0.6653184 0.5362073
## Min 0.5663553 0.5794224 0.2889561
## 0.25 Qt. 0.5832283 0.6466446 0.4818164
## Median 0.5971917 0.6706664 0.5622711
## 0.75 Qt. 0.6065677 0.6875236 0.5988114
## Max 0.6471559 0.7249818 0.7560349
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7591606 0.7651505 0.6598180 0.6668614 0.6573271 0.6717549 0.7167163
## Min 0.7468533 0.7528590 0.6375564 0.6564413 0.6428270 0.6515716 0.6950791
## 0.25 Qt. 0.7556877 0.7619354 0.6532144 0.6632977 0.6524156 0.6662698 0.7111428
## Median 0.7593593 0.7646676 0.6581791 0.6661259 0.6567368 0.6713395 0.7160384
## 0.75 Qt. 0.7622169 0.7691297 0.6652583 0.6695167 0.6626247 0.6769218 0.7239472
## Max 0.7693340 0.7782079 0.6837068 0.6789755 0.6707026 0.6948704 0.7344319
## 96 108 120
## Mean 0.5969193 0.6653184 0.5362073
## Min 0.5663553 0.5794224 0.2889561
## 0.25 Qt. 0.5832283 0.6466446 0.4818164
## Median 0.5971917 0.6706664 0.5622711
## 0.75 Qt. 0.6065677 0.6875236 0.5988114
## Max 0.6471559 0.7249818 0.7560349
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7591606 0.7651505 0.6598180 0.6668614 0.6573271 0.6717549 0.7167163
## Min 0.7468533 0.7528590 0.6375564 0.6564413 0.6428270 0.6515716 0.6950791
## 0.25 Qt. 0.7556877 0.7619354 0.6532144 0.6632977 0.6524156 0.6662698 0.7111428
## Median 0.7593593 0.7646676 0.6581791 0.6661259 0.6567368 0.6713395 0.7160384
## 0.75 Qt. 0.7622169 0.7691297 0.6652583 0.6695167 0.6626247 0.6769218 0.7239472
## Max 0.7693340 0.7782079 0.6837068 0.6789755 0.6707026 0.6948704 0.7344319
## 96 108 120
## Mean 0.5969193 0.6653184 0.5362073
## Min 0.5663553 0.5794224 0.2889561
## 0.25 Qt. 0.5832283 0.6466446 0.4818164
## Median 0.5971917 0.6706664 0.5622711
## 0.75 Qt. 0.6065677 0.6875236 0.5988114
## Max 0.6471559 0.7249818 0.7560349LASSO_Train_IV_Summary## Model Validation MedianTDAUROC Time
## 1 LASSO Cross-Validation 0.7593593 12
## 2 LASSO Cross-Validation 0.7646676 24
## 3 LASSO Cross-Validation 0.6581791 36
## 4 LASSO Cross-Validation 0.6661259 48
## 5 LASSO Cross-Validation 0.6567368 60
## 6 LASSO Cross-Validation 0.6713395 72
## 7 LASSO Cross-Validation 0.7160384 84
## 8 LASSO Cross-Validation 0.5971917 96
## 9 LASSO Cross-Validation 0.6706664 108
## 10 LASSO Cross-Validation 0.5622711 120plot(LASSO_Train_CoxPH_Model_InternalValidation,
ylim = c(0, 1))## 12 24 36 48 60 72 84
## Mean 0.7591606 0.7651505 0.6598180 0.6668614 0.6573271 0.6717549 0.7167163
## Min 0.7468533 0.7528590 0.6375564 0.6564413 0.6428270 0.6515716 0.6950791
## 0.25 Qt. 0.7556877 0.7619354 0.6532144 0.6632977 0.6524156 0.6662698 0.7111428
## Median 0.7593593 0.7646676 0.6581791 0.6661259 0.6567368 0.6713395 0.7160384
## 0.75 Qt. 0.7622169 0.7691297 0.6652583 0.6695167 0.6626247 0.6769218 0.7239472
## Max 0.7693340 0.7782079 0.6837068 0.6789755 0.6707026 0.6948704 0.7344319
## 96 108 120
## Mean 0.5969193 0.6653184 0.5362073
## Min 0.5663553 0.5794224 0.2889561
## 0.25 Qt. 0.5832283 0.6466446 0.4818164
## Median 0.5971917 0.6706664 0.5622711
## 0.75 Qt. 0.6065677 0.6875236 0.5988114
## Max 0.6471559 0.7249818 0.7560349
##################################
# Conducting an external validation
# of the LASSO model
# using the test data
##################################
LASSO_Test_Predictors <- as.matrix(
PMA_PreModelling_LASSO_Test[,!names(PMA_PreModelling_LASSO_Test)
%in% c("ttodead","died")])
LASSO_Test_Time <- PMA_PreModelling_LASSO_Test$ttodead
LASSO_Test_Status <- PMA_PreModelling_LASSO_Test$died
LASSO_Test_Response <- survival::Surv(LASSO_Test_Time, LASSO_Test_Status)
LASSO_Train_CoxPH_Model_ExternalValidation <- validate_external(LASSO_Train_CoxPH_Fit,
LASSO_Train_Predictors,
LASSO_Train_Time,
LASSO_Train_Status,
LASSO_Test_Predictors,
LASSO_Test_Time,
LASSO_Test_Status,
tauc.type = "UNO",
tauc.time = seq(12, 120, 12))
print(LASSO_Train_CoxPH_Model_ExternalValidation)## High-Dimensional Cox Model External Validation Object
## Model type: lasso
## Time-dependent AUC type: UNO
## Evaluation time points for tAUC: 12 24 36 48 60 72 84 96 108 120summary(LASSO_Train_CoxPH_Model_ExternalValidation)## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7552909 0.7416851 0.6366259 0.6214336 0.6549471 0.6722079 0.6516368
## 96 108 120
## AUC 0.7317083 0.3407045 0.325LASSO_Train_EV_Summary <- data.frame(matrix(ncol=4, nrow=10))
names(LASSO_Train_EV_Summary) <- c("Model","Validation","MedianTDAUROC","Time")
for (i in 1:10){
LASSO_Train_EV_Summary[i,1] <- "LASSO"
LASSO_Train_EV_Summary[i,2] <- "Test"
LASSO_Train_EV_Summary[i,3] <- summary(LASSO_Train_CoxPH_Model_ExternalValidation)[[1,i]]
LASSO_Train_EV_Summary[i,4] <- i*12
}## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7552909 0.7416851 0.6366259 0.6214336 0.6549471 0.6722079 0.6516368
## 96 108 120
## AUC 0.7317083 0.3407045 0.325
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7552909 0.7416851 0.6366259 0.6214336 0.6549471 0.6722079 0.6516368
## 96 108 120
## AUC 0.7317083 0.3407045 0.325
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7552909 0.7416851 0.6366259 0.6214336 0.6549471 0.6722079 0.6516368
## 96 108 120
## AUC 0.7317083 0.3407045 0.325
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7552909 0.7416851 0.6366259 0.6214336 0.6549471 0.6722079 0.6516368
## 96 108 120
## AUC 0.7317083 0.3407045 0.325
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7552909 0.7416851 0.6366259 0.6214336 0.6549471 0.6722079 0.6516368
## 96 108 120
## AUC 0.7317083 0.3407045 0.325
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7552909 0.7416851 0.6366259 0.6214336 0.6549471 0.6722079 0.6516368
## 96 108 120
## AUC 0.7317083 0.3407045 0.325
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7552909 0.7416851 0.6366259 0.6214336 0.6549471 0.6722079 0.6516368
## 96 108 120
## AUC 0.7317083 0.3407045 0.325
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7552909 0.7416851 0.6366259 0.6214336 0.6549471 0.6722079 0.6516368
## 96 108 120
## AUC 0.7317083 0.3407045 0.325
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7552909 0.7416851 0.6366259 0.6214336 0.6549471 0.6722079 0.6516368
## 96 108 120
## AUC 0.7317083 0.3407045 0.325
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7552909 0.7416851 0.6366259 0.6214336 0.6549471 0.6722079 0.6516368
## 96 108 120
## AUC 0.7317083 0.3407045 0.325LASSO_Train_EV_Summary## Model Validation MedianTDAUROC Time
## 1 LASSO Test 0.7552909 12
## 2 LASSO Test 0.7416851 24
## 3 LASSO Test 0.6366259 36
## 4 LASSO Test 0.6214336 48
## 5 LASSO Test 0.6549471 60
## 6 LASSO Test 0.6722079 72
## 7 LASSO Test 0.6516368 84
## 8 LASSO Test 0.7317083 96
## 9 LASSO Test 0.3407045 108
## 10 LASSO Test 0.3250000 120plot(LASSO_Train_CoxPH_Model_ExternalValidation,
ylim = c(0, 1))## 12 24 36 48 60 72 84
## AUC 0.7552909 0.7416851 0.6366259 0.6214336 0.6549471 0.6722079 0.6516368
## 96 108 120
## AUC 0.7317083 0.3407045 0.325
##################################
# Conducting an internal calibration
# of the LASSO model
# using a 50-cycle bootstrap
# for Year 5 (Time = 60)
##################################
LASSO_Train_CoxPH_Model_InternalCalibration <- calibrate(LASSO_Train_Predictors,
LASSO_Train_Time,
LASSO_Train_Status,
model.type = "lasso",
alpha = 1,
lambda = LASSO_Train_CoxPH_Fit$lambda,
method = "bootstrap",
boot.times = 50,
pred.at = 60,
ngroup = 3,
seed = 88888888)## Start bootstrap sample 1
## Start bootstrap sample 2
## Start bootstrap sample 3
## Start bootstrap sample 4
## Start bootstrap sample 5
## Start bootstrap sample 6
## Start bootstrap sample 7
## Start bootstrap sample 8
## Start bootstrap sample 9
## Start bootstrap sample 10
## Start bootstrap sample 11
## Start bootstrap sample 12
## Start bootstrap sample 13
## Start bootstrap sample 14
## Start bootstrap sample 15
## Start bootstrap sample 16
## Start bootstrap sample 17
## Start bootstrap sample 18
## Start bootstrap sample 19
## Start bootstrap sample 20
## Start bootstrap sample 21
## Start bootstrap sample 22
## Start bootstrap sample 23
## Start bootstrap sample 24
## Start bootstrap sample 25
## Start bootstrap sample 26
## Start bootstrap sample 27
## Start bootstrap sample 28
## Start bootstrap sample 29
## Start bootstrap sample 30
## Start bootstrap sample 31
## Start bootstrap sample 32
## Start bootstrap sample 33
## Start bootstrap sample 34
## Start bootstrap sample 35
## Start bootstrap sample 36
## Start bootstrap sample 37
## Start bootstrap sample 38
## Start bootstrap sample 39
## Start bootstrap sample 40
## Start bootstrap sample 41
## Start bootstrap sample 42
## Start bootstrap sample 43
## Start bootstrap sample 44
## Start bootstrap sample 45
## Start bootstrap sample 46
## Start bootstrap sample 47
## Start bootstrap sample 48
## Start bootstrap sample 49
## Start bootstrap sample 50print(LASSO_Train_CoxPH_Model_InternalCalibration)## High-Dimensional Cox Model Calibration Object
## Random seed: 88888888
## Calibration method: bootstrap
## Bootstrap samples: 50
## Model type: lasso
## glmnet model alpha: 1
## glmnet model lambda: 0.06068989
## glmnet model penalty factor: not specified
## Calibration time point: 60
## Number of groups formed for calibration: 3summary(LASSO_Train_CoxPH_Model_InternalCalibration)## Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.6332843 0.5270044 0.4853813 0.5721969
## 2 0.7269382 0.7714103 0.7359916 0.8085336
## 3 0.8083382 0.8713612 0.8430459 0.9006276plot(LASSO_Train_CoxPH_Model_InternalCalibration)
##################################
# Estimating survival curves
# of the LASSO model
# for the risk groups
# using the train data
##################################
LASSO_Train_CoxPH_Model_KMPlot <- kmplot( LASSO_Train_CoxPH_Model_InternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
##################################
# Conducting an internal calibration
# of the LASSO model
# using a 50-cycle bootstrap
# for Year 10 (Time = 120)
##################################
LASSO_Train_CoxPH_Model_InternalCalibration <- calibrate(LASSO_Train_Predictors,
LASSO_Train_Time,
LASSO_Train_Status,
model.type = "lasso",
alpha = 1,
lambda = LASSO_Train_CoxPH_Fit$lambda,
method = "bootstrap",
boot.times = 50,
pred.at = 120,
ngroup = 3,
seed = 88888888)## Start bootstrap sample 1
## Start bootstrap sample 2
## Start bootstrap sample 3
## Start bootstrap sample 4
## Start bootstrap sample 5
## Start bootstrap sample 6
## Start bootstrap sample 7
## Start bootstrap sample 8
## Start bootstrap sample 9
## Start bootstrap sample 10
## Start bootstrap sample 11
## Start bootstrap sample 12
## Start bootstrap sample 13
## Start bootstrap sample 14
## Start bootstrap sample 15
## Start bootstrap sample 16
## Start bootstrap sample 17
## Start bootstrap sample 18
## Start bootstrap sample 19
## Start bootstrap sample 20
## Start bootstrap sample 21
## Start bootstrap sample 22
## Start bootstrap sample 23
## Start bootstrap sample 24
## Start bootstrap sample 25
## Start bootstrap sample 26
## Start bootstrap sample 27
## Start bootstrap sample 28
## Start bootstrap sample 29
## Start bootstrap sample 30
## Start bootstrap sample 31
## Start bootstrap sample 32
## Start bootstrap sample 33
## Start bootstrap sample 34
## Start bootstrap sample 35
## Start bootstrap sample 36
## Start bootstrap sample 37
## Start bootstrap sample 38
## Start bootstrap sample 39
## Start bootstrap sample 40
## Start bootstrap sample 41
## Start bootstrap sample 42
## Start bootstrap sample 43
## Start bootstrap sample 44
## Start bootstrap sample 45
## Start bootstrap sample 46
## Start bootstrap sample 47
## Start bootstrap sample 48
## Start bootstrap sample 49
## Start bootstrap sample 50print(LASSO_Train_CoxPH_Model_InternalCalibration)## High-Dimensional Cox Model Calibration Object
## Random seed: 88888888
## Calibration method: bootstrap
## Bootstrap samples: 50
## Model type: lasso
## glmnet model alpha: 1
## glmnet model lambda: 0.06068989
## glmnet model penalty factor: not specified
## Calibration time point: 120
## Number of groups formed for calibration: 3summary(LASSO_Train_CoxPH_Model_InternalCalibration)## Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.4348425 0.3537721 0.3071830 0.4074271
## 2 0.5590328 0.5403953 0.4792945 0.6092852
## 3 0.6784640 0.7864948 0.7447209 0.8306119plot(LASSO_Train_CoxPH_Model_InternalCalibration)
##################################
# Estimating survival curves
# of the LASSO model
# for the risk groups
# using the train data
##################################
LASSO_Train_CoxPH_Model_KMPlot <- kmplot( LASSO_Train_CoxPH_Model_InternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
##################################
# Conducting an external calibration
# of the LASSO model
# using the test data
# for Year 5 (Time = 60)
##################################
LASSO_Train_CoxPH_Model_ExternalCalibration <- calibrate_external(LASSO_Train_CoxPH_Fit,
LASSO_Train_Predictors,
LASSO_Train_Time,
LASSO_Train_Status,
LASSO_Test_Predictors,
LASSO_Test_Time,
LASSO_Test_Status,
pred.at = 60,
ngroup = 3)
print(LASSO_Train_CoxPH_Model_ExternalCalibration)## High-Dimensional Cox Model External Calibration Object
## Model type: lasso
## Calibration time point: 60
## Number of groups formed for calibration: 3summary(LASSO_Train_CoxPH_Model_ExternalCalibration)## External Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.6255384 0.5581767 0.4789768 0.6504725
## 2 0.7299284 0.6789203 0.6031764 0.7641758
## 3 0.8074499 0.8808991 0.8275443 0.9376940plot(LASSO_Train_CoxPH_Model_ExternalCalibration)
##################################
# Estimating survival curves
# of the LASSO model
# for the risk groups
# using the test data
##################################
LASSO_Test_CoxPH_Model_KMPlot <- kmplot( LASSO_Train_CoxPH_Model_ExternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
##################################
# Conducting an external calibration
# of the LASSO model
# using the test data
# for Year 10 (Time = 120)
##################################
LASSO_Train_CoxPH_Model_ExternalCalibration <- calibrate_external(LASSO_Train_CoxPH_Fit,
LASSO_Train_Predictors,
LASSO_Train_Time,
LASSO_Train_Status,
LASSO_Test_Predictors,
LASSO_Test_Time,
LASSO_Test_Status,
pred.at = 120,
ngroup = 3)
print(LASSO_Train_CoxPH_Model_ExternalCalibration)## High-Dimensional Cox Model External Calibration Object
## Model type: lasso
## Calibration time point: 120
## Number of groups formed for calibration: 3summary(LASSO_Train_CoxPH_Model_ExternalCalibration)## External Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.4193310 0.4355568 0.3516847 0.5394313
## 2 0.5581158 0.5668081 0.4787103 0.6711187
## 3 0.6728682 0.7962081 0.7073103 0.8962789plot(LASSO_Train_CoxPH_Model_ExternalCalibration)
##################################
# Estimating survival curves
# of the LASSO model
# for the risk groups
# using the test data
##################################
LASSO_Test_CoxPH_Model_KMPlot <- kmplot( LASSO_Train_CoxPH_Model_ExternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
1.5.2 Elastic Net (ENET)
Elastic Net Regression is a regularization technique aimed at balancing between sparsity and including correlated predictors. The algorithm is a combination of the ridge and LASSO regression methods, by adding both L1 and L2 regularization terms in the cost function. The LASSO penalty promotes sparsity by encouraging some coefficients to be exactly zero, while the ridge penalty encourages shrinkage of coefficients towards zero. This method introduces two tuning parameters: lambda, which controls the overall amount of penalty applied, and alpha which determines the trade-off between the LASSO and ridge penalties. The method formulates an optimization problem that minimizes the negative log-partial likelihood of the Cox proportional hazards model subject to the penalty terms. The optimization problem is solved using coordinate descent or proximal gradient methods, by iteratively updating regression coefficients, while considering both the LASSO and ridge penalties. This approach can be useful for striking a balance between sparsity and accounting for correlated predictors by effectively identifying important predictors while handling multicollinearity and improving both the interpretability and predictive performance of the survival model.
[A] The multivariate Cox proportional hazards regression model from the survival package was implemented with the ENET penalty function applied using the hdnom, penalized and glmnet packages.
[B] The method contains 3 hyperparameters:
[B.1] nfolds = fold numbers of cross-validation fixed at a value = 5
[B.2] rule = model selection criterion fixed at a setting of optimal lambda such that error is within 1 standard error of the minimum
[B.3] alphas = elastic net mixing parameter made to vary across a range of values equal to 0.05 to 0.95
[C] The method selected 7 out of the 25 predictors for the final Cox Proportional Hazards model as follows:
[C.1] betablok variable (factor)
[C.2] resting.hr variable (numeric)
[C.3] lvef.metabl variable (numeric)
[C.4] peak.vo2 variable (numeric)
[C.5] interval variable (numeric)
[C.6] bun variable (numeric)
[C.7] male variable (factor)
[D] Optimal model parameters were determined as:
[D.1] Lambda = 0.209
[D.2] Alpha = 0.200
[E] Internally validated Time-Dependent AUROC metrics using bootstrap resampling of the train data showed good performance overall except for the last few time periods and are summarized as follows:
[E.1] 12 months = 0.76568
[E.2] 24 months = 0.77544
[E.3] 36 months = 0.66988
[E.4] 48 months = 0.67471
[E.5] 60 months = 0.66267
[E.6] 72 months = 0.67954
[E.7] 84 months = 0.71874
[E.8] 96 months = 0.60604
[E.9] 108 months = 0.66103
[E.10] 120 months = 0.46548
[F] Externally validated Time-Dependent AUROC metrics using the test data showed good performance overall except for the last few time periods and are summarized as follows:
[F.1] 12 months = 0.77262
[F.2] 24 months = 0.74574
[F.3] 36 months = 0.63438
[F.4] 48 months = 0.61830
[F.5] 60 months = 0.65565
[F.6] 72 months = 0.66441
[F.7] 84 months = 0.62992
[F.8] 96 months = 0.69808
[F.9] 108 months = 0.41854
[F.10] 120 months = 0.35000
[G] Model predictions at time = 60 months were reasonably calibrated for both the train and test data. Kaplan-Meier survival curves for the three risk groups from the calibration data were differentially profiled with p-value < 0.05.
[H] Model predictions at time = 120 months were reasonably calibrated for both the train and test data. Kaplan-Meier survival curves for the three risk groups from the calibration data were differentially profiled with p-value < 0.05.
Code Chunk | Output
##################################
# Loading the preprocessed data
##################################
PMA_PreModelling_ENET <- peakVO2.Source[,!names(peakVO2.Source) %in% rownames(UF_CoxPHModelSummary_NonSig)]
##################################
# Setting the appropriate variable types
##################################
PMA_PreModelling_ENET$died <- as.factor(PMA_PreModelling_ENET$died)
##################################
# Creating a local object
# for the train and test sets
##################################
set.seed(88888888)
TrainIndex <- createDataPartition(PMA_PreModelling_ENET$died,
p = 0.8,
list = FALSE,
times = 1)
PMA_PreModelling_ENET_Train <- PMA_PreModelling_ENET[ TrainIndex,]
PMA_PreModelling_ENET_Test <- PMA_PreModelling_ENET[-TrainIndex,]
PMA_PreModelling_ENET_Train$died <- as.numeric(PMA_PreModelling_ENET_Train$died)
PMA_PreModelling_ENET_Test$died <- as.numeric(PMA_PreModelling_ENET_Test$died)
##################################
# Formulating the model components
# for the train data
##################################
ENET_Train_Predictors <- as.matrix(
PMA_PreModelling_ENET_Train[,!names(PMA_PreModelling_ENET_Train)
%in% c("ttodead","died")])
ENET_Train_Time <- PMA_PreModelling_ENET_Train$ttodead
ENET_Train_Status <- PMA_PreModelling_ENET_Train$died
ENET_Train_Response <- survival::Surv(ENET_Train_Time, ENET_Train_Status)
##################################
# Fitting a ENET model
##################################
ENET_Train_CoxPH_Fit <- fit_enet(ENET_Train_Predictors,
ENET_Train_Response,
nfolds = 5,
alphas = seq(0.05, 0.95, 0.05),
rule = "lambda.1se",
seed = 88888888)
##################################
# Extracting the model objects and
# optimal hyperparameters
# from the ENET model
##################################
(ENET_Train_CoxPH_Model <- ENET_Train_CoxPH_Fit$model)##
## Call: glmnet(x = x, y = y, family = "cox", alpha = alpha_opt, lambda = lambda_opt)
##
## Df %Dev Lambda
## 1 7 3.67 0.2092(ENET_Train_CoxPH_Model <- ENET_Train_CoxPH_Fit$model$beta)## 25 x 1 sparse Matrix of class "dgCMatrix"
## s0
## age .
## betablok -1.028539e-01
## acei .
## angioten.II .
## anti.arrhy .
## anti.coag .
## aspirin .
## vasodilator .
## diuretic.loop .
## diuretic.thiazide .
## insulin .
## surgery.pacemaker .
## surgery.cabg .
## resting.systolic.bp .
## resting.hr 3.370436e-05
## lvef.metabl -2.883289e-03
## peak.vo2 -3.125922e-02
## interval -6.849160e-04
## cad .
## bun 1.260275e-02
## sodium .
## hgb .
## glucose .
## male 1.050350e-01
## crcl .(ENET_Train_CoxPH_Alpha <- ENET_Train_CoxPH_Fit$alpha)## [1] 0.2(ENET_Train_CoxPH_Lambda <- ENET_Train_CoxPH_Fit$lambda)## [1] 0.2091559##################################
# Conducting an internal validation
# of the ENET model
# using a 50-cycle bootstrap
##################################
ENET_Train_CoxPH_Model_InternalValidation <- validate(ENET_Train_Predictors,
ENET_Train_Time,
ENET_Train_Status,
model.type = "enet",
alpha = ENET_Train_CoxPH_Fit$alpha,
lambda = ENET_Train_CoxPH_Fit$lambda,
method = "bootstrap",
boot.times = 50,
tauc.type = "UNO",
tauc.time = seq(12, 120, 12),
seed = 88888888)## Start bootstrap sample 1
## Start bootstrap sample 2
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## Start bootstrap sample 45
## Start bootstrap sample 46
## Start bootstrap sample 47
## Start bootstrap sample 48
## Start bootstrap sample 49
## Start bootstrap sample 50print(ENET_Train_CoxPH_Model_InternalValidation)## High-Dimensional Cox Model Validation Object
## Random seed: 88888888
## Validation method: bootstrap
## Bootstrap samples: 50
## Model type: enet
## glmnet model alpha: 0.2
## glmnet model lambda: 0.2091559
## glmnet model penalty factor: not specified
## Time-dependent AUC type: UNO
## Evaluation time points for tAUC: 12 24 36 48 60 72 84 96 108 120summary(ENET_Train_CoxPH_Model_InternalValidation)## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7655219 0.7743178 0.6705211 0.6745131 0.6634270 0.6790747 0.7193120
## Min 0.7538623 0.7578300 0.6491276 0.6660373 0.6448826 0.6633212 0.6989629
## 0.25 Qt. 0.7632845 0.7718722 0.6637615 0.6694505 0.6584857 0.6739864 0.7136789
## Median 0.7656844 0.7754405 0.6698848 0.6747182 0.6626752 0.6795469 0.7187430
## 0.75 Qt. 0.7695715 0.7770750 0.6769634 0.6786613 0.6692509 0.6842525 0.7239570
## Max 0.7728373 0.7829010 0.6970798 0.6865302 0.6783294 0.6962400 0.7417582
## 96 108 120
## Mean 0.6081846 0.6593068 0.4630437
## Min 0.5735731 0.5926576 0.3001157
## 0.25 Qt. 0.5994215 0.6377880 0.4174266
## Median 0.6060480 0.6610317 0.4654877
## 0.75 Qt. 0.6181274 0.6758354 0.5181776
## Max 0.6474141 0.7464270 0.6396987ENET_Train_IV_Summary <- data.frame(matrix(ncol=4, nrow=10))
names(ENET_Train_IV_Summary) <- c("Model","Validation","MedianTDAUROC","Time")
for (i in 1:10){
ENET_Train_IV_Summary[i,1] <- "ENET"
ENET_Train_IV_Summary[i,2] <- "Cross-Validation"
ENET_Train_IV_Summary[i,3] <- summary(ENET_Train_CoxPH_Model_InternalValidation)[[4,i]]
ENET_Train_IV_Summary[i,4] <- i*12
}## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7655219 0.7743178 0.6705211 0.6745131 0.6634270 0.6790747 0.7193120
## Min 0.7538623 0.7578300 0.6491276 0.6660373 0.6448826 0.6633212 0.6989629
## 0.25 Qt. 0.7632845 0.7718722 0.6637615 0.6694505 0.6584857 0.6739864 0.7136789
## Median 0.7656844 0.7754405 0.6698848 0.6747182 0.6626752 0.6795469 0.7187430
## 0.75 Qt. 0.7695715 0.7770750 0.6769634 0.6786613 0.6692509 0.6842525 0.7239570
## Max 0.7728373 0.7829010 0.6970798 0.6865302 0.6783294 0.6962400 0.7417582
## 96 108 120
## Mean 0.6081846 0.6593068 0.4630437
## Min 0.5735731 0.5926576 0.3001157
## 0.25 Qt. 0.5994215 0.6377880 0.4174266
## Median 0.6060480 0.6610317 0.4654877
## 0.75 Qt. 0.6181274 0.6758354 0.5181776
## Max 0.6474141 0.7464270 0.6396987
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7655219 0.7743178 0.6705211 0.6745131 0.6634270 0.6790747 0.7193120
## Min 0.7538623 0.7578300 0.6491276 0.6660373 0.6448826 0.6633212 0.6989629
## 0.25 Qt. 0.7632845 0.7718722 0.6637615 0.6694505 0.6584857 0.6739864 0.7136789
## Median 0.7656844 0.7754405 0.6698848 0.6747182 0.6626752 0.6795469 0.7187430
## 0.75 Qt. 0.7695715 0.7770750 0.6769634 0.6786613 0.6692509 0.6842525 0.7239570
## Max 0.7728373 0.7829010 0.6970798 0.6865302 0.6783294 0.6962400 0.7417582
## 96 108 120
## Mean 0.6081846 0.6593068 0.4630437
## Min 0.5735731 0.5926576 0.3001157
## 0.25 Qt. 0.5994215 0.6377880 0.4174266
## Median 0.6060480 0.6610317 0.4654877
## 0.75 Qt. 0.6181274 0.6758354 0.5181776
## Max 0.6474141 0.7464270 0.6396987
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7655219 0.7743178 0.6705211 0.6745131 0.6634270 0.6790747 0.7193120
## Min 0.7538623 0.7578300 0.6491276 0.6660373 0.6448826 0.6633212 0.6989629
## 0.25 Qt. 0.7632845 0.7718722 0.6637615 0.6694505 0.6584857 0.6739864 0.7136789
## Median 0.7656844 0.7754405 0.6698848 0.6747182 0.6626752 0.6795469 0.7187430
## 0.75 Qt. 0.7695715 0.7770750 0.6769634 0.6786613 0.6692509 0.6842525 0.7239570
## Max 0.7728373 0.7829010 0.6970798 0.6865302 0.6783294 0.6962400 0.7417582
## 96 108 120
## Mean 0.6081846 0.6593068 0.4630437
## Min 0.5735731 0.5926576 0.3001157
## 0.25 Qt. 0.5994215 0.6377880 0.4174266
## Median 0.6060480 0.6610317 0.4654877
## 0.75 Qt. 0.6181274 0.6758354 0.5181776
## Max 0.6474141 0.7464270 0.6396987
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7655219 0.7743178 0.6705211 0.6745131 0.6634270 0.6790747 0.7193120
## Min 0.7538623 0.7578300 0.6491276 0.6660373 0.6448826 0.6633212 0.6989629
## 0.25 Qt. 0.7632845 0.7718722 0.6637615 0.6694505 0.6584857 0.6739864 0.7136789
## Median 0.7656844 0.7754405 0.6698848 0.6747182 0.6626752 0.6795469 0.7187430
## 0.75 Qt. 0.7695715 0.7770750 0.6769634 0.6786613 0.6692509 0.6842525 0.7239570
## Max 0.7728373 0.7829010 0.6970798 0.6865302 0.6783294 0.6962400 0.7417582
## 96 108 120
## Mean 0.6081846 0.6593068 0.4630437
## Min 0.5735731 0.5926576 0.3001157
## 0.25 Qt. 0.5994215 0.6377880 0.4174266
## Median 0.6060480 0.6610317 0.4654877
## 0.75 Qt. 0.6181274 0.6758354 0.5181776
## Max 0.6474141 0.7464270 0.6396987
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7655219 0.7743178 0.6705211 0.6745131 0.6634270 0.6790747 0.7193120
## Min 0.7538623 0.7578300 0.6491276 0.6660373 0.6448826 0.6633212 0.6989629
## 0.25 Qt. 0.7632845 0.7718722 0.6637615 0.6694505 0.6584857 0.6739864 0.7136789
## Median 0.7656844 0.7754405 0.6698848 0.6747182 0.6626752 0.6795469 0.7187430
## 0.75 Qt. 0.7695715 0.7770750 0.6769634 0.6786613 0.6692509 0.6842525 0.7239570
## Max 0.7728373 0.7829010 0.6970798 0.6865302 0.6783294 0.6962400 0.7417582
## 96 108 120
## Mean 0.6081846 0.6593068 0.4630437
## Min 0.5735731 0.5926576 0.3001157
## 0.25 Qt. 0.5994215 0.6377880 0.4174266
## Median 0.6060480 0.6610317 0.4654877
## 0.75 Qt. 0.6181274 0.6758354 0.5181776
## Max 0.6474141 0.7464270 0.6396987
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7655219 0.7743178 0.6705211 0.6745131 0.6634270 0.6790747 0.7193120
## Min 0.7538623 0.7578300 0.6491276 0.6660373 0.6448826 0.6633212 0.6989629
## 0.25 Qt. 0.7632845 0.7718722 0.6637615 0.6694505 0.6584857 0.6739864 0.7136789
## Median 0.7656844 0.7754405 0.6698848 0.6747182 0.6626752 0.6795469 0.7187430
## 0.75 Qt. 0.7695715 0.7770750 0.6769634 0.6786613 0.6692509 0.6842525 0.7239570
## Max 0.7728373 0.7829010 0.6970798 0.6865302 0.6783294 0.6962400 0.7417582
## 96 108 120
## Mean 0.6081846 0.6593068 0.4630437
## Min 0.5735731 0.5926576 0.3001157
## 0.25 Qt. 0.5994215 0.6377880 0.4174266
## Median 0.6060480 0.6610317 0.4654877
## 0.75 Qt. 0.6181274 0.6758354 0.5181776
## Max 0.6474141 0.7464270 0.6396987
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7655219 0.7743178 0.6705211 0.6745131 0.6634270 0.6790747 0.7193120
## Min 0.7538623 0.7578300 0.6491276 0.6660373 0.6448826 0.6633212 0.6989629
## 0.25 Qt. 0.7632845 0.7718722 0.6637615 0.6694505 0.6584857 0.6739864 0.7136789
## Median 0.7656844 0.7754405 0.6698848 0.6747182 0.6626752 0.6795469 0.7187430
## 0.75 Qt. 0.7695715 0.7770750 0.6769634 0.6786613 0.6692509 0.6842525 0.7239570
## Max 0.7728373 0.7829010 0.6970798 0.6865302 0.6783294 0.6962400 0.7417582
## 96 108 120
## Mean 0.6081846 0.6593068 0.4630437
## Min 0.5735731 0.5926576 0.3001157
## 0.25 Qt. 0.5994215 0.6377880 0.4174266
## Median 0.6060480 0.6610317 0.4654877
## 0.75 Qt. 0.6181274 0.6758354 0.5181776
## Max 0.6474141 0.7464270 0.6396987
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7655219 0.7743178 0.6705211 0.6745131 0.6634270 0.6790747 0.7193120
## Min 0.7538623 0.7578300 0.6491276 0.6660373 0.6448826 0.6633212 0.6989629
## 0.25 Qt. 0.7632845 0.7718722 0.6637615 0.6694505 0.6584857 0.6739864 0.7136789
## Median 0.7656844 0.7754405 0.6698848 0.6747182 0.6626752 0.6795469 0.7187430
## 0.75 Qt. 0.7695715 0.7770750 0.6769634 0.6786613 0.6692509 0.6842525 0.7239570
## Max 0.7728373 0.7829010 0.6970798 0.6865302 0.6783294 0.6962400 0.7417582
## 96 108 120
## Mean 0.6081846 0.6593068 0.4630437
## Min 0.5735731 0.5926576 0.3001157
## 0.25 Qt. 0.5994215 0.6377880 0.4174266
## Median 0.6060480 0.6610317 0.4654877
## 0.75 Qt. 0.6181274 0.6758354 0.5181776
## Max 0.6474141 0.7464270 0.6396987
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7655219 0.7743178 0.6705211 0.6745131 0.6634270 0.6790747 0.7193120
## Min 0.7538623 0.7578300 0.6491276 0.6660373 0.6448826 0.6633212 0.6989629
## 0.25 Qt. 0.7632845 0.7718722 0.6637615 0.6694505 0.6584857 0.6739864 0.7136789
## Median 0.7656844 0.7754405 0.6698848 0.6747182 0.6626752 0.6795469 0.7187430
## 0.75 Qt. 0.7695715 0.7770750 0.6769634 0.6786613 0.6692509 0.6842525 0.7239570
## Max 0.7728373 0.7829010 0.6970798 0.6865302 0.6783294 0.6962400 0.7417582
## 96 108 120
## Mean 0.6081846 0.6593068 0.4630437
## Min 0.5735731 0.5926576 0.3001157
## 0.25 Qt. 0.5994215 0.6377880 0.4174266
## Median 0.6060480 0.6610317 0.4654877
## 0.75 Qt. 0.6181274 0.6758354 0.5181776
## Max 0.6474141 0.7464270 0.6396987
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7655219 0.7743178 0.6705211 0.6745131 0.6634270 0.6790747 0.7193120
## Min 0.7538623 0.7578300 0.6491276 0.6660373 0.6448826 0.6633212 0.6989629
## 0.25 Qt. 0.7632845 0.7718722 0.6637615 0.6694505 0.6584857 0.6739864 0.7136789
## Median 0.7656844 0.7754405 0.6698848 0.6747182 0.6626752 0.6795469 0.7187430
## 0.75 Qt. 0.7695715 0.7770750 0.6769634 0.6786613 0.6692509 0.6842525 0.7239570
## Max 0.7728373 0.7829010 0.6970798 0.6865302 0.6783294 0.6962400 0.7417582
## 96 108 120
## Mean 0.6081846 0.6593068 0.4630437
## Min 0.5735731 0.5926576 0.3001157
## 0.25 Qt. 0.5994215 0.6377880 0.4174266
## Median 0.6060480 0.6610317 0.4654877
## 0.75 Qt. 0.6181274 0.6758354 0.5181776
## Max 0.6474141 0.7464270 0.6396987ENET_Train_IV_Summary## Model Validation MedianTDAUROC Time
## 1 ENET Cross-Validation 0.7656844 12
## 2 ENET Cross-Validation 0.7754405 24
## 3 ENET Cross-Validation 0.6698848 36
## 4 ENET Cross-Validation 0.6747182 48
## 5 ENET Cross-Validation 0.6626752 60
## 6 ENET Cross-Validation 0.6795469 72
## 7 ENET Cross-Validation 0.7187430 84
## 8 ENET Cross-Validation 0.6060480 96
## 9 ENET Cross-Validation 0.6610317 108
## 10 ENET Cross-Validation 0.4654877 120plot(ENET_Train_CoxPH_Model_InternalValidation,
ylim = c(0, 1))## 12 24 36 48 60 72 84
## Mean 0.7655219 0.7743178 0.6705211 0.6745131 0.6634270 0.6790747 0.7193120
## Min 0.7538623 0.7578300 0.6491276 0.6660373 0.6448826 0.6633212 0.6989629
## 0.25 Qt. 0.7632845 0.7718722 0.6637615 0.6694505 0.6584857 0.6739864 0.7136789
## Median 0.7656844 0.7754405 0.6698848 0.6747182 0.6626752 0.6795469 0.7187430
## 0.75 Qt. 0.7695715 0.7770750 0.6769634 0.6786613 0.6692509 0.6842525 0.7239570
## Max 0.7728373 0.7829010 0.6970798 0.6865302 0.6783294 0.6962400 0.7417582
## 96 108 120
## Mean 0.6081846 0.6593068 0.4630437
## Min 0.5735731 0.5926576 0.3001157
## 0.25 Qt. 0.5994215 0.6377880 0.4174266
## Median 0.6060480 0.6610317 0.4654877
## 0.75 Qt. 0.6181274 0.6758354 0.5181776
## Max 0.6474141 0.7464270 0.6396987
##################################
# Conducting an external validation
# of the ENET model
# using the test data
##################################
ENET_Test_Predictors <- as.matrix(
PMA_PreModelling_ENET_Test[,!names(PMA_PreModelling_ENET_Test)
%in% c("ttodead","died")])
ENET_Test_Time <- PMA_PreModelling_ENET_Test$ttodead
ENET_Test_Status <- PMA_PreModelling_ENET_Test$died
ENET_Test_Response <- survival::Surv(ENET_Test_Time, ENET_Test_Status)
ENET_Train_CoxPH_Model_ExternalValidation <- validate_external(ENET_Train_CoxPH_Fit,
ENET_Train_Predictors,
ENET_Train_Time,
ENET_Train_Status,
ENET_Test_Predictors,
ENET_Test_Time,
ENET_Test_Status,
tauc.type = "UNO",
tauc.time = seq(12, 120, 12))
print(ENET_Train_CoxPH_Model_ExternalValidation)## High-Dimensional Cox Model External Validation Object
## Model type: enet
## Time-dependent AUC type: UNO
## Evaluation time points for tAUC: 12 24 36 48 60 72 84 96 108 120summary(ENET_Train_CoxPH_Model_ExternalValidation)## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.772624 0.7457409 0.6343841 0.6183024 0.6556561 0.664411 0.6299208
## 96 108 120
## AUC 0.6980839 0.4185411 0.35ENET_Train_EV_Summary <- data.frame(matrix(ncol=4, nrow=10))
names(ENET_Train_EV_Summary) <- c("Model","Validation","MedianTDAUROC","Time")
for (i in 1:10){
ENET_Train_EV_Summary[i,1] <- "ENET"
ENET_Train_EV_Summary[i,2] <- "Test"
ENET_Train_EV_Summary[i,3] <- summary(ENET_Train_CoxPH_Model_ExternalValidation)[[1,i]]
ENET_Train_EV_Summary[i,4] <- i*12
}## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.772624 0.7457409 0.6343841 0.6183024 0.6556561 0.664411 0.6299208
## 96 108 120
## AUC 0.6980839 0.4185411 0.35
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.772624 0.7457409 0.6343841 0.6183024 0.6556561 0.664411 0.6299208
## 96 108 120
## AUC 0.6980839 0.4185411 0.35
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.772624 0.7457409 0.6343841 0.6183024 0.6556561 0.664411 0.6299208
## 96 108 120
## AUC 0.6980839 0.4185411 0.35
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.772624 0.7457409 0.6343841 0.6183024 0.6556561 0.664411 0.6299208
## 96 108 120
## AUC 0.6980839 0.4185411 0.35
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.772624 0.7457409 0.6343841 0.6183024 0.6556561 0.664411 0.6299208
## 96 108 120
## AUC 0.6980839 0.4185411 0.35
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.772624 0.7457409 0.6343841 0.6183024 0.6556561 0.664411 0.6299208
## 96 108 120
## AUC 0.6980839 0.4185411 0.35
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.772624 0.7457409 0.6343841 0.6183024 0.6556561 0.664411 0.6299208
## 96 108 120
## AUC 0.6980839 0.4185411 0.35
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.772624 0.7457409 0.6343841 0.6183024 0.6556561 0.664411 0.6299208
## 96 108 120
## AUC 0.6980839 0.4185411 0.35
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.772624 0.7457409 0.6343841 0.6183024 0.6556561 0.664411 0.6299208
## 96 108 120
## AUC 0.6980839 0.4185411 0.35
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.772624 0.7457409 0.6343841 0.6183024 0.6556561 0.664411 0.6299208
## 96 108 120
## AUC 0.6980839 0.4185411 0.35ENET_Train_EV_Summary## Model Validation MedianTDAUROC Time
## 1 ENET Test 0.7726240 12
## 2 ENET Test 0.7457409 24
## 3 ENET Test 0.6343841 36
## 4 ENET Test 0.6183024 48
## 5 ENET Test 0.6556561 60
## 6 ENET Test 0.6644110 72
## 7 ENET Test 0.6299208 84
## 8 ENET Test 0.6980839 96
## 9 ENET Test 0.4185411 108
## 10 ENET Test 0.3500000 120plot(ENET_Train_CoxPH_Model_ExternalValidation,
ylim = c(0, 1))## 12 24 36 48 60 72 84
## AUC 0.772624 0.7457409 0.6343841 0.6183024 0.6556561 0.664411 0.6299208
## 96 108 120
## AUC 0.6980839 0.4185411 0.35
##################################
# Conducting an internal calibration
# of the ENET model
# using a 50-cycle bootstrap
# for Year 5 (Time = 60)
##################################
ENET_Train_CoxPH_Model_InternalCalibration <- calibrate(ENET_Train_Predictors,
ENET_Train_Time,
ENET_Train_Status,
model.type = "enet",
alpha = ENET_Train_CoxPH_Fit$alpha,
lambda = ENET_Train_CoxPH_Fit$lambda,
method = "bootstrap",
boot.times = 50,
pred.at = 60,
ngroup = 3,
seed = 88888888)## Start bootstrap sample 1
## Start bootstrap sample 2
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## Start bootstrap sample 50print(ENET_Train_CoxPH_Model_InternalCalibration)## High-Dimensional Cox Model Calibration Object
## Random seed: 88888888
## Calibration method: bootstrap
## Bootstrap samples: 50
## Model type: enet
## glmnet model alpha: 0.2
## glmnet model lambda: 0.2091559
## glmnet model penalty factor: not specified
## Calibration time point: 60
## Number of groups formed for calibration: 3summary(ENET_Train_CoxPH_Model_InternalCalibration)## Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.6337089 0.5227166 0.4811637 0.5678580
## 2 0.7258780 0.7656098 0.7298848 0.8030835
## 3 0.8055644 0.8823147 0.8549938 0.9105085plot(ENET_Train_CoxPH_Model_InternalCalibration)
##################################
# Estimating survival curves
# of the ENET model
# for the risk groups
# using the train data
##################################
ENET_Train_CoxPH_Model_KMPlot <- kmplot( ENET_Train_CoxPH_Model_InternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
##################################
# Conducting an internal calibration
# of the ENET model
# using a 50-cycle bootstrap
# for Year 10 (Time = 120)
##################################
ENET_Train_CoxPH_Model_InternalCalibration <- calibrate(ENET_Train_Predictors,
ENET_Train_Time,
ENET_Train_Status,
model.type = "enet",
alpha = ENET_Train_CoxPH_Fit$alpha,
lambda = ENET_Train_CoxPH_Fit$lambda,
method = "bootstrap",
boot.times = 50,
pred.at = 120,
ngroup = 3,
seed = 88888888)## Start bootstrap sample 1
## Start bootstrap sample 2
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## Start bootstrap sample 48
## Start bootstrap sample 49
## Start bootstrap sample 50print(ENET_Train_CoxPH_Model_InternalCalibration)## High-Dimensional Cox Model Calibration Object
## Random seed: 88888888
## Calibration method: bootstrap
## Bootstrap samples: 50
## Model type: enet
## glmnet model alpha: 0.2
## glmnet model lambda: 0.2091559
## glmnet model penalty factor: not specified
## Calibration time point: 120
## Number of groups formed for calibration: 3summary(ENET_Train_CoxPH_Model_InternalCalibration)## Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.4363653 0.3513336 0.3055778 0.4039408
## 2 0.5583717 0.5350393 0.4737550 0.6042512
## 3 0.6749076 0.7985929 0.7564109 0.8431273plot(ENET_Train_CoxPH_Model_InternalCalibration)
##################################
# Estimating survival curves
# of the ENET model
# for the risk groups
# using the train data
##################################
ENET_Train_CoxPH_Model_KMPlot <- kmplot( ENET_Train_CoxPH_Model_InternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
##################################
# Conducting an external calibration
# of the ENET model
# using the test data
# for Year 5 (Time = 60)
##################################
ENET_Train_CoxPH_Model_ExternalCalibration <- calibrate_external(ENET_Train_CoxPH_Fit,
ENET_Train_Predictors,
ENET_Train_Time,
ENET_Train_Status,
ENET_Test_Predictors,
ENET_Test_Time,
ENET_Test_Status,
pred.at = 60,
ngroup = 3)
print(ENET_Train_CoxPH_Model_ExternalCalibration)## High-Dimensional Cox Model External Calibration Object
## Model type: enet
## Calibration time point: 60
## Number of groups formed for calibration: 3summary(ENET_Train_CoxPH_Model_ExternalCalibration)## External Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.6333423 0.5449845 0.4657763 0.6376625
## 2 0.7270443 0.6847920 0.6092030 0.7697599
## 3 0.8048570 0.8889017 0.8372582 0.9437308plot(ENET_Train_CoxPH_Model_ExternalCalibration)
##################################
# Estimating survival curves
# of the ENET model
# for the risk groups
# using the test data
##################################
ENET_Test_CoxPH_Model_KMPlot <- kmplot( ENET_Train_CoxPH_Model_ExternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
##################################
# Conducting an external calibration
# of the ENET model
# using the test data
# for Year 10 (Time = 120)
##################################
ENET_Train_CoxPH_Model_ExternalCalibration <- calibrate_external(ENET_Train_CoxPH_Fit,
ENET_Train_Predictors,
ENET_Train_Time,
ENET_Train_Status,
ENET_Test_Predictors,
ENET_Test_Time,
ENET_Test_Status,
pred.at = 120,
ngroup = 3)
print(ENET_Train_CoxPH_Model_ExternalCalibration)## High-Dimensional Cox Model External Calibration Object
## Model type: enet
## Calibration time point: 120
## Number of groups formed for calibration: 3summary(ENET_Train_CoxPH_Model_ExternalCalibration)## External Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.4309757 0.4249822 0.3421227 0.5279097
## 2 0.5557502 0.5749567 0.4873554 0.6783041
## 3 0.6702783 0.7927570 0.6936271 0.9060539plot(ENET_Train_CoxPH_Model_ExternalCalibration)
##################################
# Estimating survival curves
# of the ENET model
# for the risk groups
# using the test data
##################################
ENET_Test_CoxPH_Model_KMPlot <- kmplot( ENET_Train_CoxPH_Model_ExternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
1.5.3 Minimax Concave Penalty (MCP)
Minimax Concave Penalty is a regularization technique aimed at encouraging sparsity in the model by imposing folded concave penalty functions on the regression coefficients. The algorithm uses the tuning parameter lambda which controls the amount of penalty applied, and a shape parameter gamma which determines the concavity of the penalty function. The method formulates an optimization problem that minimizes the negative log-partial likelihood of the Cox proportional hazards model subject to the penalty terms. The optimization problem is solved using coordinate descent, by iteratively updating regression coefficients, while incorporating the MCP penalty. The MCP penalty retains the penalization rate (and bias) of the LASSO for small coefficients, but immedciately relaxes the rate of penalization as the absolute value of the coefficient increases. This approach can be useful for feature selection, allowing for the identification of a parsimonious set of predictors that significantly impact survival outcomes, while balancing the trade-off between model complexity and prediction.
[A] The multivariate Cox proportional hazards regression model from the survival package was implemented with the MCP penalty function applied using the hdnom, penalized and ncvreg packages.
[B] The method contains 2 hyperparameters:
[B.1] nfolds = fold numbers of cross-validation fixed at a value = 5
[B.2] gammas = MCP penalty made to vary across a range of values equal to 2.10 to 5.00
[C] The method selected 13 out of the 25 predictors for the final Cox Proportional Hazards model as follows:
[C.1] age variable (numeric)
[C.2] betablok variable (factor)
[C.3] acei variable (factor)
[C.4] aspirin variable (factor)
[C.5] insulin variable (factor)
[C.6] resting.systolic.bp variable (numeric)
[C.7] resting.hr variable (numeric)
[C.8] lvef.metabl variable (numeric)
[C.9] peak.vo2 variable (numeric)
[C.10] interval variable (numeric)
[C.11] cad variable (factor)
[C.12] bun variable (numeric)
[C.13] male variable (factor)
[D] Optimal model parameters were determined as:
[D.1] Lambda = 0.014
[D.2] Gamma = 4.000
[E] Internally validated Time-Dependent AUROC metrics using bootstrap resampling of the train data showed good performance overall except for the last few time periods and are summarized as follows:
[E.1] 12 months = 0.76617
[E.2] 24 months = 0.77818
[E.3] 36 months = 0.68530
[E.4] 48 months = 0.67992
[E.5] 60 months = 0.66145
[E.6] 72 months = 0.67633
[E.7] 84 months = 0.68757
[E.8] 96 months = 0.61512
[E.9] 108 months = 0.62571
[E.10] 120 months = 0.26660
[F] Externally validated Time-Dependent AUROC metrics using the test data showed good performance overall except for the last few time periods and are summarized as follows:
[F.1] 12 months = 0.80360
[F.2] 24 months = 0.74632
[F.3] 36 months = 0.64447
[F.4] 48 months = 0.60192
[F.5] 60 months = 0.64827
[F.6] 72 months = 0.60107
[F.7] 84 months = 0.56609
[F.8] 96 months = 0.65251
[F.9] 108 months = 0.46980
[F.10] 120 months = 0.27500
[G] Model predictions at time = 60 months were reasonably calibrated for both the train and test data. Kaplan-Meier survival curves for the three risk groups from the calibration data were differentially profiled with p-value < 0.05.
[H] Model predictions at time = 120 months were reasonably calibrated for both the train and test data. Kaplan-Meier survival curves for the three risk groups from the calibration data were differentially profiled with p-value < 0.05.
Code Chunk | Output
##################################
# Loading the preprocessed data
##################################
PMA_PreModelling_MCP <- peakVO2.Source[,!names(peakVO2.Source) %in% rownames(UF_CoxPHModelSummary_NonSig)]
##################################
# Setting the appropriate variable types
##################################
PMA_PreModelling_MCP$died <- as.factor(PMA_PreModelling_MCP$died)
##################################
# Creating a local object
# for the train and test sets
##################################
set.seed(88888888)
TrainIndex <- createDataPartition(PMA_PreModelling_MCP$died,
p = 0.8,
list = FALSE,
times = 1)
PMA_PreModelling_MCP_Train <- PMA_PreModelling_MCP[ TrainIndex,]
PMA_PreModelling_MCP_Test <- PMA_PreModelling_MCP[-TrainIndex,]
PMA_PreModelling_MCP_Train$died <- as.numeric(PMA_PreModelling_MCP_Train$died)
PMA_PreModelling_MCP_Test$died <- as.numeric(PMA_PreModelling_MCP_Test$died)
##################################
# Formulating the model components
# for the train data
##################################
MCP_Train_Predictors <- as.matrix(
PMA_PreModelling_MCP_Train[,!names(PMA_PreModelling_MCP_Train)
%in% c("ttodead","died")])
MCP_Train_Time <- PMA_PreModelling_MCP_Train$ttodead
MCP_Train_Status <- PMA_PreModelling_MCP_Train$died
MCP_Train_Response <- survival::Surv(MCP_Train_Time, MCP_Train_Status)
##################################
# Fitting a MCP model
##################################
MCP_Train_CoxPH_Fit <- fit_mcp(MCP_Train_Predictors,
MCP_Train_Response,
nfolds = 5,
gammas = c(2.1, 3, 4, 5),
seed = 88888888,
trace = FALSE)
##################################
# Extracting the model objects and
# optimal hyperparameters
# from the MCP model
##################################
(MCP_Train_CoxPH_Model <- MCP_Train_CoxPH_Fit$model)## $beta
## 0.0137
## age 0.002448205
## betablok -0.379260896
## acei 0.038713889
## angioten.II 0.000000000
## anti.arrhy 0.000000000
## anti.coag 0.000000000
## aspirin -0.210588087
## vasodilator 0.000000000
## diuretic.loop 0.000000000
## diuretic.thiazide 0.000000000
## insulin 0.077586207
## surgery.pacemaker 0.000000000
## surgery.cabg 0.000000000
## resting.systolic.bp -0.001705225
## resting.hr 0.002940415
## lvef.metabl -0.011928129
## peak.vo2 -0.063746539
## interval -0.001120881
## cad 0.102984692
## bun 0.017095396
## sodium 0.000000000
## hgb 0.000000000
## glucose 0.000000000
## male 0.521276288
## crcl 0.000000000
##
## $iter
## [1] 43
##
## $lambda
## [1] 0.01374311
##
## $penalty
## [1] "MCP"
##
## $gamma
## [1] 4
##
## $alpha
## [1] 1
##
## $convex.min
## NULL
##
## $loss
## [1] 3934.129
##
## $penalty.factor
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##
## $n
## [1] 1785
##
## $time
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2
## [19] 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3
## [37] 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4
## [55] 4 4 4 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6
## [73] 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 8 8
## [91] 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9
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## [127] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
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## [325] 22 22 22 22 22 23 23 23 23 23 23 23 23 23 23 23 23 23
## [343] 23 23 23 23 23 23 24 24 24 24 24 24 24 24 25 25 25 25
## [361] 25 25 25 25 25 25 25 25 25 25 25 26 26 26 26 26 26 26
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## [415] 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29
## [433] 29 29 29 29 29 29 30 30 30 30 30 30 30 30 30 30 30 30
## [451] 30 30 30 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
## [469] 31 31 31 31 31 31 32 32 32 32 32 32 32 32 32 32 32 32
## [487] 32 32 32 32 32 32 33 33 33 33 33 33 33 33 33 33 33 34
## [505] 34 34 34 34 34 34 34 34 34 35 35 35 35 35 35 35 35 35
## [523] 35 35 35 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36
## [541] 36 36 36 36 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [559] 37 38 38 38 38 38 38 38 38 38 38 39 39 39 39 39 39 39
## [577] 39 39 39 39 39 39 39 39 39 39 39 40 40 40 40 40 40 40
## [595] 40 40 40 40 40 40 40 40 40 40 40 41 41 41 41 41 41 41
## [613] 41 41 41 41 41 42 42 42 42 42 42 42 42 42 42 42 42 42
## [631] 42 42 42 43 43 43 43 43 43 43 43 43 43 43 43 43 43 44
## [649] 44 44 44 44 44 44 44 45 45 45 45 45 45 45 45 45 45 45
## [667] 45 46 46 46 46 46 46 46 46 46 46 46 46 46 46 47 47 47
## [685] 47 47 47 47 47 47 48 48 48 48 48 48 48 48 48 48 48 48
## [703] 49 49 49 49 49 49 49 49 49 49 49 49 49 50 50 50 50 50
## [721] 50 50 50 50 50 50 50 51 51 51 51 51 51 51 51 51 51 51
## [739] 51 51 51 52 52 52 52 52 52 52 52 52 52 52 52 52 52 53
## [757] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 54 54
## [775] 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 55 55
## [793] 55 55 55 55 55 55 55 55 55 55 55 55 55 55 56 56 56 56
## [811] 56 56 56 56 56 56 56 56 56 57 57 57 57 57 57 57 57 57
## [829] 57 57 57 57 57 57 57 57 58 58 58 58 58 58 58 58 58 58
## [847] 58 58 58 58 58 58 58 59 59 59 59 59 59 59 59 59 59 59
## [865] 59 59 59 59 59 59 59 59 59 59 59 60 60 60 60 60 60 60
## [883] 60 60 60 60 61 61 61 61 61 61 61 61 61 61 61 62 62 62
## [901] 62 62 62 62 62 62 62 62 62 62 62 62 63 63 63 63 63 63
## [919] 63 63 63 63 63 63 63 63 63 63 64 64 64 64 64 64 64 64
## [937] 64 64 64 64 64 64 65 65 65 65 65 65 65 65 65 65 65 65
## [955] 65 65 65 65 65 65 65 66 66 66 66 66 66 66 66 66 66 67
## [973] 67 67 67 67 67 67 67 67 68 68 68 68 68 68 68 68 68 68
## [991] 68 68 68 68 68 69 69 69 69 69 69 69 69 69 69 69 70 70
## [1009] 70 70 70 70 70 71 71 71 71 71 71 71 71 71 71 72 72 72
## [1027] 72 72 72 72 72 72 72 72 72 73 73 73 73 73 73 73 73 74
## [1045] 74 74 74 74 74 74 74 74 74 74 75 75 75 75 75 75 75 75
## [1063] 75 75 75 75 75 76 76 76 76 76 76 76 76 76 76 76 76 76
## [1081] 76 76 76 76 76 77 77 77 77 77 77 77 77 77 77 77 77 77
## [1099] 77 77 78 78 78 78 78 78 78 78 78 78 78 78 79 79 79 79
## [1117] 79 79 79 79 79 79 79 79 79 80 80 80 80 80 80 80 80 80
## [1135] 81 81 81 81 81 81 81 81 81 81 81 82 82 82 82 82 82 82
## [1153] 82 82 82 82 82 82 82 82 83 83 83 83 83 83 83 83 83 83
## [1171] 83 83 83 83 83 84 84 84 84 84 84 84 84 84 84 84 84 84
## [1189] 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 86 86 86
## [1207] 86 86 86 86 86 86 86 86 86 86 86 86 87 87 87 87 87 87
## [1225] 87 87 87 87 87 87 87 88 88 88 88 88 88 88 88 88 88 88
## [1243] 88 88 88 88 89 89 89 89 89 89 89 89 89 89 89 89 89 89
## [1261] 89 89 89 89 89 90 90 90 90 90 90 90 90 90 90 90 90 90
## [1279] 90 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91
## [1297] 92 92 92 92 92 92 92 92 92 92 92 92 92 92 92 92 92 93
## [1315] 93 93 93 93 93 93 93 93 93 94 94 94 94 94 94 94 94 94
## [1333] 94 94 94 94 94 94 94 94 94 95 95 95 95 95 95 95 95 95
## [1351] 95 95 95 95 96 96 96 96 96 96 96 96 96 96 96 96 96 96
## [1369] 96 96 96 96 96 96 96 96 97 97 97 97 97 97 97 97 97 97
## [1387] 97 97 97 97 97 97 97 97 97 98 98 98 98 98 98 98 98 98
## [1405] 98 98 98 98 98 98 98 98 98 98 98 99 99 99 99 99 99 100
## [1423] 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
## [1441] 100 101 101 101 101 101 101 101 101 101 101 101 101 101 101 102 102 102
## [1459] 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 103
## [1477] 103 103 103 103 103 103 103 103 104 104 104 104 104 104 104 104 104 104
## [1495] 104 104 104 104 104 104 105 105 105 105 105 105 105 105 105 105 106 106
## [1513] 106 106 106 106 106 106 106 106 106 106 106 106 106 107 107 107 107 107
## [1531] 107 107 107 107 107 107 107 108 108 108 108 108 108 108 108 108 108 108
## [1549] 108 108 108 109 109 109 109 109 109 109 109 109 109 109 109 109 109 110
## [1567] 110 110 110 110 110 110 110 110 110 110 111 111 111 111 111 111 111 111
## [1585] 112 112 112 112 112 112 112 113 113 113 113 113 113 113 113 113 113 113
## [1603] 113 113 113 113 113 114 114 114 114 114 114 114 114 114 114 114 114 114
## [1621] 114 115 115 115 115 115 115 115 115 115 115 115 115 115 115 115 115 115
## [1639] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1657] 117 117 117 117 117 117 117 117 117 117 117 117 117 117 118 118 118 118
## [1675] 119 119 119 119 119 119 119 119 119 119 119 119 119 119 120 120 120 120
## [1693] 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 121 121 121
## [1711] 121 121 121 121 121 121 121 121 122 122 122 122 122 122 122 122 122 122
## [1729] 122 122 122 123 123 123 123 123 123 123 123 124 124 124 124 124 124 124
## [1747] 124 124 125 125 125 125 125 125 125 125 125 125 125 125 125 126 126 126
## [1765] 126 126 126 126 126 126 126 126 126 126 126 127 127 127 127 127 127 127
## [1783] 127 127 127
##
## $fail
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 0 0
## [149] 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0
## [186] 0 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 1 0 0 1 0
## [223] 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1
## [260] 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1
## [297] 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 0 0 0 0 1 0 1 1
## [334] 1 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 1 1 1 1 0 0 1 0 0 0 0
## [371] 0 0 1 0 1 1 1 1 1 1 0 1 1 0 1 0 0 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 1 0 0 1 1
## [408] 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1
## [445] 1 1 1 1 0 0 0 0 0 0 1 0 1 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1
## [482] 1 1 0 1 0 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 1 0 0 0 1 1 0 1 1
## [519] 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 0 0 0
## [556] 0 0 0 0 1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 0 1 1 1 0 0 1 0 0 0 0 0 1 0 1 1 1
## [593] 1 1 1 1 0 0 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 0 0 1 0 1 1 1 0
## [630] 0 0 0 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 1 0 0 0 0 0 0 0
## [667] 1 1 0 0 1 1 1 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 1 1 1 0 1 1 1 0 0 0 0 0
## [704] 1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 1 1 1 0 0 1 0 1 0 0 0 0 0
## [741] 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
## [778] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 1 1 1 0 0 0 0 0 0 1 0 1 1 0 0 0 0
## [815] 0 0 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 0 1 0 0 0 0
## [852] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 1 1 0 0 0 1 1
## [889] 0 1 1 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 0 0 0
## [926] 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 1 1 1 0 0 0 0 0 0 0 1
## [963] 1 0 1 1 0 1 1 0 0 1 0 0 1 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
## [1000] 1 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0
## [1037] 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0
## [1074] 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 0
## [1111] 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## [1148] 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0
## [1185] 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1
## [1222] 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0
## [1259] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0
## [1296] 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0
## [1333] 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 0
## [1370] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## [1407] 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0
## [1444] 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1481] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1518] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1555] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## [1592] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1629] 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1666] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1703] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1740] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1777] 0 0 0 0 0 0 0 0 0
##
## $order
## [1] 198 260 499 699 873 1130 1248 1278 1370 1416 1478 1550 1657 89
## [15] 93 267 387 571 580 585 635 708 965 1258 1308 1433 1695 1702
## [29] 3 148 262 430 500 562 639 861 991 1056 1342 1378 1388 1681
## [43] 1724 1743 1757 357 614 634 716 718 744 926 931 1148 1332 1603
## [57] 1626 147 302 629 763 808 888 1010 1144 1593 70 94 183 411
## [71] 464 513 842 964 984 1139 1158 1162 1456 1742 158 369 393 406
## [85] 503 695 701 1769 123 133 259 320 392 441 515 694 1266 1597
## [99] 1630 9 190 291 332 581 772 773 907 908 946 947 1352 1650
## [113] 1763 30 318 355 424 512 572 902 1075 1282 1420 7 177 182
## [127] 256 276 283 368 456 610 625 626 702 819 850 915 1008 1093
## [141] 1243 1526 1748 1754 1759 57 193 281 354 378 381 419 561 1045
## [155] 1218 1303 1393 1634 1648 1744 1746 1750 1751 1752 1753 6 49 132
## [169] 249 266 432 485 502 642 715 1089 1146 1214 1287 1740 1741 1745
## [183] 1747 1749 137 202 233 394 426 480 486 525 769 849 1153 1226
## [197] 1249 1343 1527 1615 1689 1704 1736 1738 1739 1758 1760 1761 1 37
## [211] 83 175 188 413 505 1222 1383 1502 1717 1718 135 217 218 238
## [225] 277 557 591 730 1040 1076 1564 1582 1684 1726 1731 1737 1755 189
## [239] 293 327 637 1481 1503 1611 1613 1640 1661 1706 1728 1729 1730 1732
## [253] 1734 1735 1756 126 142 296 544 791 866 878 1576 1708 1723 1725
## [267] 1733 178 541 768 954 1208 1224 1225 1242 1254 1614 1674 1696 1719
## [281] 1721 1727 1785 90 139 174 308 543 1023 1298 1429 1715 1720 1722
## [295] 78 79 167 203 269 288 326 1150 1371 1399 1435 1693 1701 1707
## [309] 1709 1710 1711 1713 1714 1716 1774 50 209 384 389 477 656 1119
## [323] 1179 1321 1505 1699 1705 1712 1768 124 364 367 382 395 749 922
## [337] 972 1020 1046 1256 1330 1419 1688 1691 1697 1698 1700 1703 36 141
## [351] 250 1052 1497 1662 1692 1694 248 270 412 472 721 820 997 1364
## [365] 1612 1667 1683 1685 1686 1687 1690 31 75 187 289 360 492 745
## [379] 770 823 851 895 993 1609 1649 1658 1677 1679 1682 1762 152 313
## [393] 579 1019 1079 1181 1636 1672 1676 1678 1680 1764 55 244 830 834
## [407] 999 1255 1384 1596 1605 1668 1673 1675 66 221 298 334 380 511
## [421] 531 1110 1216 1395 1543 1580 1583 1617 1637 1651 1659 1660 1663 1665
## [435] 1666 1669 1670 1671 58 86 329 344 423 666 846 932 942 1482
## [449] 1624 1652 1653 1656 1664 60 212 258 351 422 600 870 904 1136
## [463] 1295 1440 1577 1589 1610 1639 1641 1642 1643 1654 1655 1770 29 51
## [477] 80 88 136 165 652 913 917 1299 1447 1483 1551 1635 1644 1645
## [491] 1646 1775 241 272 300 436 692 777 970 1044 1206 1585 1647 309
## [505] 346 670 685 733 1142 1197 1470 1627 1638 299 303 304 407 576
## [519] 654 1316 1369 1404 1491 1518 1601 23 27 72 255 366 374 689
## [533] 725 807 893 1051 1300 1409 1619 1620 1628 1629 1633 1781 127 421
## [547] 463 474 475 644 1060 1192 1588 1621 1625 1631 1632 1778 1783 484
## [561] 554 727 795 950 975 1514 1595 1622 1623 77 159 275 567 1097
## [575] 1159 1189 1201 1337 1353 1366 1368 1423 1590 1616 1618 1772 1776 106
## [589] 129 376 496 821 1095 1215 1304 1360 1363 1367 1376 1488 1555 1572
## [603] 1773 1782 1784 204 648 728 865 949 1121 1194 1220 1608 1777 1779
## [617] 1780 19 85 115 179 222 287 343 440 767 797 1116 1462 1600
## [631] 1602 1607 1771 87 116 144 330 410 551 811 1098 1340 1546 1574
## [645] 1598 1604 1606 2 112 125 546 613 675 956 1477 5 44 280
## [659] 680 1579 1584 1586 1587 1591 1592 1599 1766 196 252 447 448 939
## [673] 952 1024 1171 1240 1348 1438 1498 1578 1581 102 569 664 1397 1487
## [687] 1567 1568 1573 1575 46 153 987 1014 1281 1284 1403 1467 1556 1569
## [701] 1570 1571 337 1127 1227 1320 1544 1554 1557 1558 1560 1561 1562 1565
## [715] 1566 160 396 587 617 852 1236 1537 1541 1552 1553 1559 1563 18
## [729] 105 264 286 307 1261 1412 1425 1523 1539 1540 1545 1547 1549 311
## [743] 437 859 1151 1273 1292 1525 1528 1534 1535 1536 1538 1542 1548 59
## [757] 145 400 443 457 620 762 1275 1436 1490 1499 1506 1520 1530 1531
## [771] 1532 1533 92 207 263 653 1101 1301 1317 1354 1492 1504 1510 1513
## [785] 1515 1519 1521 1522 1524 1529 65 166 268 359 361 478 597 650
## [799] 671 856 948 1461 1471 1512 1516 1517 22 169 528 709 934 1170
## [813] 1228 1362 1489 1507 1508 1509 1511 76 254 349 473 490 615 804
## [827] 862 1391 1427 1453 1465 1494 1495 1496 1500 1501 64 206 253 279
## [841] 372 470 536 668 1154 1289 1297 1414 1464 1474 1485 1486 1493 39
## [855] 41 42 199 282 319 687 784 953 1133 1441 1463 1466 1468 1469
## [869] 1472 1473 1476 1479 1480 1484 1767 162 297 565 737 978 1183 1195
## [883] 1267 1455 1459 1475 38 52 323 647 906 1269 1446 1448 1457 1458
## [897] 1460 143 227 251 684 806 1069 1408 1434 1443 1449 1450 1451 1452
## [911] 1454 1594 101 128 130 211 216 618 778 1229 1341 1413 1431 1437
## [925] 1439 1442 1444 1445 34 228 352 455 847 887 1204 1379 1380 1417
## [939] 1422 1428 1430 1432 24 181 292 491 560 627 1081 1112 1115 1141
## [953] 1163 1335 1381 1401 1411 1421 1424 1426 1765 62 242 339 350 693
## [967] 855 986 1143 1415 1418 53 173 314 461 750 871 1109 1389 1405
## [981] 54 229 290 483 1328 1359 1377 1382 1385 1394 1400 1402 1406 1407
## [995] 1410 69 95 416 628 1108 1213 1351 1390 1392 1396 1398 197 465
## [1009] 814 928 1016 1386 1387 96 121 317 1346 1356 1358 1372 1373 1374
## [1023] 1375 138 476 578 645 864 1085 1099 1221 1355 1357 1361 1365 186
## [1037] 1259 1322 1336 1338 1347 1349 1350 549 624 738 828 1032 1291 1329
## [1051] 1331 1339 1344 1345 305 732 748 822 1080 1279 1306 1312 1319 1326
## [1065] 1327 1333 1334 161 184 208 239 246 428 690 766 780 1065 1210
## [1079] 1251 1309 1314 1318 1323 1324 1325 16 220 225 235 449 683 1211
## [1093] 1286 1290 1305 1307 1310 1311 1313 1315 35 100 200 548 812 898
## [1107] 990 1113 1191 1294 1296 1302 171 438 458 459 921 985 1096 1120
## [1121] 1253 1262 1285 1288 1293 1031 1084 1233 1264 1268 1276 1277 1280 1283
## [1135] 113 114 163 1132 1217 1257 1265 1270 1271 1272 1274 14 316 383
## [1149] 429 454 537 633 741 891 955 1114 1219 1250 1260 1263 71 156
## [1163] 224 348 391 857 1176 1234 1235 1237 1239 1245 1246 1247 1252 98
## [1177] 417 471 482 514 612 623 677 1184 1202 1238 1241 1244 20 340
## [1191] 353 506 539 1137 1177 1190 1196 1203 1212 1223 1230 1231 1232 21
## [1205] 110 333 445 712 1072 1094 1104 1168 1169 1180 1199 1205 1207 1209
## [1219] 56 325 586 810 973 1106 1107 1186 1187 1188 1193 1198 1200 45
## [1233] 214 322 342 388 839 1165 1166 1167 1173 1174 1175 1178 1182 1185
## [1247] 13 149 237 510 540 883 892 1105 1118 1128 1149 1152 1155 1156
## [1261] 1157 1160 1161 1164 1172 867 875 992 1007 1013 1037 1100 1102 1134
## [1275] 1135 1138 1140 1145 1147 26 74 104 582 824 1035 1091 1092 1103
## [1289] 1117 1122 1123 1124 1125 1126 1129 1131 63 168 379 901 903 920
## [1303] 1009 1041 1064 1071 1077 1078 1083 1087 1088 1090 1111 774 1018 1048
## [1317] 1059 1067 1068 1070 1073 1082 1086 150 210 274 450 698 829 854
## [1331] 858 941 977 1030 1049 1050 1055 1058 1061 1066 1074 245 945 981
## [1345] 1004 1029 1042 1043 1047 1053 1054 1057 1062 1063 48 120 134 306
## [1359] 592 598 646 775 869 909 937 1005 1021 1022 1025 1027 1028 1033
## [1373] 1034 1036 1038 1039 285 321 754 788 943 969 982 994 996 1000
## [1387] 1001 1002 1003 1006 1011 1012 1015 1017 1026 192 427 550 756 800
## [1401] 899 957 960 961 966 968 974 976 979 980 983 988 989 995
## [1415] 998 232 345 446 959 963 967 119 271 371 386 415 495 498
## [1429] 573 608 676 740 885 916 927 935 936 944 951 958 962 109
## [1443] 172 377 466 467 641 838 848 900 912 930 933 938 940 122
## [1457] 131 261 705 760 836 860 880 889 894 896 910 911 914 918
## [1471] 919 923 924 925 929 43 191 201 460 827 882 884 897 905
## [1485] 151 240 278 295 328 802 843 872 874 876 877 879 881 886
## [1499] 890 971 25 294 468 764 789 825 837 845 863 868 284 315
## [1513] 661 710 759 782 813 826 831 832 833 835 840 841 844 68
## [1527] 118 385 402 425 493 752 755 785 816 817 818 338 401 451
## [1541] 479 696 783 786 792 798 799 803 805 809 815 347 373 375
## [1555] 604 621 765 771 781 787 790 793 794 796 801 47 155 213
## [1569] 336 439 469 530 711 761 776 779 108 236 662 743 751 753
## [1583] 757 758 370 552 735 739 742 746 747 170 195 226 442 713
## [1597] 714 720 722 723 724 726 729 731 734 736 853 146 194 231
## [1611] 301 444 589 640 657 703 704 706 707 717 719 12 40 61
## [1625] 180 215 257 420 453 529 566 583 609 672 688 691 697 700
## [1639] 17 84 107 403 622 638 655 660 663 667 669 673 674 678
## [1653] 679 681 682 686 15 33 73 81 103 157 234 324 588 616
## [1667] 643 658 659 665 331 631 649 651 32 82 230 265 312 335
## [1681] 418 431 602 603 611 630 632 636 28 140 185 219 310 362
## [1695] 452 524 574 590 593 594 595 596 599 601 605 606 607 91
## [1709] 99 111 117 223 243 435 489 533 575 584 8 10 358 390
## [1723] 519 521 527 547 555 564 568 570 577 67 247 408 553 556
## [1737] 558 559 563 11 341 356 397 405 433 520 534 545 154 176
## [1751] 399 404 414 504 518 523 526 532 535 538 542 164 205 363
## [1765] 365 409 434 497 501 507 508 509 516 517 522 4 97 273
## [1779] 398 462 481 487 488 494 619
##
## $Eta
## [,1]
## [1,] -0.7134369938
## [2,] 0.7983499117
## [3,] -1.5053445502
## [4,] -0.7320358293
## [5,] -0.0070999164
## [6,] -0.5196550755
## [7,] -0.1314227219
## [8,] -1.6899793109
## [9,] 0.4108817486
## [10,] -1.7671486357
## [11,] 0.3617339362
## [12,] -1.5993816748
## [13,] -0.5964373983
## [14,] 0.2421506096
## [15,] -0.8491593646
## [16,] 0.4271179980
## [17,] 0.0278233551
## [18,] -1.2315141083
## [19,] -0.2361686026
## [20,] 0.3401576175
## [21,] 0.4777440633
## [22,] -0.4115164829
## [23,] 1.3706052995
## [24,] -0.1428740142
## [25,] -0.4741780686
## [26,] -0.0597362330
## [27,] -0.3841731340
## [28,] -0.5752347668
## [29,] -0.3099900466
## [30,] -1.1480922244
## [31,] -0.3150370936
## [32,] -0.5267108018
## [33,] 0.0878698701
## [34,] 0.0136983688
## [35,] -0.0073011605
## [36,] -0.0127425309
## [37,] 1.2062226770
## [38,] 0.1893530934
## [39,] 0.4812568530
## [40,] 0.3676292795
## [41,] -0.2929517546
## [42,] -0.3044013767
## [43,] -1.4617835884
## [44,] -0.1163426681
## [45,] 0.4821233342
## [46,] -0.4485791063
## [47,] -0.2036683201
## [48,] -0.9826724366
## [49,] -1.4138487407
## [50,] -0.5136211554
## [51,] -0.6991805004
## [52,] -1.2360960958
## [53,] 0.1518115625
## [54,] 0.6506645703
## [55,] -0.9655243929
## [56,] -0.4506202473
## [57,] -0.1300201677
## [58,] -0.4601290141
## [59,] -0.8600112876
## [60,] 0.2519215110
## [61,] 0.2298845437
## [62,] -0.0288801218
## [63,] 0.8957012459
## [64,] -0.2088005334
## [65,] -1.0529775745
## [66,] -1.0294851504
## [67,] -0.6548826118
## [68,] 0.5571637425
## [69,] -0.0760780137
## [70,] -0.7343399421
## [71,] 0.8785145556
## [72,] -0.6971801957
## [73,] -0.0410584474
## [74,] -0.7101688223
## [75,] -0.4979946179
## [76,] -0.3080610311
## [77,] -0.8164789739
## [78,] -0.4235635444
## [79,] -1.2631728822
## [80,] 0.3456827389
## [81,] 0.0128823711
## [82,] -0.1880365559
## [83,] -0.6927309182
## [84,] -0.0780408329
## [85,] -1.6910306212
## [86,] 0.1513686904
## [87,] -0.8595097887
## [88,] -0.0375993623
## [89,] 0.9660440237
## [90,] 0.0216869734
## [91,] 0.1971374025
## [92,] -0.6814741185
## [93,] -2.9324796014
## [94,] 0.3642494752
## [95,] 0.2771925863
## [96,] 0.0264574541
## [97,] -1.2691443895
## [98,] -0.8831902628
## [99,] -0.3881173723
## [100,] -0.8529891989
## [101,] 0.0193481325
## [102,] 0.2426295153
## [103,] -0.1902416642
## [104,] 0.0512208652
## [105,] 0.2653060475
## [106,] 0.1199420867
## [107,] -1.3044836993
## [108,] -0.9130625575
## [109,] -0.4009703771
## [110,] -0.3888197713
## [111,] -1.9562720843
## [112,] -0.3938092256
## [113,] -0.1610473808
## [114,] -2.1279062994
## [115,] -1.1593808477
## [116,] -0.0248619991
## [117,] -0.8271720159
## [118,] 0.4973908810
## [119,] -0.8224095091
## [120,] -0.6839147243
## [121,] -1.6868349394
## [122,] -1.6960868809
## [123,] -0.9442204360
## [124,] -0.7130394269
## [125,] -0.9715812605
## [126,] -2.0926057438
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##
## $X
## age betablok acei angioten.II anti.arrhy anti.coag
## 249 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 318 -0.02486626 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 631 0.69686174 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 883 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1095 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1405 1.32837374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1554 0.24578174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1589 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1708 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1773 -1.73897025 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1850 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1935 -1.91940225 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2077 0.78707774 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 110 0.60664574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 116 -3.27264225 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 328 0.15556574 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 482 0.42621374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 723 -1.10745826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 732 0.51642974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 737 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 802 0.24578174 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 894 1.23815774 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1204 1.23815774 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1564 1.32837374 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1632 0.51642974 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1794 0.60664574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2120 -2.73134625 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2127 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 3 2.23053373 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 188 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 320 0.33599774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 539 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 632 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 712 0.60664574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 808 1.41858974 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1081 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1232 -0.65637826 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1313 -0.20529826 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1674 0.42621374 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1717 -1.10745826 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1727 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2104 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2154 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2181 0.60664574 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2197 0.87729374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 445 -2.19005025 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 777 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 800 2.32074973 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 904 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 906 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 933 1.41858974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1155 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1162 2.50118173 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1424 0.96750974 -1.3440996 0.5538366 2.5619444 -0.5322119 -0.8193585
## 1664 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2000 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2035 1.77945374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 186 0.24578174 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 375 0.51642974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 792 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 957 1.50880574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1014 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1112 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1253 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1420 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1988 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 88 0.69686174 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 117 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 229 -2.55091425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 512 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 583 -0.29551426 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 648 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1058 1.23815774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1203 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1225 0.60664574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1415 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1438 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1442 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1824 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2178 0.24578174 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 200 0.69686174 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 460 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 490 -0.38573026 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 505 0.78707774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 636 1.32837374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 877 0.96750974 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 885 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2214 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 157 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 170 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 317 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 399 -0.65637826 -1.3440996 -1.8055867 2.5619444 1.8789510 1.2204670
## 489 1.50880574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 552 1.59902174 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 652 -0.20529826 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 876 -0.47594626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1573 1.14794174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1993 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2040 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 10 -0.83681026 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 238 0.42621374 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 360 0.69686174 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 416 1.86966973 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 733 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 967 -0.29551426 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 968 1.32837374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1134 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1135 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1183 -1.37810625 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1184 -0.29551426 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1686 -1.91940225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2069 0.78707774 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 2204 -1.82918625 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 38 0.33599774 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 392 -0.56616226 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 443 0.51642974 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 532 -0.92702626 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 647 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 724 -1.91940225 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1128 0.87729374 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1337 0.06534974 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1593 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1777 0.24578174 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 8 1.32837374 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 222 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 228 -1.55853825 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 313 1.41858974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 342 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 350 1.14794174 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 459 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 573 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 771 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 788 0.87729374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 789 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 886 -1.28789025 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1029 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1067 0.87729374 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1143 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1251 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1360 -0.29551426 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1547 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1906 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2187 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2193 1.14794174 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2199 1.14794174 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 74 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 241 -1.55853825 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 348 0.15556574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 442 1.50880574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 470 0.78707774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 474 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 524 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 711 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1299 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1515 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1624 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1736 0.78707774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2044 0.33599774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2067 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2183 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2185 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2189 1.23815774 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2190 1.68923774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2191 -1.10745826 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 2192 -1.37810625 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 7 0.15556574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 62 1.05772574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 169 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 305 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 327 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 542 -1.82918625 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 613 1.77945374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 635 0.60664574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 812 0.24578174 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 903 -0.29551426 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1355 0.60664574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1422 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1509 -1.73897025 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1601 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2175 0.15556574 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 2177 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2184 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2186 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2188 0.96750974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 174 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 253 1.77945374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 286 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 492 0.69686174 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 534 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 607 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 614 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 663 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 964 0.78707774 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1066 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1433 -1.01724226 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1524 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1555 0.78707774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1675 0.87729374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1907 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2021 0.60664574 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 2113 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2129 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2170 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2172 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2173 -0.38573026 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2198 0.24578174 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2200 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2201 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1 1.77945374 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 46 0.96750974 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 103 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 220 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 236 0.06534974 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 514 1.32837374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 638 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1520 0.60664574 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1722 -0.02486626 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1877 0.06534974 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 2145 0.96750974 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 2146 0.69686174 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 172 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 269 0.51642974 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 270 0.42621374 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 291 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 343 -1.82918625 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 707 -1.10745826 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 745 0.60664574 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 919 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1293 0.42621374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1338 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1952 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1975 -1.19767425 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2108 -0.38573026 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 2156 -1.73897025 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2164 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2171 0.96750974 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2195 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 237 1.86966973 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 363 0.60664574 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 411 0.51642974 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 806 0.15556574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1854 0.06534974 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1879 -0.11508226 0.7439925 0.5538366 2.5619444 -0.5322119 -0.8193585
## 2015 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2018 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2056 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2081 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2132 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2158 0.42621374 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2159 0.96750974 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 2163 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2165 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2168 -1.10745826 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2169 2.05010173 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2196 0.42621374 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 161 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 180 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 367 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 685 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 990 1.59902174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1086 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1100 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1969 -0.11508226 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 2135 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2153 0.33599774 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 2155 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2166 0.42621374 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 223 0.15556574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 681 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 963 0.15556574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1192 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1500 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1522 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1523 0.78707774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1545 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1560 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2019 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2095 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2121 -0.83681026 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 2147 0.60664574 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 2151 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2157 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2231 0.96750974 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 112 0.24578174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 176 1.68923774 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 219 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 382 0.87729374 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 684 1.50880574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1268 0.06534974 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1617 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1786 1.05772574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2143 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2148 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2152 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 98 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 99 -0.65637826 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 209 0.69686174 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 254 -0.29551426 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 331 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 357 -0.02486626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 410 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1426 1.23815774 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1709 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1746 1.50880574 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 1796 -0.29551426 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2118 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2126 0.60664574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2134 0.60664574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2136 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2137 -0.29551426 0.7439925 0.5538366 2.5619444 1.8789510 -0.8193585
## 2138 0.69686174 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 2141 0.78707774 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2142 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2144 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2220 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 64 0.69686174 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 261 1.77945374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 478 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 485 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 604 0.42621374 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 828 -0.56616226 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1392 0.87729374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1463 1.05772574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1647 -1.73897025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1881 1.32837374 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 2124 0.42621374 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2131 -1.28789025 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2140 -0.38573026 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2213 -0.74659426 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 159 0.42621374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 454 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 458 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 475 0.87729374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 493 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 939 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1150 -0.02486626 0.7439925 0.5538366 2.5619444 -0.5322119 -0.8193585
## 1213 -0.11508226 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1264 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1300 0.51642974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1562 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1661 1.59902174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1776 0.24578174 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2112 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2115 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2122 0.42621374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2123 -1.10745826 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2125 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2128 -0.83681026 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 45 1.41858974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 179 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 306 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1307 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1872 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2082 -0.29551426 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 2117 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2119 2.50118173 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 304 1.32837374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 334 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 513 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 598 -0.02486626 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 909 0.15556574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1030 1.14794174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1238 0.60664574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1700 0.78707774 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2017 0.78707774 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 2087 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2106 0.87729374 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 2109 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2110 -1.19767425 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2111 -0.83681026 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2114 -1.19767425 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 40 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 95 1.41858974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 234 0.78707774 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 358 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 450 0.51642974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 621 -0.83681026 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 934 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 965 1.14794174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1033 1.41858974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1068 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1120 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1234 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2011 -1.01724226 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2068 1.41858974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2078 -0.11508226 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 2100 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2102 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2105 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2203 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 193 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 387 1.95988573 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 731 1.05772574 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 1262 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1342 -3.00199425 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1465 0.42621374 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2048 -3.18242625 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2093 -0.11508226 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2099 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2101 0.51642974 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 2103 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2205 -1.37810625 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 72 2.32074973 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 299 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1043 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1049 0.24578174 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1240 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1561 -0.29551426 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1723 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1992 -2.00961825 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2005 -0.38573026 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 2088 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2094 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2097 -2.37048225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 84 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 273 0.69686174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 370 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 418 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 473 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 646 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 670 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1381 0.15556574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1512 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1738 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1928 -2.00961825 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1973 1.68923774 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1976 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2024 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2049 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2070 -0.74659426 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2079 -1.55853825 0.7439925 0.5538366 2.5619444 -0.5322119 1.2204670
## 2080 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2083 -2.19005025 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2085 2.14031773 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2086 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2090 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2091 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2092 -0.38573026 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 75 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 106 1.32837374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 413 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 432 2.41096573 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 531 -0.47594626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 839 -0.02486626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1062 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1163 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1177 -2.09983425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1855 -2.19005025 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2033 -2.46069825 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2071 1.77945374 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 2073 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2076 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2084 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 78 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 264 0.69686174 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 315 -0.56616226 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 439 -0.65637826 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 529 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 758 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1092 0.06534974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1130 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1412 -0.29551426 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1614 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1802 -0.74659426 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1970 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1983 0.42621374 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2012 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2053 -1.55853825 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2057 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2058 -2.00961825 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2059 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2074 -0.56616226 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2075 0.33599774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2215 -1.91940225 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 37 1.95988573 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 65 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 100 0.78707774 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 108 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 173 0.87729374 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 207 -0.65637826 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 824 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1141 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1145 -2.28026625 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1618 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1812 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1856 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1937 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2047 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2061 0.60664574 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2062 0.96750974 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2063 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2221 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 295 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 337 -0.02486626 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 372 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 546 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 874 1.23815774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 972 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1210 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1298 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1498 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1979 -0.47594626 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2066 -0.47594626 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 383 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 434 1.23815774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 843 1.41858974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 865 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 922 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1418 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1485 -3.27264225 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1842 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2037 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2052 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 371 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 376 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 377 1.05772574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 506 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 728 0.51642974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 826 -0.92702626 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1642 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1707 -1.28789025 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1751 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1864 1.32837374 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1895 0.96750974 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1998 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 31 1.05772574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 35 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 90 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 312 1.05772574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 457 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 466 2.05010173 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 870 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 913 0.78707774 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1013 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1117 -2.82156225 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1306 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1619 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1756 -0.38573026 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 2026 -0.65637826 0.7439925 0.5538366 2.5619444 -0.5322119 -0.8193585
## 2028 1.41858974 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2038 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2039 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2043 0.06534974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2227 1.05772574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 162 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 528 -0.11508226 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 581 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 600 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 601 1.86966973 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 814 0.42621374 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1318 -1.10745826 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1477 -0.65637826 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1982 1.05772574 -1.3440996 -1.8055867 2.5619444 1.8789510 1.2204670
## 2029 0.15556574 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2034 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2041 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2042 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2224 -0.29551426 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 2229 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 612 1.32837374 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 703 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 916 0.60664574 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 994 0.78707774 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1188 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1216 1.14794174 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1890 -0.02486626 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1991 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2031 1.50880574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2032 -0.65637826 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 97 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 201 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 341 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 717 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1364 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1439 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1474 -2.09983425 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1489 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1669 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1687 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1704 0.06534974 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 1706 -2.55091425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1780 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1984 -0.02486626 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 2022 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2025 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2217 -0.38573026 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2222 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 135 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 166 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 468 -0.02486626 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 626 1.23815774 -1.3440996 0.5538366 2.5619444 -0.5322119 -0.8193585
## 1031 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1362 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1510 0.42621374 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1625 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1696 -2.28026625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1699 -0.56616226 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1705 -1.46832225 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1715 -2.73134625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1861 -0.65637826 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1941 -0.56616226 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1964 -0.92702626 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2218 0.15556574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2228 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2230 -3.00199425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 255 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 819 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 917 1.05772574 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1085 0.06534974 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1187 -0.47594626 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1394 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1479 1.59902174 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1517 -0.38573026 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2009 0.87729374 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 2223 -0.56616226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2225 -1.55853825 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2226 -1.64875425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 27 2.50118173 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 105 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 147 0.78707774 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 224 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 274 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 356 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 431 0.78707774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 551 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 962 0.78707774 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 997 0.96750974 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1389 0.96750974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1831 1.77945374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1996 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1999 1.68923774 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2008 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2216 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 107 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 148 -0.11508226 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 182 1.41858974 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 414 2.05010173 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 511 -0.02486626 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 699 -0.02486626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1017 1.50880574 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1366 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1672 -0.02486626 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1931 -0.92702626 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1966 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1994 -1.01724226 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2002 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2007 -1.82918625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2 2.05010173 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 143 1.14794174 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 160 -0.83681026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 688 1.95988573 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 776 -0.29551426 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 850 1.32837374 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1194 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1849 -0.02486626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 5 2.23053373 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 55 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 347 1.77945374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 856 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1972 -0.20529826 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1977 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1980 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1981 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1985 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1987 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1995 0.33599774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2207 -1.28789025 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 244 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 308 0.24578174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 560 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 561 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1173 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1190 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1269 0.24578174 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1453 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1543 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1681 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1800 0.69686174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1873 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1971 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1974 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 130 0.33599774 -1.3440996 0.5538366 2.5619444 -0.5322119 -0.8193585
## 721 1.32837374 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 837 1.23815774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1741 -1.46832225 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1860 1.50880574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1957 -0.02486626 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1960 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1965 0.51642974 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1968 0.42621374 -1.3440996 -1.8055867 2.5619444 1.8789510 1.2204670
## 58 0.42621374 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 195 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1228 -1.64875425 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1257 -0.29551426 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1592 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1596 -1.01724226 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1750 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1837 -2.28026625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1942 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1961 -1.19767425 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1962 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1963 0.60664574 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 422 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1401 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1525 0.06534974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1646 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1929 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1940 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1945 -1.64875425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1946 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1948 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1949 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1950 -0.38573026 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1953 -2.09983425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1955 -1.64875425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 202 -0.02486626 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 494 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 740 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 780 0.69686174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1069 -1.91940225 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1537 -1.37810625 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1920 -1.73897025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1926 -2.37048225 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1938 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1939 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1947 -2.73134625 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1951 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 25 -1.91940225 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 134 0.60664574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 323 0.15556574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 354 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 381 -1.64875425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1568 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1766 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1782 1.68923774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1903 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1923 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1925 1.14794174 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 1930 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1932 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1934 -3.09221025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 385 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 548 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1078 0.78707774 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1429 -1.10745826 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1581 -0.92702626 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1609 -1.82918625 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1905 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1908 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1916 0.60664574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1918 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1919 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1921 1.14794174 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1927 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1933 1.23815774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 77 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 184 -1.28789025 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 498 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 555 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 574 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 783 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 956 -0.47594626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1584 2.14031773 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1797 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1863 -1.73897025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1874 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1882 -2.28026625 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1898 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1911 1.50880574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1913 0.33599774 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1914 0.51642974 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1915 -1.91940225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 115 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 259 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 321 1.32837374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 825 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1370 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1621 0.69686174 0.7439925 0.5538366 2.5619444 -0.5322119 1.2204670
## 1643 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1688 -1.82918625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1865 -0.29551426 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1880 0.42621374 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1886 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1889 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1892 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1897 -1.73897025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1899 -2.09983425 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1900 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1904 1.23815774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1909 -1.28789025 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 83 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 208 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 329 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 449 -2.64113025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 451 -0.56616226 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 605 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 754 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 822 1.32837374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 844 -2.28026625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1074 0.60664574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1185 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1829 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1843 2.41096573 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1888 -1.64875425 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1893 1.77945374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1894 0.51642974 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 30 -1.73897025 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 211 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 666 -0.02486626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 895 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1165 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1452 -2.28026625 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1526 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1698 -1.46832225 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1862 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1883 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1884 1.41858974 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1885 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1887 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 96 -0.56616226 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 311 1.68923774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 437 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 599 -1.28789025 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 619 -1.28789025 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 778 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1008 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1082 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1732 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1784 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1820 0.15556574 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1834 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1869 1.14794174 0.7439925 0.5538366 2.5619444 -0.5322119 1.2204670
## 1870 -0.47594626 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1871 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1875 -0.38573026 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 1876 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 82 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 258 0.33599774 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 310 1.05772574 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 345 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 464 -1.28789025 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 595 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 676 1.86966973 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 841 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1434 -1.37810625 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1605 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1616 0.87729374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1769 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1833 -2.64113025 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1846 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1858 -0.02486626 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1859 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1866 0.24578174 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 50 0.60664574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 52 1.50880574 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 53 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 250 1.95988573 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 349 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 396 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 868 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 979 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1191 1.32837374 0.7439925 0.5538366 2.5619444 -0.5322119 -0.8193585
## 1409 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1803 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1832 -1.19767425 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1836 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1839 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1841 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1844 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1845 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1848 -0.29551426 -1.3440996 0.5538366 2.5619444 -0.5322119 -0.8193585
## 1851 1.41858974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1852 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1857 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2208 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 204 0.51642974 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 369 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 715 1.14794174 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 926 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1219 0.15556574 -1.3440996 -1.8055867 2.5619444 1.8789510 -0.8193585
## 1468 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1480 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1574 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1823 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1827 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1847 -1.64875425 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 47 0.60664574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 66 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 405 0.24578174 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 818 1.23815774 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1133 0.60664574 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1576 -2.00961825 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1811 0.69686174 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1813 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1825 1.50880574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1826 1.41858974 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1828 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 181 1.14794174 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 279 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 307 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 863 -1.55853825 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1011 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1330 0.06534974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1755 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1795 -1.82918625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1806 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1814 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1815 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1816 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1818 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1821 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1990 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 126 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 165 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 167 1.68923774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 263 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 268 -0.47594626 0.7439925 0.5538366 2.5619444 -0.5322119 -0.8193585
## 781 -1.28789025 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 973 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1528 -1.82918625 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1673 1.59902174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1768 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1788 -0.47594626 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1798 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1801 1.23815774 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1804 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1807 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1809 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 43 1.23815774 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 280 -1.19767425 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 440 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 572 -0.74659426 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1063 0.69686174 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1111 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1496 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1718 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1719 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1774 0.78707774 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1779 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1785 -2.91177825 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1787 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1789 -0.02486626 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 32 -0.20529826 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 226 -0.38573026 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 361 1.14794174 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 620 -2.46069825 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 710 0.33599774 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 790 1.23815774 0.7439925 0.5538366 2.5619444 -0.5322119 1.2204670
## 1344 0.51642974 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1383 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1388 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1417 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1443 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1667 -1.10745826 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1720 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1748 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1765 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1778 0.42621374 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1781 -0.56616226 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1783 0.69686174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2206 0.51642974 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 80 1.05772574 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 297 0.60664574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 424 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 438 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 875 1.68923774 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1073 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1227 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1419 -1.28789025 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1772 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1775 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 69 1.50880574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 217 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 388 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 579 -0.29551426 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 942 -0.20529826 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1093 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1380 -0.56616226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1729 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1752 1.77945374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 71 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 281 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 359 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 611 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1658 0.69686174 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1694 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1716 -1.46832225 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1721 -0.02486626 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 1724 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1737 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1747 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1749 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1753 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1754 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1757 -0.02486626 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 87 -0.56616226 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 118 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 519 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 791 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1379 0.60664574 -1.3440996 -1.8055867 2.5619444 1.8789510 1.2204670
## 1508 0.87729374 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1685 -0.20529826 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1730 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1734 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1740 -0.56616226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1745 0.06534974 -1.3440996 0.5538366 2.5619444 1.8789510 -0.8193585
## 248 1.23815774 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 585 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1021 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1159 -0.38573026 -1.3440996 -1.8055867 2.5619444 1.8789510 -0.8193585
## 1259 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1725 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1726 0.42621374 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 119 0.87729374 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 153 1.32837374 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 391 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1679 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1690 -2.09983425 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1693 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1710 0.24578174 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1711 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1712 -0.56616226 0.7439925 0.5538366 2.5619444 -0.5322119 1.2204670
## 1713 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 175 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 603 -1.28789025 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 730 -0.47594626 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 815 0.96750974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1084 0.60664574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1349 -1.19767425 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1368 1.50880574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1519 0.87729374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1689 -1.46832225 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1691 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1697 0.15556574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1701 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 233 -2.19005025 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1565 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1649 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1668 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1670 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1680 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1590 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1644 1.68923774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
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## 862 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1505 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1607 -1.19767425 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1634 -0.20529826 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
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## 44 0.24578174 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
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## 693 -0.20529826 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
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## 1123 0.51642974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
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## 1476 -1.64875425 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
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## 549 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
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## 1393 0.42621374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1559 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1569 0.42621374 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
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## 1602 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1278 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1347 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1408 -2.73134625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
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## 1577 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1579 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 930 0.42621374 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
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## 1193 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1570 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 609 1.14794174 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
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## 679 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1529 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
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## 140 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 899 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
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## 1450 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1487 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1497 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1501 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 73 -1.19767425 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 408 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 738 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1016 -1.73897025 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
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## 1471 -1.91940225 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1473 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1478 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1486 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1488 -0.02486626 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 57 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 266 -0.02486626 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 404 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 429 -0.02486626 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 484 1.77945374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1055 0.24578174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
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## 1447 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1448 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1456 -0.38573026 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1457 0.78707774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1458 2.05010173 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
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## 1466 0.06534974 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1470 -0.74659426 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 17 1.50880574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 189 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 290 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 645 -0.20529826 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 680 -0.11508226 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1105 1.68923774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1116 -0.47594626 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1376 -0.38573026 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1391 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1402 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1425 1.86966973 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1430 -0.38573026 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1435 1.59902174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1436 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1437 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1440 1.41858974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1441 1.41858974 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1444 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1454 1.41858974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1087 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1097 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1233 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1250 0.78707774 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1256 -0.02486626 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1290 -2.55091425 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1369 0.69686174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1371 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1410 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1411 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1414 0.15556574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1416 0.96750974 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1421 -1.46832225 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1423 1.68923774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 34 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 94 1.50880574 -1.3440996 -1.8055867 2.5619444 1.8789510 -0.8193585
## 133 -2.55091425 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 734 1.68923774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1034 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1287 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1357 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1358 0.06534974 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1372 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1390 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1395 0.60664574 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1396 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1397 -0.74659426 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1399 -1.55853825 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1400 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1404 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1406 -2.37048225 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 81 1.50880574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 210 -0.02486626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 471 1.68923774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1126 -0.02486626 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1129 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1148 1.32837374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1252 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1294 1.05772574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1322 -0.56616226 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1333 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1339 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1341 0.24578174 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1346 0.06534974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1352 -0.92702626 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1353 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1356 -1.91940225 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1382 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 969 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1261 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1303 1.05772574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1316 0.96750974 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 1328 1.41858974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1329 -2.37048225 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1332 0.60664574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1335 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1345 -0.38573026 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1350 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 190 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 262 0.69686174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 339 -0.38573026 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 563 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 882 -0.02486626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1042 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1071 -0.56616226 -1.3440996 0.5538366 2.5619444 -0.5322119 1.2204670
## 1076 0.87729374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1176 0.33599774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1218 1.77945374 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1277 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1304 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1305 0.60664574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1311 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1315 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1319 -0.02486626 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1327 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1336 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 300 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1182 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1222 -1.10745826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1246 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1276 -2.82156225 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1296 0.24578174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1297 -0.20529826 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1301 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1309 1.05772574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1310 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1314 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1320 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1321 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 61 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 152 1.32837374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 171 0.60664574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 380 0.60664574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 746 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 755 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 817 0.15556574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 970 -1.73897025 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1091 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1136 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1170 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1247 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1265 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1266 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1270 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1272 0.60664574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1273 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1283 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1285 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1289 -0.38573026 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1291 0.51642974 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1292 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 352 0.78707774 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 403 0.42621374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 947 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 987 -2.73134625 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1178 1.68923774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1209 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1223 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1235 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1237 0.33599774 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1242 -0.20529826 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1243 -2.91177825 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1244 -0.56616226 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1245 1.41858974 -1.3440996 -1.8055867 2.5619444 1.8789510 1.2204670
## 1248 -0.29551426 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1254 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1255 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1258 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1260 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1271 -0.65637826 -1.3440996 -1.8055867 2.5619444 1.8789510 1.2204670
## 240 -0.92702626 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 536 -0.29551426 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 698 1.50880574 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 949 0.15556574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1001 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1124 -2.28026625 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1195 -1.55853825 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1198 -0.38573026 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1199 -0.92702626 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1205 -0.11508226 -1.3440996 0.5538366 2.5619444 -0.5322119 1.2204670
## 1208 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1215 0.42621374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1217 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1220 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1221 -0.47594626 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1224 -0.38573026 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1229 -0.83681026 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1230 -2.37048225 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1236 1.23815774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1239 1.32837374 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 285 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 433 0.15556574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 559 -1.55853825 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1197 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1202 -0.47594626 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1206 0.06534974 0.7439925 0.5538366 2.5619444 -0.5322119 1.2204670
## 151 0.60664574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 335 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 463 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 480 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 518 0.42621374 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 624 1.23815774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 630 1.41858974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 725 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 769 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 851 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 929 -0.38573026 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1108 1.32837374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1144 0.24578174 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1157 0.06534974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1166 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1169 -0.38573026 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1181 -1.10745826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1189 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1196 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1201 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 139 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 215 0.24578174 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 469 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 586 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 587 -1.19767425 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 811 0.69686174 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 1054 0.51642974 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1064 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1125 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1140 1.32837374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1161 -1.55853825 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1164 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1172 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1174 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 154 -0.02486626 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
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## 319 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 890 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 953 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1080 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1102 -1.64875425 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
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## 1119 -1.10745826 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
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## 1137 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1139 0.42621374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
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## 1146 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1147 1.05772574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1151 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1153 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1154 -1.82918625 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
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## 54 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 239 0.69686174 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 252 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 577 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1039 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1104 0.69686174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1106 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1122 0.60664574 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1131 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 293 1.32837374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 344 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 365 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 412 -0.11508226 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1005 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1059 -1.82918625 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1094 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1096 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1098 -0.02486626 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1099 -1.64875425 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1101 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1103 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1110 1.68923774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1114 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1211 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 33 0.60664574 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 364 -0.11508226 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 590 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 958 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 988 0.42621374 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 1035 1.23815774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1053 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1061 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1083 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1089 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 351 -0.56616226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 389 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 834 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 896 -2.73134625 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 952 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 977 -1.73897025 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1020 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1037 -1.28789025 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1044 -0.02486626 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1045 -1.73897025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1048 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1050 -0.56616226 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1056 -2.19005025 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1057 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1060 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 86 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 150 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 479 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 500 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 533 -0.65637826 -1.3440996 -1.8055867 2.5619444 1.8789510 1.2204670
## 622 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 944 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 948 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 982 -0.38573026 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1024 -0.29551426 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1027 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1028 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 423 -0.02486626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 499 0.15556574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 564 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 606 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 878 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 978 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 983 0.06534974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 991 -2.09983425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 998 0.87729374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1000 -0.38573026 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1007 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1009 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1015 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1023 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 435 -0.11508226 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 465 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 467 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 763 -2.46069825 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 784 1.50880574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 960 -2.55091425 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 966 -0.11508226 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 976 -1.46832225 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 986 0.15556574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 989 -1.55853825 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 992 -1.01724226 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 993 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 996 -1.46832225 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1002 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 60 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 197 0.06534974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 265 0.33599774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 421 -1.10745826 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 550 0.78707774 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 593 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 668 0.24578174 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 897 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 955 0.69686174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 971 1.14794174 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 974 0.78707774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 138 0.15556574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 289 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 835 0.69686174 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 932 -1.19767425 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 943 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 946 1.23815774 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 950 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 951 0.33599774 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 462 0.78707774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 701 1.59902174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 924 -1.10745826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 928 0.15556574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 931 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 935 -1.55853825 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 936 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 212 -0.38573026 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 243 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 278 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 553 -0.65637826 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 901 -0.83681026 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 902 -0.29551426 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 908 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 910 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 911 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 912 -1.55853825 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 915 0.15556574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 918 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 920 1.14794174 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 923 0.42621374 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 925 -0.02486626 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1070 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 185 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 242 -1.28789025 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 284 -2.09983425 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 373 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 557 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 742 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 810 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 830 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 887 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 889 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 891 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 892 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 905 0.24578174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 907 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 16 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 51 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 79 1.05772574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 225 0.69686174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 267 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 314 0.51642974 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 526 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 570 1.59902174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 667 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 716 -2.82156225 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 735 0.78707774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 770 1.23815774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 847 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 869 0.15556574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 873 0.06534974 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 879 -2.55091425 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 884 0.51642974 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 23 -1.46832225 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 104 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 137 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 502 1.41858974 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 785 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 807 -2.37048225 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 827 0.06534974 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 833 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 836 0.60664574 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 840 -1.01724226 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 842 -1.64875425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 848 -0.02486626 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 849 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 853 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 855 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 857 0.06534974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 860 -0.83681026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 866 -0.02486626 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 20 -0.20529826 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 42 1.59902174 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 91 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 101 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 131 -0.56616226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 199 1.68923774 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 287 -0.47594626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 406 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 741 0.06534974 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 779 -0.56616226 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 813 0.78707774 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 831 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 832 -1.82918625 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 838 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 415 1.50880574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 794 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 821 -1.73897025 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 823 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 41 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 102 0.87729374 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 282 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 326 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 386 -0.11508226 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 419 -1.28789025 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 523 -0.38573026 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 541 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 760 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 761 -0.47594626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 772 -2.09983425 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 793 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 798 0.87729374 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 804 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 36 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 177 1.50880574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 231 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 271 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 384 -2.55091425 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 452 -1.37810625 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 565 0.69686174 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 662 -1.64875425 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 726 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 744 -0.11508226 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 748 -0.02486626 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 749 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 751 -2.91177825 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 753 -2.55091425 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 756 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 759 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 764 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 766 -0.11508226 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 768 -1.64875425 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 113 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 124 0.78707774 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 142 -0.29551426 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 149 -3.00199425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 275 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 298 -1.55853825 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 545 0.33599774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 618 -1.55853825 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 672 -0.47594626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 727 -0.02486626 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 736 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 9 -1.19767425 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 11 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 448 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 486 0.60664574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 657 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 659 0.15556574 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 665 0.06534974 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 690 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 704 -1.55853825 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 714 0.15556574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 719 -0.29551426 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 722 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 729 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 85 -0.65637826 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 303 -0.74659426 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 508 -1.01724226 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 702 1.50880574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 705 0.06534974 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 708 0.33599774 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 709 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 713 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 13 -1.28789025 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 427 -1.37810625 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 444 -0.56616226 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 495 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 504 0.42621374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 543 -1.82918625 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 658 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 673 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 686 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 196 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 221 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 497 -0.38573026 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 503 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 516 0.96750974 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 637 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 656 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 661 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 664 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 671 0.06534974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 674 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 678 -1.10745826 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 683 0.60664574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 206 0.24578174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 256 0.06534974 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 453 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 456 -0.29551426 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 509 1.23815774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 544 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 627 0.15556574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 634 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 641 -2.28026625 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 642 -0.02486626 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 643 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 653 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 654 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 660 -0.56616226 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 4 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 121 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 338 -0.47594626 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 496 -1.82918625 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 580 -1.28789025 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 608 -0.47594626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 615 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 616 1.41858974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 623 0.51642974 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 782 -1.10745826 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## aspirin vasodilator diuretic.loop diuretic.thiazide insulin
## 249 1.0690450 4.062019 -2.3135067 -0.378811 -0.3254965
## 318 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 631 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 883 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1095 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1405 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1554 1.0690450 4.062019 0.4322443 -0.378811 3.0722299
## 1589 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1708 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1773 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1850 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1935 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2077 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 110 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 116 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 328 -0.9354143 4.062019 0.4322443 2.639839 3.0722299
## 482 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 723 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 732 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 737 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 802 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 894 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1204 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1564 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1632 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1794 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2120 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 2127 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 3 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 188 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 320 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 539 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 632 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 712 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 808 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 1081 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1232 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1313 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1674 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1717 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1727 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2104 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2154 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2181 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2197 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 445 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 777 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 800 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 904 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 906 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 933 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1155 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1162 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1424 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1664 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2000 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2035 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 186 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 375 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 792 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 957 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1014 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1112 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1253 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1420 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1988 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 88 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 117 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 229 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 512 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 583 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 648 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1058 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 1203 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1225 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1415 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1438 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1442 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1824 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2178 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 200 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 460 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 490 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 505 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 636 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 877 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 885 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2214 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 157 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 170 -0.9354143 4.062019 0.4322443 -0.378811 3.0722299
## 317 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 399 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 489 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 552 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 652 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 876 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1573 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1993 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2040 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 10 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 238 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 360 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 416 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 733 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 967 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 968 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1134 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 1135 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1183 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1184 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1686 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2069 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2204 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 38 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 392 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 443 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 532 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 647 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 724 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1128 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1337 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 1593 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1777 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 8 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 222 1.0690450 -0.246183 0.4322443 2.639839 3.0722299
## 228 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 313 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 342 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 350 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 459 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 573 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 771 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 788 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 789 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 886 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1029 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1067 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1143 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1251 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1360 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1547 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1906 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2187 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2193 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2199 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 74 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 241 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 348 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 442 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 470 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 474 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 524 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 711 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1299 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1515 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1624 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1736 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 2044 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2067 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2183 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2185 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2189 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2190 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 2191 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2192 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 7 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 62 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 169 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 305 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 327 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 542 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 613 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 635 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 812 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 903 -0.9354143 4.062019 -2.3135067 2.639839 -0.3254965
## 1355 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1422 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1509 -0.9354143 4.062019 0.4322443 -0.378811 3.0722299
## 1601 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2175 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2177 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2184 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2186 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2188 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 174 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 253 1.0690450 -0.246183 0.4322443 2.639839 3.0722299
## 286 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 492 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 534 1.0690450 4.062019 -2.3135067 -0.378811 -0.3254965
## 607 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 614 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 663 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 964 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1066 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1433 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1524 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1555 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1675 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1907 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2021 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2113 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2129 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2170 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2172 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2173 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2198 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2200 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2201 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 46 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 103 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 220 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 236 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 514 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 638 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1520 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1722 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1877 -0.9354143 -0.246183 -2.3135067 -0.378811 3.0722299
## 2145 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2146 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 172 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 269 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 270 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 291 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 343 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 707 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 745 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 919 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1293 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1338 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1952 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1975 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2108 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2156 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 2164 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2171 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 2195 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 237 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 363 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 411 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 806 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1854 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1879 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2015 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 2018 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2056 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2081 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2132 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2158 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2159 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2163 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2165 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2168 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2169 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2196 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 161 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 180 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 367 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 685 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 990 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1086 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1100 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1969 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2135 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2153 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2155 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2166 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 223 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 681 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 963 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 1192 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1500 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1522 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1523 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1545 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 1560 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2019 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2095 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2121 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2147 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2151 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2157 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2231 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 112 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 176 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 219 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 382 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 684 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1268 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1617 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1786 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2143 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2148 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 2152 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 98 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 99 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 209 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 254 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 331 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 357 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 410 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1426 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1709 1.0690450 -0.246183 -2.3135067 -0.378811 3.0722299
## 1746 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1796 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2118 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2126 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2134 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2136 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2137 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2138 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 2141 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2142 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2144 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2220 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 64 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 261 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 478 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 485 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 604 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 828 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1392 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1463 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1647 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1881 -0.9354143 4.062019 0.4322443 2.639839 -0.3254965
## 2124 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2131 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2140 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2213 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 159 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 454 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 458 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 475 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 493 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 939 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1150 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1213 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1264 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1300 -0.9354143 4.062019 0.4322443 2.639839 3.0722299
## 1562 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1661 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1776 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2112 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 2115 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2122 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2123 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2125 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2128 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 45 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 179 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 306 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1307 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1872 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2082 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2117 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2119 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 304 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 334 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 513 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 598 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 909 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 1030 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1238 1.0690450 -0.246183 0.4322443 2.639839 3.0722299
## 1700 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2017 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 2087 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2106 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2109 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2110 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2111 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2114 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 40 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 95 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 234 1.0690450 4.062019 0.4322443 -0.378811 3.0722299
## 358 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 450 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 621 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 934 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 965 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1033 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1068 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1120 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1234 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2011 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2068 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2078 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2100 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2102 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2105 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 2203 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 193 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 387 -0.9354143 -0.246183 -2.3135067 -0.378811 3.0722299
## 731 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1262 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1342 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1465 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2048 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2093 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2099 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2101 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2103 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2205 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 72 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 299 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1043 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1049 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1240 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1561 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1723 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1992 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2005 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2088 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2094 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2097 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 84 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 273 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 370 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 418 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 473 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 646 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 670 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1381 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1512 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1738 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1928 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1973 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1976 -0.9354143 4.062019 0.4322443 -0.378811 3.0722299
## 2024 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2049 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2070 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2079 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2080 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 2083 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2085 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2086 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2090 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 2091 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 2092 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 75 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 106 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 413 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 432 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 531 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 839 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1062 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1163 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1177 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1855 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2033 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2071 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2073 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2076 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2084 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 78 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 264 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 315 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 439 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 529 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 758 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1092 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1130 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1412 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1614 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1802 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1970 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1983 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2012 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2053 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2057 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2058 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2059 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2074 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2075 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2215 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 37 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 65 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 100 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 108 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 173 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 207 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 824 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1141 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1145 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1618 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1812 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1856 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1937 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2047 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2061 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2062 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2063 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2221 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 295 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 337 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 372 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 546 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 874 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 972 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1210 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1298 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1498 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1979 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2066 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 383 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 434 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 843 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 865 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 922 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1418 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1485 -0.9354143 -0.246183 -2.3135067 2.639839 -0.3254965
## 1842 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2037 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2052 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 371 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 376 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 377 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 506 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 728 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 826 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1642 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1707 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1751 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1864 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1895 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 1998 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 31 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 35 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 90 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 312 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 457 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 466 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 870 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 913 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1013 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1117 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1306 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1619 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1756 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2026 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2028 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2038 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2039 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2043 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2227 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 162 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 528 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 581 1.0690450 -0.246183 -2.3135067 2.639839 3.0722299
## 600 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 601 1.0690450 -0.246183 0.4322443 2.639839 3.0722299
## 814 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1318 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1477 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1982 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2029 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2034 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2041 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2042 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2224 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2229 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 612 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 703 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 916 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 994 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1188 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1216 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1890 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 1991 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2031 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2032 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 97 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 201 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 341 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 717 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1364 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1439 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1474 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1489 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1669 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1687 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 1704 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1706 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1780 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1984 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2022 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2025 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2217 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2222 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 135 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 166 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 468 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 626 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1031 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1362 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1510 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1625 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1696 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1699 -0.9354143 4.062019 0.4322443 -0.378811 3.0722299
## 1705 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1715 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1861 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1941 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1964 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2218 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2228 1.0690450 4.062019 0.4322443 -0.378811 3.0722299
## 2230 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 255 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 819 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 917 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1085 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 1187 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1394 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1479 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1517 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 2009 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2223 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2225 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2226 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 27 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 105 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 147 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 224 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 274 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 356 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 431 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 551 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 962 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 997 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1389 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1831 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1996 1.0690450 -0.246183 -2.3135067 -0.378811 3.0722299
## 1999 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2008 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2216 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 107 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 148 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 182 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 414 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 511 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 699 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1017 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1366 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1672 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 1931 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 1966 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1994 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 2002 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2007 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 143 -0.9354143 4.062019 0.4322443 2.639839 -0.3254965
## 160 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 688 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 776 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 850 -0.9354143 4.062019 0.4322443 2.639839 -0.3254965
## 1194 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1849 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 5 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 55 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 347 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 856 1.0690450 -0.246183 0.4322443 2.639839 3.0722299
## 1972 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1977 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1980 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1981 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1985 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1987 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1995 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2207 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 244 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 308 -0.9354143 -0.246183 -2.3135067 2.639839 -0.3254965
## 560 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 561 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1173 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1190 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1269 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1453 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1543 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1681 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1800 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1873 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1971 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1974 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 130 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 721 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 837 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 1741 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1860 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1957 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1960 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1965 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1968 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 58 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 195 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1228 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1257 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1592 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1596 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1750 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1837 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1942 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1961 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1962 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1963 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 422 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1401 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 1525 -0.9354143 -0.246183 -2.3135067 2.639839 -0.3254965
## 1646 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1929 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1940 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1945 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 1946 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1948 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1949 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1950 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1953 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1955 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 202 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 494 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 740 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 780 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1069 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1537 -0.9354143 4.062019 -2.3135067 -0.378811 -0.3254965
## 1920 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1926 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1938 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1939 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1947 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1951 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 25 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 134 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 323 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 354 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 381 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1568 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1766 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1782 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1903 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1923 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 1925 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1930 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1932 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1934 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 385 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 548 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1078 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1429 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1581 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1609 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1905 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1908 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1916 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1918 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1919 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1921 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1927 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1933 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 77 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 184 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 498 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 555 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 574 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 783 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 956 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 1584 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1797 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1863 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1874 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1882 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1898 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1911 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1913 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1914 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1915 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 115 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 259 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 321 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 825 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1370 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1621 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1643 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1688 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1865 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1880 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1886 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1889 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1892 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1897 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1899 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1900 -0.9354143 4.062019 0.4322443 2.639839 -0.3254965
## 1904 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1909 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 83 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 208 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 329 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 449 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 451 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 605 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 754 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 822 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 844 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1074 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1185 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 1829 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1843 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1888 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1893 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1894 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 30 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 211 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 666 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 895 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1165 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1452 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1526 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1698 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1862 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1883 1.0690450 4.062019 0.4322443 2.639839 -0.3254965
## 1884 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1885 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1887 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 96 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 311 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 437 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 599 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 619 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 778 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1008 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1082 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1732 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1784 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1820 1.0690450 -0.246183 0.4322443 2.639839 3.0722299
## 1834 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1869 1.0690450 4.062019 0.4322443 2.639839 3.0722299
## 1870 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1871 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1875 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1876 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 82 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 258 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 310 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 345 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 464 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 595 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 676 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 841 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1434 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1605 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1616 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1769 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1833 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1846 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1858 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 1859 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1866 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 50 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 52 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 53 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 250 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 349 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 396 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 868 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 979 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1191 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1409 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1803 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
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## 1283 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
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## 769 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 929 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
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## 469 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 890 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 1023 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
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## 889 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
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## 51 -0.9354143 -0.246183 -2.3135067 2.639839 -0.3254965
## 79 -0.9354143 -0.246183 -2.3135067 2.639839 -0.3254965
## 225 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
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## 314 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 869 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 879 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 104 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 785 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 848 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 849 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 853 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 855 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 91 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
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## 287 -0.9354143 4.062019 0.4322443 -0.378811 3.0722299
## 406 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 741 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 779 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 813 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 831 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 832 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 838 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 415 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 794 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 821 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
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## 41 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 102 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 282 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 326 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 386 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 523 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 541 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 760 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
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## 772 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 793 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 798 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 804 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 36 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
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## 231 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 271 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 384 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 452 -0.9354143 -0.246183 -2.3135067 2.639839 -0.3254965
## 565 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 662 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 749 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 751 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 753 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 756 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 759 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 764 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 766 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 768 1.0690450 -0.246183 -2.3135067 -0.378811 3.0722299
## 113 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 486 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 657 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
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## 303 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 508 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 702 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 705 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 708 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
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## 427 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 444 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
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## 504 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 543 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 658 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 497 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 503 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 637 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 656 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 661 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 664 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 671 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 674 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 678 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 683 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 206 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 256 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 453 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 456 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 509 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 544 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 627 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 634 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 641 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 642 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 643 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 653 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 654 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 660 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 4 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 121 1.0690450 -0.246183 0.4322443 2.639839 3.0722299
## 338 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 496 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 580 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 608 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 615 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 616 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 623 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 782 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## surgery.pacemaker surgery.cabg resting.systolic.bp resting.hr lvef.metabl
## 249 1.8728523 -0.6125297 -0.37910858 -0.64933938 -1.38824471
## 318 -0.5339449 1.6325740 -0.04424236 -1.41083843 -0.01847084
## 631 1.8728523 -0.6125297 0.51386800 -0.23397626 0.66641609
## 883 -0.5339449 -0.6125297 1.63008872 1.15056748 -0.01847084
## 1095 1.8728523 1.6325740 -1.71857344 -0.51088500 -1.38824471
## 1405 -0.5339449 -0.6125297 0.17900178 0.94288592 -0.70335777
## 1554 -0.5339449 -0.6125297 -0.49073065 1.77361216 -0.70335777
## 1589 -0.5339449 -0.6125297 0.17900178 1.28902185 0.66641609
## 1708 -0.5339449 1.6325740 -0.04424236 1.98129372 -0.70335777
## 1773 1.8728523 -0.6125297 -0.04424236 0.11215968 -1.38824471
## 1850 1.8728523 -0.6125297 1.63008872 -0.44165782 -0.70335777
## 1935 -0.5339449 -0.6125297 0.17900178 0.80443154 -0.01847084
## 2077 -0.5339449 -0.6125297 0.06737971 -0.85702094 2.72107688
## 110 1.8728523 1.6325740 -1.27208515 0.31984124 -1.38824471
## 116 -0.5339449 -0.6125297 -1.38370722 2.32742965 -0.01847084
## 328 -0.5339449 -0.6125297 -1.04884101 -0.23397626 -1.66219948
## 482 -0.5339449 -0.6125297 -0.15586443 -0.51088500 -1.79917686
## 723 -0.5339449 -0.6125297 -1.16046308 0.66597717 -0.01847084
## 732 -0.5339449 -0.6125297 -0.15586443 -0.37243063 -1.38824471
## 737 -0.5339449 1.6325740 -0.04424236 -0.85702094 -1.38824471
## 802 -0.5339449 -0.6125297 -0.60235272 -1.34161124 -0.01847084
## 894 -0.5339449 1.6325740 0.84873421 0.04293249 -0.01847084
## 1204 1.8728523 1.6325740 1.63008872 0.18138686 -0.01847084
## 1564 1.8728523 1.6325740 -0.04424236 -0.51088500 0.66641609
## 1632 -0.5339449 -0.6125297 -1.16046308 -0.30320344 -0.01847084
## 1794 1.8728523 1.6325740 -0.37910858 0.80443154 1.35130302
## 2120 1.8728523 -0.6125297 -1.16046308 -0.44165782 1.35130302
## 2127 -0.5339449 -0.6125297 -0.60235272 1.35824904 0.39246131
## 3 -0.5339449 -0.6125297 -0.15586443 1.91206653 2.72107688
## 188 -0.5339449 -0.6125297 1.63008872 0.11215968 0.66641609
## 320 -0.5339449 -0.6125297 0.06737971 -0.02629470 -0.70335777
## 539 -0.5339449 -0.6125297 0.06737971 -0.37243063 -0.70335777
## 632 -0.5339449 -0.6125297 -1.60695137 1.77361216 -0.01847084
## 712 -0.5339449 -0.6125297 -0.60235272 0.80443154 -1.38824471
## 808 -0.5339449 1.6325740 -0.15586443 1.70438497 -0.01847084
## 1081 -0.5339449 -0.6125297 1.63008872 1.01211310 -0.01847084
## 1232 1.8728523 -0.6125297 -1.71857344 -0.30320344 -0.01847084
## 1313 -0.5339449 1.6325740 0.51386800 0.11215968 -0.84033516
## 1674 1.8728523 1.6325740 -0.26748651 1.08134029 -1.38824471
## 1717 1.8728523 1.6325740 1.63008872 -0.71856656 -1.38824471
## 1727 1.8728523 1.6325740 -1.38370722 -0.71856656 -0.70335777
## 2104 1.8728523 1.6325740 -0.04424236 -0.92624812 -0.01847084
## 2154 -0.5339449 -0.6125297 -1.04884101 -0.44165782 1.35130302
## 2181 -0.5339449 1.6325740 -1.04884101 -1.13392968 0.66641609
## 2197 -0.5339449 -0.6125297 -1.38370722 1.28902185 -0.01847084
## 445 -0.5339449 -0.6125297 -0.49073065 -1.34161124 -0.01847084
## 777 -0.5339449 1.6325740 -0.71397479 0.52752280 -0.42940300
## 800 -0.5339449 -0.6125297 -0.15586443 0.04293249 2.72107688
## 904 -0.5339449 -0.6125297 2.63468736 -0.51088500 -0.70335777
## 906 -0.5339449 1.6325740 -0.82559686 -0.99547531 0.52943870
## 933 1.8728523 1.6325740 0.06737971 -0.44165782 -0.01847084
## 1155 -0.5339449 1.6325740 2.18819907 -0.51088500 2.03618995
## 1162 -0.5339449 -0.6125297 -0.04424236 0.25061405 -1.38824471
## 1424 1.8728523 1.6325740 -0.71397479 0.73520436 -0.01847084
## 1664 -0.5339449 1.6325740 1.63008872 -0.09552188 -1.38824471
## 2000 -0.5339449 1.6325740 -0.04424236 0.11215968 -0.01847084
## 2035 -0.5339449 1.6325740 -1.49532930 0.04293249 -0.01847084
## 186 -0.5339449 1.6325740 2.18819907 -1.06470250 -0.70335777
## 375 -0.5339449 1.6325740 -1.27208515 -0.51088500 -0.01847084
## 792 -0.5339449 -0.6125297 -0.93721894 2.18897528 -0.01847084
## 957 1.8728523 1.6325740 0.06737971 0.25061405 -0.01847084
## 1014 -0.5339449 1.6325740 -0.49073065 -0.23397626 -0.70335777
## 1112 -0.5339449 1.6325740 -0.60235272 0.04293249 -1.66219948
## 1253 -0.5339449 1.6325740 -0.04424236 1.08134029 -0.70335777
## 1420 -0.5339449 1.6325740 0.62549007 0.80443154 1.35130302
## 1988 -0.5339449 1.6325740 -0.04424236 -0.02629470 -1.38824471
## 88 -0.5339449 -0.6125297 0.17900178 -0.78779375 -2.07313164
## 117 1.8728523 -0.6125297 -0.93721894 0.04293249 -1.38824471
## 229 -0.5339449 -0.6125297 0.51386800 1.42747622 -1.38824471
## 512 -0.5339449 -0.6125297 1.18360043 2.05052090 0.66641609
## 583 1.8728523 -0.6125297 -0.49073065 -1.13392968 0.25548393
## 648 -0.5339449 -0.6125297 -0.60235272 -0.71856656 -0.70335777
## 1058 -0.5339449 -0.6125297 0.17900178 0.59674998 -0.70335777
## 1203 -0.5339449 -0.6125297 0.51386800 3.01970152 -1.38824471
## 1225 -0.5339449 -0.6125297 -1.49532930 0.94288592 -0.70335777
## 1415 1.8728523 1.6325740 -1.27208515 -1.20315687 -0.70335777
## 1438 -0.5339449 1.6325740 1.29522250 -1.06470250 1.35130302
## 1442 1.8728523 -0.6125297 -1.27208515 0.04293249 -0.70335777
## 1824 1.8728523 -0.6125297 -1.16046308 0.18138686 -0.01847084
## 2178 -0.5339449 1.6325740 -1.04884101 1.08134029 -0.70335777
## 200 1.8728523 -0.6125297 0.40224593 0.66597717 -0.70335777
## 460 1.8728523 -0.6125297 -1.16046308 1.35824904 -0.01847084
## 490 -0.5339449 -0.6125297 -0.93721894 -1.06470250 2.03618995
## 505 -0.5339449 -0.6125297 0.06737971 -1.13392968 -0.01847084
## 636 -0.5339449 -0.6125297 0.06737971 -0.85702094 -0.56638039
## 877 1.8728523 1.6325740 1.07197836 0.66597717 -1.38824471
## 885 -0.5339449 -0.6125297 -0.26748651 1.08134029 -0.84033516
## 2214 -0.5339449 -0.6125297 -0.37910858 0.04293249 -1.38824471
## 157 1.8728523 -0.6125297 -1.04884101 -0.16474907 -1.38824471
## 170 -0.5339449 -0.6125297 0.17900178 -0.30320344 -0.01847084
## 317 -0.5339449 1.6325740 -1.71857344 -0.71856656 0.39246131
## 399 -0.5339449 1.6325740 -1.49532930 -0.30320344 -0.01847084
## 489 -0.5339449 1.6325740 0.96035628 -2.03388311 0.66641609
## 552 -0.5339449 -0.6125297 -0.93721894 1.35824904 -1.38824471
## 652 -0.5339449 -0.6125297 -1.71857344 -0.92624812 -1.38824471
## 876 -0.5339449 -0.6125297 -1.16046308 1.63515778 -1.52522209
## 1573 -0.5339449 -0.6125297 -0.71397479 -0.92624812 -0.01847084
## 1993 1.8728523 -0.6125297 -0.93721894 0.11215968 -0.01847084
## 2040 -0.5339449 -0.6125297 -0.49073065 1.08134029 2.03618995
## 10 1.8728523 -0.6125297 -0.60235272 0.52752280 0.66641609
## 238 -0.5339449 -0.6125297 0.40224593 -0.09552188 -0.01847084
## 360 -0.5339449 -0.6125297 -1.60695137 -0.09552188 -0.70335777
## 416 -0.5339449 -0.6125297 -0.37910858 0.11215968 -0.56638039
## 733 -0.5339449 -0.6125297 -0.71397479 1.08134029 -1.38824471
## 967 -0.5339449 1.6325740 -0.82559686 -0.16474907 0.52943870
## 968 1.8728523 -0.6125297 -0.04424236 1.21979466 -0.70335777
## 1134 -0.5339449 -0.6125297 -0.60235272 1.63515778 -0.70335777
## 1135 -0.5339449 1.6325740 0.40224593 -1.13392968 -0.70335777
## 1183 -0.5339449 -0.6125297 -0.60235272 1.42747622 -1.93615425
## 1184 -0.5339449 -0.6125297 -1.16046308 2.11974809 -1.66219948
## 1686 -0.5339449 1.6325740 1.07197836 0.80443154 -0.01847084
## 2069 -0.5339449 1.6325740 0.17900178 -1.13392968 -0.70335777
## 2204 -0.5339449 -0.6125297 -1.16046308 2.25820246 -0.70335777
## 38 -0.5339449 -0.6125297 1.07197836 2.67356558 -0.70335777
## 392 1.8728523 -0.6125297 -1.04884101 -0.78779375 0.66641609
## 443 1.8728523 -0.6125297 1.63008872 -0.37243063 -1.66219948
## 532 -0.5339449 -0.6125297 -1.27208515 0.80443154 -0.70335777
## 647 1.8728523 1.6325740 -0.04424236 -0.09552188 -0.01847084
## 724 -0.5339449 -0.6125297 0.51386800 0.45829561 -0.97731255
## 1128 -0.5339449 1.6325740 -1.04884101 0.11215968 0.66641609
## 1337 -0.5339449 -0.6125297 0.84873421 0.59674998 0.66641609
## 1593 -0.5339449 1.6325740 -0.49073065 -1.48006562 2.31014472
## 1777 1.8728523 1.6325740 -0.60235272 -1.13392968 -0.01847084
## 8 -0.5339449 -0.6125297 -0.37910858 -0.30320344 -0.70335777
## 222 -0.5339449 1.6325740 0.17900178 -0.37243063 -0.70335777
## 228 -0.5339449 -0.6125297 0.51386800 -1.27238406 -1.38824471
## 313 1.8728523 -0.6125297 1.18360043 -0.78779375 -1.38824471
## 342 -0.5339449 1.6325740 0.29062385 -0.58011219 -0.01847084
## 350 -0.5339449 1.6325740 0.51386800 -1.13392968 -0.01847084
## 459 -0.5339449 -0.6125297 -0.15586443 -0.09552188 -0.01847084
## 573 -0.5339449 -0.6125297 -0.60235272 -0.30320344 -1.38824471
## 771 -0.5339449 -0.6125297 -0.49073065 1.08134029 -0.70335777
## 788 -0.5339449 1.6325740 -0.37910858 1.15056748 0.66641609
## 789 -0.5339449 -0.6125297 -0.04424236 1.49670341 -0.70335777
## 886 -0.5339449 -0.6125297 -0.93721894 -0.02629470 -0.01847084
## 1029 1.8728523 1.6325740 -1.16046308 0.66597717 -0.70335777
## 1067 1.8728523 -0.6125297 1.63008872 0.73520436 -1.38824471
## 1143 -0.5339449 -0.6125297 -0.82559686 -0.30320344 1.35130302
## 1251 -0.5339449 -0.6125297 -1.38370722 0.66597717 -0.01847084
## 1360 1.8728523 1.6325740 -1.49532930 1.01211310 -0.56638039
## 1547 -0.5339449 -0.6125297 -1.04884101 0.87365873 -0.01847084
## 1906 -0.5339449 -0.6125297 0.51386800 0.45829561 -0.01847084
## 2187 -0.5339449 1.6325740 1.18360043 -0.23397626 -0.70335777
## 2193 1.8728523 -0.6125297 -1.60695137 3.01970152 0.66641609
## 2199 1.8728523 -0.6125297 -0.37910858 -0.37243063 -0.29242562
## 74 1.8728523 -0.6125297 -0.15586443 0.94288592 -1.38824471
## 241 -0.5339449 -0.6125297 1.07197836 -1.20315687 -1.38824471
## 348 1.8728523 -0.6125297 0.17900178 -0.16474907 1.35130302
## 442 -0.5339449 1.6325740 -0.60235272 -1.34161124 -0.70335777
## 470 -0.5339449 -0.6125297 2.07657700 0.11215968 -0.70335777
## 474 -0.5339449 -0.6125297 0.17900178 -0.64933938 -0.01847084
## 524 -0.5339449 1.6325740 -1.71857344 -0.23397626 0.66641609
## 711 1.8728523 1.6325740 -0.93721894 0.66597717 0.52943870
## 1299 -0.5339449 -0.6125297 0.62549007 0.04293249 0.66641609
## 1515 1.8728523 -0.6125297 -0.04424236 -1.13392968 -0.01847084
## 1624 1.8728523 -0.6125297 -0.71397479 -1.41083843 -0.84033516
## 1736 -0.5339449 -0.6125297 -0.82559686 0.52752280 -0.70335777
## 2044 1.8728523 -0.6125297 4.08577430 -0.64933938 -0.70335777
## 2067 -0.5339449 1.6325740 1.51846664 -1.06470250 -0.01847084
## 2183 1.8728523 1.6325740 -0.04424236 -0.64933938 1.35130302
## 2185 -0.5339449 1.6325740 -0.04424236 -0.44165782 -0.01847084
## 2189 1.8728523 -0.6125297 -0.60235272 -0.44165782 -0.70335777
## 2190 1.8728523 -0.6125297 -0.60235272 -0.78779375 1.35130302
## 2191 -0.5339449 -0.6125297 -0.15586443 -0.44165782 -0.01847084
## 2192 -0.5339449 -0.6125297 -1.49532930 -0.09552188 -0.01847084
## 7 -0.5339449 -0.6125297 -0.60235272 0.18138686 -1.38824471
## 62 -0.5339449 1.6325740 -1.27208515 0.18138686 -0.01847084
## 169 1.8728523 1.6325740 2.29982115 -0.37243063 0.66641609
## 305 -0.5339449 -0.6125297 -1.16046308 -1.61851999 -0.01847084
## 327 -0.5339449 1.6325740 -1.71857344 -0.37243063 -0.70335777
## 542 -0.5339449 -0.6125297 -0.15586443 0.04293249 0.66641609
## 613 -0.5339449 -0.6125297 -0.60235272 -0.02629470 1.62525779
## 635 -0.5339449 -0.6125297 0.62549007 -0.99547531 1.62525779
## 812 1.8728523 -0.6125297 -1.16046308 0.38906842 -1.66219948
## 903 1.8728523 1.6325740 -0.93721894 -0.09552188 1.76223518
## 1355 -0.5339449 -0.6125297 2.85793151 -0.02629470 0.66641609
## 1422 -0.5339449 1.6325740 2.63468736 -0.16474907 -0.70335777
## 1509 -0.5339449 -0.6125297 1.96495493 1.15056748 0.66641609
## 1601 1.8728523 -0.6125297 0.84873421 -0.09552188 -0.01847084
## 2175 1.8728523 -0.6125297 0.96035628 0.59674998 0.80339347
## 2177 -0.5339449 -0.6125297 0.62549007 -0.02629470 2.03618995
## 2184 -0.5339449 1.6325740 -0.49073065 0.94288592 -0.70335777
## 2186 -0.5339449 1.6325740 -0.15586443 0.66597717 -0.01847084
## 2188 -0.5339449 1.6325740 0.73711214 -1.75697436 2.03618995
## 174 -0.5339449 1.6325740 0.51386800 0.18138686 -0.01847084
## 253 -0.5339449 -0.6125297 1.63008872 -0.51088500 -0.70335777
## 286 -0.5339449 1.6325740 -0.49073065 -0.58011219 -1.38824471
## 492 -0.5339449 -0.6125297 -0.60235272 0.87365873 1.35130302
## 534 -0.5339449 1.6325740 1.63008872 1.49670341 -0.84033516
## 607 -0.5339449 -0.6125297 1.63008872 0.18138686 0.66641609
## 614 -0.5339449 1.6325740 0.06737971 -0.51088500 -0.56638039
## 663 -0.5339449 1.6325740 0.06737971 -0.23397626 0.66641609
## 964 -0.5339449 1.6325740 -1.16046308 0.66597717 0.66641609
## 1066 1.8728523 1.6325740 0.51386800 -0.64933938 0.66641609
## 1433 -0.5339449 -0.6125297 0.40224593 -0.02629470 -0.01847084
## 1524 -0.5339449 -0.6125297 -1.16046308 1.15056748 -0.70335777
## 1555 -0.5339449 1.6325740 -0.60235272 -0.85702094 -1.38824471
## 1675 1.8728523 1.6325740 -0.71397479 -0.30320344 -0.70335777
## 1907 1.8728523 -0.6125297 -0.26748651 0.66597717 -0.01847084
## 2021 -0.5339449 -0.6125297 -0.04424236 0.18138686 -0.70335777
## 2113 -0.5339449 1.6325740 0.06737971 0.73520436 -0.01847084
## 2129 -0.5339449 -0.6125297 0.06737971 -0.23397626 -0.01847084
## 2170 -0.5339449 -0.6125297 -1.49532930 -0.30320344 -0.01847084
## 2172 -0.5339449 1.6325740 1.96495493 -0.09552188 -0.01847084
## 2173 1.8728523 -0.6125297 2.18819907 -0.92624812 2.72107688
## 2198 -0.5339449 1.6325740 -0.60235272 0.04293249 -0.01847084
## 2200 -0.5339449 -0.6125297 -0.49073065 0.80443154 -0.01847084
## 2201 -0.5339449 -0.6125297 -0.71397479 -0.64933938 -0.70335777
## 1 -0.5339449 -0.6125297 -1.16046308 -0.16474907 -0.70335777
## 46 -0.5339449 1.6325740 1.40684457 -0.02629470 0.66641609
## 103 -0.5339449 1.6325740 -1.04884101 -0.37243063 -0.01847084
## 220 -0.5339449 -0.6125297 1.74171079 1.77361216 -0.01847084
## 236 -0.5339449 -0.6125297 -1.60695137 0.59674998 1.35130302
## 514 -0.5339449 -0.6125297 -1.16046308 -0.02629470 -1.66219948
## 638 -0.5339449 1.6325740 -1.16046308 -0.44165782 -0.70335777
## 1520 1.8728523 1.6325740 -0.93721894 0.25061405 2.72107688
## 1722 -0.5339449 -0.6125297 -1.04884101 2.11974809 -1.38824471
## 1877 -0.5339449 -0.6125297 -1.16046308 -0.16474907 -0.70335777
## 2145 1.8728523 -0.6125297 1.18360043 -0.64933938 -0.70335777
## 2146 1.8728523 -0.6125297 -0.04424236 0.38906842 -0.70335777
## 172 -0.5339449 1.6325740 0.17900178 -0.58011219 -0.01847084
## 269 -0.5339449 1.6325740 2.18819907 -0.51088500 2.03618995
## 270 -0.5339449 1.6325740 0.73711214 -1.20315687 -1.11428993
## 291 1.8728523 1.6325740 -0.60235272 -0.44165782 -0.70335777
## 343 1.8728523 -0.6125297 -0.60235272 -0.16474907 -1.38824471
## 707 -0.5339449 -0.6125297 1.63008872 -0.78779375 -0.70335777
## 745 -0.5339449 -0.6125297 -1.10465204 -0.16474907 -0.01847084
## 919 -0.5339449 -0.6125297 -0.15586443 0.73520436 -0.70335777
## 1293 1.8728523 -0.6125297 -0.04424236 -0.44165782 -0.70335777
## 1338 -0.5339449 -0.6125297 0.40224593 -0.23397626 -0.01847084
## 1952 -0.5339449 1.6325740 -0.60235272 -2.24156467 1.35130302
## 1975 1.8728523 -0.6125297 -0.15586443 -1.13392968 1.35130302
## 2108 1.8728523 -0.6125297 -0.15586443 0.45829561 -0.01847084
## 2156 -0.5339449 -0.6125297 -0.04424236 -0.37243063 0.66641609
## 2164 -0.5339449 -0.6125297 -0.26748651 -0.64933938 0.66641609
## 2171 -0.5339449 1.6325740 -0.71397479 -0.37243063 -0.84033516
## 2195 -0.5339449 1.6325740 1.63008872 -0.51088500 -0.01847084
## 237 -0.5339449 -0.6125297 0.06737971 -1.06470250 0.66641609
## 363 -0.5339449 1.6325740 -0.37910858 -0.23397626 -0.70335777
## 411 -0.5339449 -0.6125297 -0.60235272 0.31984124 -0.01847084
## 806 -0.5339449 -0.6125297 -0.49073065 -1.41083843 -0.70335777
## 1854 -0.5339449 -0.6125297 0.40224593 -0.99547531 -1.38824471
## 1879 1.8728523 -0.6125297 0.73711214 -0.44165782 -0.01847084
## 2015 1.8728523 1.6325740 0.51386800 0.45829561 -0.70335777
## 2018 1.8728523 -0.6125297 -1.71857344 0.11215968 -0.29242562
## 2056 -0.5339449 -0.6125297 0.96035628 0.04293249 2.72107688
## 2081 1.8728523 -0.6125297 -0.60235272 0.59674998 -1.38824471
## 2132 -0.5339449 1.6325740 0.17900178 -0.23397626 -1.11428993
## 2158 -0.5339449 1.6325740 1.07197836 -0.44165782 2.72107688
## 2159 -0.5339449 -0.6125297 -1.04884101 0.11215968 -1.38824471
## 2163 -0.5339449 -0.6125297 0.17900178 1.77361216 -0.01847084
## 2165 1.8728523 -0.6125297 -0.37910858 0.59674998 2.03618995
## 2168 1.8728523 -0.6125297 -0.60235272 0.80443154 2.03618995
## 2169 -0.5339449 -0.6125297 0.40224593 -0.37243063 -0.01847084
## 2196 -0.5339449 1.6325740 -0.26748651 0.73520436 -0.01847084
## 161 -0.5339449 -0.6125297 -1.27208515 -0.51088500 -1.38824471
## 180 -0.5339449 1.6325740 0.06737971 1.35824904 -1.38824471
## 367 -0.5339449 -0.6125297 -0.49073065 1.84283934 -0.01847084
## 685 1.8728523 -0.6125297 -1.71857344 -0.44165782 0.66641609
## 990 -0.5339449 -0.6125297 -0.60235272 -0.64933938 -0.01847084
## 1086 -0.5339449 1.6325740 -1.04884101 -0.71856656 -0.01847084
## 1100 -0.5339449 -0.6125297 -1.16046308 0.45829561 -0.70335777
## 1969 -0.5339449 -0.6125297 -0.71397479 0.11215968 -0.01847084
## 2135 -0.5339449 -0.6125297 1.07197836 -0.37243063 -1.38824471
## 2153 -0.5339449 1.6325740 -0.04424236 -0.85702094 0.66641609
## 2155 -0.5339449 -0.6125297 -0.49073065 -0.09552188 0.66641609
## 2166 1.8728523 -0.6125297 -1.04884101 1.21979466 -0.01847084
## 223 -0.5339449 1.6325740 -1.04884101 -0.92624812 -0.01847084
## 681 -0.5339449 1.6325740 -1.27208515 1.35824904 -1.25126732
## 963 1.8728523 1.6325740 -0.04424236 -0.51088500 2.03618995
## 1192 -0.5339449 -0.6125297 -0.71397479 -0.30320344 -1.11428993
## 1500 -0.5339449 1.6325740 1.07197836 -1.48006562 0.66641609
## 1522 -0.5339449 1.6325740 2.18819907 1.49670341 2.72107688
## 1523 -0.5339449 -0.6125297 -0.60235272 2.60433840 -1.38824471
## 1545 -0.5339449 -0.6125297 -0.49073065 -1.27238406 -1.38824471
## 1560 -0.5339449 -0.6125297 -0.37910858 1.15056748 -0.70335777
## 2019 1.8728523 -0.6125297 -0.04424236 -0.51088500 -0.01847084
## 2095 1.8728523 -0.6125297 0.62549007 -0.51088500 -1.38824471
## 2121 -0.5339449 -0.6125297 0.17900178 1.01211310 2.72107688
## 2147 1.8728523 -0.6125297 -0.04424236 -0.09552188 0.66641609
## 2151 -0.5339449 -0.6125297 0.29062385 0.45829561 2.72107688
## 2157 -0.5339449 -0.6125297 0.96035628 0.25061405 -0.70335777
## 2231 -0.5339449 -0.6125297 1.40684457 -0.78779375 -0.01847084
## 112 1.8728523 -0.6125297 -0.49073065 1.15056748 1.35130302
## 176 1.8728523 1.6325740 -0.82559686 -0.85702094 0.25548393
## 219 -0.5339449 1.6325740 1.29522250 -0.30320344 -0.70335777
## 382 1.8728523 1.6325740 -1.71857344 -0.37243063 -0.70335777
## 684 -0.5339449 -0.6125297 -0.71397479 0.52752280 1.35130302
## 1268 -0.5339449 -0.6125297 -0.15586443 -0.99547531 0.66641609
## 1617 1.8728523 -0.6125297 -0.04424236 -0.71856656 -0.01847084
## 1786 -0.5339449 1.6325740 1.18360043 -0.09552188 -0.01847084
## 2143 -0.5339449 -0.6125297 -0.71397479 -0.64933938 -0.01847084
## 2148 -0.5339449 -0.6125297 -0.93721894 -0.09552188 -1.38824471
## 2152 -0.5339449 -0.6125297 -0.15586443 1.49670341 -0.70335777
## 98 -0.5339449 -0.6125297 0.06737971 -0.23397626 -0.70335777
## 99 -0.5339449 -0.6125297 1.07197836 -0.16474907 -0.70335777
## 209 -0.5339449 -0.6125297 1.07197836 -0.44165782 0.66641609
## 254 -0.5339449 -0.6125297 -0.71397479 0.94288592 0.66641609
## 331 1.8728523 -0.6125297 1.18360043 0.94288592 1.07734825
## 357 -0.5339449 -0.6125297 0.51386800 0.11215968 -0.70335777
## 410 -0.5339449 -0.6125297 1.07197836 -0.99547531 0.66641609
## 1426 -0.5339449 -0.6125297 1.63008872 -1.34161124 0.66641609
## 1709 -0.5339449 -0.6125297 -0.71397479 0.52752280 -0.01847084
## 1746 -0.5339449 1.6325740 0.73711214 -0.58011219 0.66641609
## 1796 1.8728523 1.6325740 0.17900178 0.87365873 -0.01847084
## 2118 -0.5339449 1.6325740 1.29522250 -1.89542874 1.35130302
## 2126 -0.5339449 1.6325740 -1.16046308 0.73520436 -0.01847084
## 2134 1.8728523 1.6325740 -0.15586443 0.73520436 -0.01847084
## 2136 -0.5339449 1.6325740 1.07197836 -1.34161124 0.66641609
## 2137 -0.5339449 -0.6125297 0.06737971 0.04293249 2.03618995
## 2138 1.8728523 -0.6125297 -1.16046308 0.31984124 0.66641609
## 2141 -0.5339449 -0.6125297 1.07197836 -1.06470250 2.03618995
## 2142 1.8728523 -0.6125297 1.63008872 -1.06470250 0.66641609
## 2144 1.8728523 -0.6125297 -0.82559686 -0.85702094 -1.38824471
## 2220 -0.5339449 1.6325740 -0.49073065 1.63515778 -0.01847084
## 64 -0.5339449 -0.6125297 -1.04884101 -0.09552188 0.66641609
## 261 -0.5339449 1.6325740 1.74171079 1.49670341 1.35130302
## 478 -0.5339449 1.6325740 -0.60235272 0.11215968 -0.01847084
## 485 1.8728523 -0.6125297 -1.04884101 0.25061405 -1.38824471
## 604 -0.5339449 1.6325740 -0.04424236 0.66597717 -0.01847084
## 828 -0.5339449 -0.6125297 0.51386800 1.08134029 1.35130302
## 1392 1.8728523 -0.6125297 -0.49073065 0.73520436 -0.01847084
## 1463 -0.5339449 -0.6125297 -0.60235272 0.04293249 -0.01847084
## 1647 -0.5339449 -0.6125297 -0.71397479 -0.92624812 -0.70335777
## 1881 -0.5339449 -0.6125297 1.07197836 1.91206653 -1.38824471
## 2124 -0.5339449 1.6325740 0.06737971 -0.92624812 1.35130302
## 2131 -0.5339449 -0.6125297 0.06737971 -0.78779375 0.66641609
## 2140 1.8728523 1.6325740 -0.60235272 -1.82620155 0.52943870
## 2213 -0.5339449 -0.6125297 -0.04424236 -1.41083843 -0.01847084
## 159 1.8728523 1.6325740 -1.04884101 -1.13392968 -0.70335777
## 454 -0.5339449 -0.6125297 0.62549007 0.38906842 -1.38824471
## 458 -0.5339449 -0.6125297 -1.16046308 2.05052090 -2.07313164
## 475 1.8728523 -0.6125297 -0.93721894 -0.44165782 -0.70335777
## 493 -0.5339449 1.6325740 0.84873421 0.25061405 -0.01847084
## 939 1.8728523 1.6325740 0.06737971 0.80443154 0.66641609
## 1150 -0.5339449 -0.6125297 -1.16046308 2.46588402 -0.01847084
## 1213 -0.5339449 1.6325740 -0.04424236 1.08134029 -1.38824471
## 1264 -0.5339449 1.6325740 -0.60235272 -0.99547531 -0.01847084
## 1300 -0.5339449 -0.6125297 1.51846664 -0.16474907 -0.01847084
## 1562 1.8728523 -0.6125297 -1.16046308 -0.44165782 -0.70335777
## 1661 -0.5339449 -0.6125297 -1.60695137 0.31984124 -0.70335777
## 1776 -0.5339449 1.6325740 0.29062385 -2.10311030 0.66641609
## 2112 1.8728523 1.6325740 -0.82559686 1.01211310 -0.01847084
## 2115 -0.5339449 1.6325740 0.06737971 -1.48006562 0.66641609
## 2122 -0.5339449 -0.6125297 -0.93721894 -0.85702094 -0.97731255
## 2123 1.8728523 -0.6125297 0.62549007 0.45829561 1.35130302
## 2125 -0.5339449 -0.6125297 0.96035628 0.04293249 -0.70335777
## 2128 -0.5339449 -0.6125297 -0.04424236 0.66597717 -0.01847084
## 45 1.8728523 1.6325740 0.62549007 -0.99547531 -1.38824471
## 179 -0.5339449 -0.6125297 -0.60235272 0.25061405 -0.56638039
## 306 1.8728523 -0.6125297 2.74630943 0.80443154 -0.01847084
## 1307 1.8728523 -0.6125297 -0.93721894 -1.20315687 -1.25126732
## 1872 -0.5339449 1.6325740 0.51386800 -0.30320344 -0.01847084
## 2082 -0.5339449 1.6325740 1.63008872 -1.54929280 1.35130302
## 2117 -0.5339449 -0.6125297 0.06737971 -0.85702094 -1.38824471
## 2119 1.8728523 1.6325740 0.62549007 0.11215968 2.03618995
## 304 -0.5339449 1.6325740 1.18360043 -0.02629470 -0.01847084
## 334 1.8728523 -0.6125297 -0.04424236 -1.13392968 -0.70335777
## 513 -0.5339449 1.6325740 0.62549007 -0.64933938 0.66641609
## 598 -0.5339449 -0.6125297 -0.60235272 -0.44165782 2.72107688
## 909 -0.5339449 1.6325740 -0.49073065 0.87365873 0.66641609
## 1030 1.8728523 -0.6125297 -1.04884101 -0.44165782 -0.01847084
## 1238 1.8728523 1.6325740 -1.27208515 0.31984124 -0.70335777
## 1700 1.8728523 -0.6125297 -0.04424236 -1.13392968 -1.38824471
## 2017 -0.5339449 -0.6125297 -0.15586443 2.32742965 -1.38824471
## 2087 1.8728523 1.6325740 0.17900178 0.87365873 -0.29242562
## 2106 -0.5339449 1.6325740 0.17900178 -0.23397626 -0.01847084
## 2109 -0.5339449 -0.6125297 2.07657700 -0.30320344 2.72107688
## 2110 -0.5339449 1.6325740 1.29522250 -0.71856656 2.03618995
## 2111 -0.5339449 -0.6125297 -0.60235272 -0.64933938 -0.01847084
## 2114 1.8728523 1.6325740 -0.04424236 0.18138686 1.07734825
## 40 -0.5339449 -0.6125297 -0.49073065 1.08134029 -0.70335777
## 95 -0.5339449 1.6325740 -0.49073065 -0.85702094 -0.01847084
## 234 1.8728523 1.6325740 -1.60695137 -1.34161124 -0.01847084
## 358 -0.5339449 -0.6125297 -0.37910858 -0.78779375 -1.38824471
## 450 -0.5339449 -0.6125297 0.40224593 0.38906842 0.66641609
## 621 -0.5339449 -0.6125297 0.29062385 1.49670341 -0.01847084
## 934 1.8728523 1.6325740 1.63008872 0.38906842 1.35130302
## 965 -0.5339449 1.6325740 1.07197836 -0.58011219 -0.70335777
## 1033 1.8728523 1.6325740 1.63008872 -0.16474907 -1.25126732
## 1068 1.8728523 -0.6125297 -0.49073065 0.87365873 -1.38824471
## 1120 -0.5339449 -0.6125297 -0.49073065 0.73520436 -0.70335777
## 1234 -0.5339449 -0.6125297 -1.16046308 1.35824904 -0.01847084
## 2011 1.8728523 -0.6125297 -0.26748651 -0.16474907 -0.70335777
## 2068 -0.5339449 -0.6125297 0.62549007 0.04293249 -0.01847084
## 2078 -0.5339449 1.6325740 -0.49073065 0.11215968 -1.38824471
## 2100 1.8728523 1.6325740 1.29522250 0.25061405 -0.70335777
## 2102 -0.5339449 -0.6125297 0.73711214 1.21979466 1.07734825
## 2105 -0.5339449 -0.6125297 -0.71397479 0.52752280 -0.70335777
## 2203 -0.5339449 -0.6125297 -0.71397479 0.38906842 -1.38824471
## 193 -0.5339449 1.6325740 -0.04424236 -0.44165782 -0.01847084
## 387 -0.5339449 -0.6125297 1.07197836 -0.71856656 0.66641609
## 731 -0.5339449 -0.6125297 0.62549007 -0.78779375 2.72107688
## 1262 -0.5339449 -0.6125297 0.40224593 -1.48006562 -0.01847084
## 1342 -0.5339449 -0.6125297 1.63008872 -0.99547531 -0.01847084
## 1465 1.8728523 -0.6125297 -1.27208515 -0.85702094 0.66641609
## 2048 1.8728523 -0.6125297 -0.49073065 0.66597717 -0.70335777
## 2093 -0.5339449 -0.6125297 1.07197836 -0.16474907 0.66641609
## 2099 -0.5339449 -0.6125297 -1.04884101 0.94288592 -0.01847084
## 2101 -0.5339449 -0.6125297 -0.15586443 -0.09552188 -1.38824471
## 2103 -0.5339449 -0.6125297 -0.04424236 -0.99547531 0.66641609
## 2205 -0.5339449 -0.6125297 -0.15586443 1.35824904 -0.70335777
## 72 -0.5339449 1.6325740 1.07197836 -0.23397626 2.03618995
## 299 1.8728523 1.6325740 0.51386800 -1.41083843 -0.70335777
## 1043 -0.5339449 -0.6125297 -0.49073065 -1.68774718 -0.01847084
## 1049 -0.5339449 -0.6125297 -0.26748651 -1.20315687 -0.70335777
## 1240 -0.5339449 -0.6125297 -0.93721894 -0.58011219 -0.01847084
## 1561 1.8728523 -0.6125297 -0.04424236 0.94288592 2.72107688
## 1723 -0.5339449 1.6325740 -0.60235272 1.98129372 -0.84033516
## 1992 -0.5339449 -0.6125297 0.40224593 0.59674998 -0.70335777
## 2005 -0.5339449 -0.6125297 -0.37910858 -0.44165782 -1.38824471
## 2088 -0.5339449 1.6325740 0.06737971 -1.20315687 -0.01847084
## 2094 -0.5339449 -0.6125297 -0.49073065 0.11215968 -0.01847084
## 2097 1.8728523 -0.6125297 -1.04884101 1.91206653 -0.01847084
## 84 -0.5339449 1.6325740 -1.16046308 2.18897528 -0.01847084
## 273 -0.5339449 1.6325740 0.62549007 -0.44165782 -0.70335777
## 370 -0.5339449 1.6325740 -0.15586443 -0.71856656 -0.01847084
## 418 -0.5339449 1.6325740 0.51386800 -0.44165782 -0.70335777
## 473 -0.5339449 1.6325740 -0.04424236 -0.92624812 -1.38824471
## 646 1.8728523 -0.6125297 1.29522250 -0.44165782 -1.38824471
## 670 -0.5339449 -0.6125297 0.17900178 0.45829561 -0.97731255
## 1381 -0.5339449 -0.6125297 -0.37910858 1.28902185 -0.01847084
## 1512 -0.5339449 1.6325740 -1.16046308 0.38906842 -0.70335777
## 1738 -0.5339449 -0.6125297 -0.26748651 1.56593060 0.66641609
## 1928 1.8728523 -0.6125297 -0.82559686 0.38906842 -0.70335777
## 1973 1.8728523 1.6325740 0.51386800 -0.30320344 -1.38824471
## 1976 1.8728523 -0.6125297 0.17900178 1.84283934 -0.01847084
## 2024 -0.5339449 -0.6125297 0.96035628 1.77361216 0.66641609
## 2049 -0.5339449 -0.6125297 -1.60695137 1.01211310 -0.01847084
## 2070 1.8728523 -0.6125297 0.06737971 -0.78779375 -0.56638039
## 2079 -0.5339449 -0.6125297 0.06737971 1.84283934 -0.70335777
## 2080 1.8728523 -0.6125297 1.18360043 0.31984124 0.66641609
## 2083 1.8728523 -0.6125297 1.63008872 1.56593060 -0.56638039
## 2085 -0.5339449 1.6325740 0.40224593 -0.78779375 1.62525779
## 2086 1.8728523 -0.6125297 0.62549007 -0.37243063 -0.01847084
## 2090 -0.5339449 1.6325740 -1.16046308 0.11215968 0.66641609
## 2091 -0.5339449 1.6325740 -0.60235272 -0.09552188 1.35130302
## 2092 -0.5339449 -0.6125297 -0.04424236 0.31984124 -1.38824471
## 75 1.8728523 -0.6125297 0.62549007 1.21979466 1.35130302
## 106 1.8728523 1.6325740 0.17900178 -1.13392968 -0.01847084
## 413 -0.5339449 -0.6125297 0.06737971 -0.92624812 0.66641609
## 432 -0.5339449 -0.6125297 1.40684457 -1.82620155 -1.38824471
## 531 -0.5339449 -0.6125297 -0.93721894 0.45829561 -0.01847084
## 839 -0.5339449 -0.6125297 0.06737971 -0.58011219 0.66641609
## 1062 -0.5339449 1.6325740 -0.60235272 0.04293249 1.35130302
## 1163 -0.5339449 -0.6125297 0.17900178 1.08134029 -0.70335777
## 1177 -0.5339449 -0.6125297 -0.04424236 -0.85702094 -1.38824471
## 1855 1.8728523 -0.6125297 -0.04424236 1.21979466 -0.70335777
## 2033 -0.5339449 -0.6125297 0.17900178 0.38906842 -0.70335777
## 2071 1.8728523 1.6325740 2.74630943 -0.85702094 -0.01847084
## 2073 -0.5339449 -0.6125297 0.62549007 0.31984124 2.03618995
## 2076 -0.5339449 1.6325740 -0.93721894 -0.64933938 1.35130302
## 2084 -0.5339449 -0.6125297 0.06737971 1.35824904 -1.38824471
## 78 -0.5339449 -0.6125297 2.07657700 -1.61851999 1.35130302
## 264 -0.5339449 1.6325740 1.85333286 -1.75697436 0.66641609
## 315 -0.5339449 1.6325740 -1.27208515 -0.44165782 -0.70335777
## 439 -0.5339449 -0.6125297 -1.16046308 0.11215968 1.35130302
## 529 -0.5339449 1.6325740 -0.82559686 0.31984124 -0.42940300
## 758 -0.5339449 -0.6125297 0.51386800 0.11215968 1.35130302
## 1092 -0.5339449 1.6325740 -1.04884101 1.70438497 -0.70335777
## 1130 -0.5339449 -0.6125297 -1.60695137 0.04293249 -1.38824471
## 1412 1.8728523 -0.6125297 -0.71397479 0.25061405 -0.70335777
## 1614 -0.5339449 -0.6125297 -1.16046308 0.25061405 -0.70335777
## 1802 1.8728523 1.6325740 -0.37910858 -0.71856656 2.72107688
## 1970 -0.5339449 1.6325740 -0.04424236 -0.64933938 0.66641609
## 1983 1.8728523 -0.6125297 1.96495493 -0.92624812 -0.01847084
## 2012 -0.5339449 -0.6125297 0.51386800 2.25820246 -0.01847084
## 2053 -0.5339449 -0.6125297 0.29062385 -0.78779375 -0.01847084
## 2057 -0.5339449 -0.6125297 -0.49073065 0.73520436 -0.70335777
## 2058 -0.5339449 -0.6125297 0.17900178 -0.92624812 -0.01847084
## 2059 1.8728523 -0.6125297 0.40224593 -0.09552188 0.66641609
## 2074 1.8728523 1.6325740 1.18360043 -0.71856656 -0.70335777
## 2075 -0.5339449 -0.6125297 1.07197836 -1.75697436 -0.42940300
## 2215 -0.5339449 -0.6125297 1.07197836 0.04293249 1.89921256
## 37 1.8728523 1.6325740 0.29062385 -1.13392968 1.35130302
## 65 -0.5339449 1.6325740 -0.71397479 0.94288592 1.07734825
## 100 -0.5339449 1.6325740 -0.04424236 -0.92624812 0.66641609
## 108 -0.5339449 -0.6125297 0.62549007 0.52752280 0.66641609
## 173 -0.5339449 -0.6125297 2.18819907 -0.44165782 -0.01847084
## 207 1.8728523 -0.6125297 -0.60235272 -0.23397626 0.66641609
## 824 -0.5339449 1.6325740 -0.37910858 -0.71856656 -0.70335777
## 1141 -0.5339449 -0.6125297 1.07197836 -0.16474907 -0.70335777
## 1145 -0.5339449 -0.6125297 -1.71857344 0.11215968 -0.97731255
## 1618 -0.5339449 -0.6125297 -0.15586443 0.52752280 -1.38824471
## 1812 1.8728523 -0.6125297 -0.93721894 0.18138686 -1.38824471
## 1856 -0.5339449 -0.6125297 0.84873421 1.98129372 -0.70335777
## 1937 -0.5339449 -0.6125297 -1.16046308 0.73520436 -0.01847084
## 2047 -0.5339449 -0.6125297 0.06737971 1.28902185 -0.01847084
## 2061 -0.5339449 1.6325740 0.62549007 -0.78779375 0.66641609
## 2062 -0.5339449 1.6325740 1.63008872 0.18138686 -0.70335777
## 2063 -0.5339449 1.6325740 -0.49073065 -0.58011219 -0.01847084
## 2221 -0.5339449 -0.6125297 0.62549007 -0.37243063 -0.01847084
## 295 1.8728523 -0.6125297 -1.60695137 0.11215968 -0.70335777
## 337 1.8728523 -0.6125297 -0.26748651 0.18138686 0.66641609
## 372 -0.5339449 -0.6125297 0.06737971 2.60433840 -1.38824471
## 546 -0.5339449 -0.6125297 -0.04424236 0.18138686 -0.01847084
## 874 -0.5339449 1.6325740 -0.04424236 -1.48006562 -0.01847084
## 972 1.8728523 1.6325740 -1.04884101 -0.64933938 -0.70335777
## 1210 -0.5339449 1.6325740 -0.04424236 0.31984124 -0.01847084
## 1298 -0.5339449 1.6325740 -1.71857344 -1.06470250 -0.01847084
## 1498 -0.5339449 -0.6125297 -1.16046308 0.73520436 -0.70335777
## 1979 1.8728523 1.6325740 -0.60235272 -0.44165782 -0.70335777
## 2066 -0.5339449 -0.6125297 -0.15586443 0.45829561 -0.01847084
## 383 -0.5339449 -0.6125297 0.29062385 -1.82620155 -0.01847084
## 434 1.8728523 -0.6125297 -0.37910858 0.73520436 -0.70335777
## 843 -0.5339449 1.6325740 -0.93721894 0.25061405 0.66641609
## 865 -0.5339449 -0.6125297 0.96035628 0.45829561 0.66641609
## 922 -0.5339449 -0.6125297 -1.60695137 -0.92624812 -1.38824471
## 1418 -0.5339449 -0.6125297 2.29982115 -0.78779375 -0.01847084
## 1485 -0.5339449 -0.6125297 0.51386800 1.35824904 -1.38824471
## 1842 -0.5339449 -0.6125297 1.63008872 -0.02629470 2.03618995
## 2037 -0.5339449 1.6325740 0.62549007 0.18138686 -0.01847084
## 2052 -0.5339449 -0.6125297 -0.60235272 0.73520436 -0.01847084
## 371 -0.5339449 1.6325740 0.62549007 0.52752280 -0.01847084
## 376 -0.5339449 -0.6125297 -0.60235272 1.91206653 -0.70335777
## 377 1.8728523 1.6325740 1.29522250 -0.99547531 -1.38824471
## 506 -0.5339449 -0.6125297 -1.60695137 -0.64933938 1.48828040
## 728 1.8728523 -0.6125297 -0.26748651 0.31984124 -1.38824471
## 826 -0.5339449 -0.6125297 -0.04424236 -0.02629470 -0.01847084
## 1642 -0.5339449 -0.6125297 -0.26748651 1.70438497 -0.70335777
## 1707 -0.5339449 -0.6125297 -1.16046308 1.08134029 0.66641609
## 1751 -0.5339449 -0.6125297 -0.60235272 -1.27238406 -0.01847084
## 1864 -0.5339449 -0.6125297 -0.49073065 1.84283934 -0.70335777
## 1895 1.8728523 1.6325740 0.29062385 -0.44165782 -0.01847084
## 1998 1.8728523 1.6325740 0.40224593 -0.58011219 -0.01847084
## 31 1.8728523 1.6325740 -1.16046308 -0.71856656 -0.01847084
## 35 1.8728523 -0.6125297 -0.37910858 -0.44165782 -0.70335777
## 90 -0.5339449 -0.6125297 -1.71857344 -0.51088500 0.66641609
## 312 -0.5339449 1.6325740 0.40224593 -0.37243063 -0.70335777
## 457 -0.5339449 -0.6125297 -0.04424236 -1.41083843 -0.01847084
## 466 -0.5339449 -0.6125297 0.17900178 2.18897528 -0.70335777
## 870 1.8728523 -0.6125297 1.63008872 -1.13392968 -0.70335777
## 913 1.8728523 -0.6125297 -0.60235272 1.28902185 -0.70335777
## 1013 -0.5339449 -0.6125297 -0.04424236 1.35824904 -0.70335777
## 1117 -0.5339449 -0.6125297 -1.16046308 2.39665684 0.66641609
## 1306 -0.5339449 -0.6125297 -1.60695137 0.80443154 -0.70335777
## 1619 1.8728523 -0.6125297 -1.16046308 1.56593060 -1.11428993
## 1756 1.8728523 -0.6125297 0.51386800 -0.30320344 2.72107688
## 2026 1.8728523 -0.6125297 0.73711214 -0.78779375 -0.70335777
## 2028 -0.5339449 1.6325740 -0.04424236 0.11215968 0.66641609
## 2038 -0.5339449 -0.6125297 -0.37910858 -2.38001904 -0.01847084
## 2039 -0.5339449 -0.6125297 -1.04884101 0.25061405 -0.70335777
## 2043 -0.5339449 1.6325740 0.17900178 -0.37243063 -0.01847084
## 2227 1.8728523 1.6325740 0.40224593 2.39665684 0.66641609
## 162 -0.5339449 -0.6125297 1.96495493 0.31984124 -1.38824471
## 528 -0.5339449 -0.6125297 -0.60235272 0.87365873 -0.01847084
## 581 -0.5339449 -0.6125297 1.07197836 0.11215968 -0.70335777
## 600 -0.5339449 -0.6125297 2.74630943 -0.51088500 1.35130302
## 601 -0.5339449 1.6325740 0.73711214 0.04293249 0.66641609
## 814 -0.5339449 -0.6125297 0.29062385 -0.85702094 -0.15544823
## 1318 -0.5339449 -0.6125297 -1.16046308 -0.64933938 -0.70335777
## 1477 -0.5339449 -0.6125297 -0.26748651 -1.61851999 2.03618995
## 1982 -0.5339449 -0.6125297 0.51386800 -0.02629470 0.66641609
## 2029 1.8728523 1.6325740 0.62549007 0.18138686 -0.01847084
## 2034 -0.5339449 -0.6125297 0.17900178 -0.44165782 -0.01847084
## 2041 -0.5339449 -0.6125297 -0.93721894 -0.51088500 -0.70335777
## 2042 -0.5339449 -0.6125297 0.40224593 -0.99547531 0.66641609
## 2224 -0.5339449 -0.6125297 1.07197836 -0.44165782 -0.70335777
## 2229 -0.5339449 -0.6125297 -1.04884101 0.25061405 0.66641609
## 612 -0.5339449 1.6325740 -0.15586443 0.11215968 -0.56638039
## 703 1.8728523 -0.6125297 0.73711214 -0.51088500 0.66641609
## 916 -0.5339449 1.6325740 0.62549007 -0.02629470 -0.70335777
## 994 -0.5339449 -0.6125297 1.18360043 1.35824904 -0.01847084
## 1188 -0.5339449 -0.6125297 1.07197836 -0.78779375 1.35130302
## 1216 -0.5339449 1.6325740 -0.04424236 -0.64933938 0.66641609
## 1890 -0.5339449 -0.6125297 -0.37910858 1.15056748 -2.07313164
## 1991 -0.5339449 -0.6125297 -0.04424236 1.28902185 0.66641609
## 2031 -0.5339449 -0.6125297 0.17900178 -1.41083843 1.35130302
## 2032 -0.5339449 -0.6125297 -1.04884101 1.21979466 2.03618995
## 97 -0.5339449 1.6325740 2.74630943 1.21979466 1.89921256
## 201 -0.5339449 -0.6125297 0.96035628 0.52752280 -0.70335777
## 341 1.8728523 1.6325740 -0.93721894 -0.37243063 -0.70335777
## 717 -0.5339449 -0.6125297 -0.49073065 -0.85702094 -0.70335777
## 1364 -0.5339449 1.6325740 -1.71857344 0.11215968 0.94037086
## 1439 1.8728523 -0.6125297 -0.71397479 0.52752280 -0.84033516
## 1474 1.8728523 -0.6125297 -0.60235272 1.01211310 -0.01847084
## 1489 -0.5339449 -0.6125297 -1.60695137 1.49670341 0.66641609
## 1669 -0.5339449 -0.6125297 -1.16046308 -0.23397626 -0.01847084
## 1687 -0.5339449 -0.6125297 -1.04884101 -1.34161124 -1.38824471
## 1704 -0.5339449 1.6325740 -1.04884101 -0.09552188 -0.70335777
## 1706 -0.5339449 -0.6125297 -1.16046308 -0.85702094 -0.01847084
## 1780 -0.5339449 -0.6125297 -0.71397479 -0.02629470 -0.70335777
## 1984 -0.5339449 -0.6125297 -0.37910858 0.59674998 1.35130302
## 2022 -0.5339449 1.6325740 -0.04424236 0.87365873 -0.70335777
## 2025 -0.5339449 -0.6125297 0.96035628 0.25061405 0.66641609
## 2217 1.8728523 -0.6125297 0.40224593 -0.71856656 -0.01847084
## 2222 -0.5339449 -0.6125297 -1.04884101 -0.37243063 -0.70335777
## 135 1.8728523 -0.6125297 -0.15586443 0.66597717 -0.70335777
## 166 1.8728523 -0.6125297 0.62549007 -0.71856656 0.66641609
## 468 -0.5339449 -0.6125297 0.51386800 -0.16474907 -0.70335777
## 626 -0.5339449 -0.6125297 1.29522250 -0.37243063 -0.01847084
## 1031 -0.5339449 1.6325740 -0.49073065 -0.02629470 -0.01847084
## 1362 1.8728523 -0.6125297 -0.04424236 1.77361216 -1.38824471
## 1510 1.8728523 -0.6125297 -1.27208515 0.04293249 -1.38824471
## 1625 -0.5339449 -0.6125297 -0.60235272 0.38906842 -1.25126732
## 1696 -0.5339449 -0.6125297 -0.26748651 0.18138686 0.11850654
## 1699 -0.5339449 -0.6125297 0.06737971 0.94288592 1.35130302
## 1705 -0.5339449 -0.6125297 1.63008872 -1.41083843 -0.42940300
## 1715 -0.5339449 -0.6125297 0.51386800 -0.16474907 -1.11428993
## 1861 -0.5339449 1.6325740 -1.16046308 0.18138686 -0.01847084
## 1941 1.8728523 -0.6125297 -0.04424236 1.42747622 -1.38824471
## 1964 -0.5339449 -0.6125297 1.07197836 -0.99547531 -0.70335777
## 2218 -0.5339449 -0.6125297 -0.04424236 -1.41083843 -0.01847084
## 2228 -0.5339449 1.6325740 -0.60235272 -0.51088500 -0.70335777
## 2230 -0.5339449 -0.6125297 0.29062385 -1.54929280 -1.38824471
## 255 1.8728523 1.6325740 -0.37910858 -1.13392968 0.66641609
## 819 1.8728523 -0.6125297 -0.49073065 1.70438497 -0.42940300
## 917 -0.5339449 1.6325740 -0.26748651 1.91206653 -0.70335777
## 1085 -0.5339449 -0.6125297 0.51386800 0.73520436 -0.42940300
## 1187 -0.5339449 -0.6125297 -0.60235272 1.77361216 -1.25126732
## 1394 -0.5339449 -0.6125297 -0.49073065 0.31984124 -0.70335777
## 1479 -0.5339449 -0.6125297 -1.60695137 0.73520436 -0.70335777
## 1517 1.8728523 -0.6125297 -0.37910858 -1.48006562 -0.70335777
## 2009 -0.5339449 1.6325740 -0.82559686 1.01211310 1.35130302
## 2223 -0.5339449 -0.6125297 -0.15586443 1.42747622 -1.38824471
## 2225 -0.5339449 -0.6125297 -0.37910858 -0.44165782 2.72107688
## 2226 -0.5339449 -0.6125297 -0.93721894 1.15056748 -1.38824471
## 27 -0.5339449 1.6325740 0.96035628 0.25061405 -0.01847084
## 105 -0.5339449 -0.6125297 -1.16046308 -0.09552188 2.72107688
## 147 1.8728523 -0.6125297 -0.04424236 -0.30320344 -0.01847084
## 224 -0.5339449 -0.6125297 0.96035628 -0.85702094 -0.42940300
## 274 1.8728523 -0.6125297 0.06737971 -1.48006562 -0.70335777
## 356 1.8728523 1.6325740 -0.82559686 0.73520436 2.72107688
## 431 -0.5339449 -0.6125297 0.62549007 0.94288592 -0.01847084
## 551 -0.5339449 -0.6125297 0.06737971 0.04293249 -0.70335777
## 962 -0.5339449 1.6325740 -1.16046308 1.21979466 -0.70335777
## 997 -0.5339449 -0.6125297 3.41604186 0.87365873 -0.01847084
## 1389 1.8728523 1.6325740 -1.71857344 1.21979466 -0.01847084
## 1831 1.8728523 1.6325740 -0.60235272 -0.09552188 -0.01847084
## 1996 1.8728523 1.6325740 0.96035628 -1.20315687 2.03618995
## 1999 -0.5339449 -0.6125297 1.63008872 -2.24156467 1.35130302
## 2008 -0.5339449 -0.6125297 -0.60235272 -1.27238406 0.25548393
## 2216 1.8728523 -0.6125297 -0.93721894 0.25061405 -0.70335777
## 107 -0.5339449 1.6325740 0.29062385 -0.51088500 0.66641609
## 148 -0.5339449 -0.6125297 -1.27208515 -1.06470250 -0.01847084
## 182 -0.5339449 -0.6125297 -0.04424236 -0.99547531 1.35130302
## 414 -0.5339449 1.6325740 0.73711214 -1.34161124 -0.70335777
## 511 -0.5339449 1.6325740 -1.04884101 0.18138686 -1.38824471
## 699 -0.5339449 -0.6125297 -0.71397479 0.38906842 -0.01847084
## 1017 -0.5339449 1.6325740 -0.26748651 -0.85702094 -0.01847084
## 1366 -0.5339449 -0.6125297 -1.71857344 -0.78779375 2.72107688
## 1672 -0.5339449 1.6325740 -0.82559686 0.25061405 -0.70335777
## 1931 -0.5339449 -0.6125297 0.84873421 -0.51088500 1.35130302
## 1966 -0.5339449 -0.6125297 -0.49073065 -0.64933938 -0.70335777
## 1994 -0.5339449 -0.6125297 0.51386800 -1.41083843 2.03618995
## 2002 -0.5339449 -0.6125297 1.07197836 -0.92624812 -0.70335777
## 2007 -0.5339449 -0.6125297 2.18819907 1.21979466 -0.70335777
## 2 -0.5339449 -0.6125297 1.29522250 -1.61851999 -0.01847084
## 143 1.8728523 -0.6125297 -0.71397479 -0.64933938 -0.01847084
## 160 -0.5339449 -0.6125297 1.74171079 -1.96465592 -0.01847084
## 688 -0.5339449 1.6325740 0.73711214 -0.51088500 0.66641609
## 776 -0.5339449 -0.6125297 0.06737971 1.84283934 -0.01847084
## 850 -0.5339449 -0.6125297 -0.04424236 -1.89542874 1.35130302
## 1194 -0.5339449 1.6325740 -0.04424236 1.49670341 -0.70335777
## 1849 -0.5339449 -0.6125297 -0.49073065 -1.34161124 -0.70335777
## 5 -0.5339449 1.6325740 1.07197836 1.21979466 -0.01847084
## 55 1.8728523 -0.6125297 1.07197836 -0.58011219 -0.01847084
## 347 -0.5339449 -0.6125297 1.63008872 -1.96465592 -0.01847084
## 856 -0.5339449 -0.6125297 -0.60235272 -0.85702094 -0.01847084
## 1972 -0.5339449 -0.6125297 -0.04424236 -1.68774718 1.35130302
## 1977 -0.5339449 -0.6125297 0.29062385 -1.48006562 -1.38824471
## 1980 -0.5339449 -0.6125297 -1.38370722 -0.23397626 -1.38824471
## 1981 1.8728523 -0.6125297 1.07197836 -0.02629470 -0.70335777
## 1985 1.8728523 1.6325740 -1.16046308 0.25061405 -1.38824471
## 1987 -0.5339449 1.6325740 0.73711214 -0.16474907 0.66641609
## 1995 1.8728523 -0.6125297 -1.04884101 -0.44165782 -1.38824471
## 2207 -0.5339449 -0.6125297 -0.82559686 -1.13392968 -0.70335777
## 244 -0.5339449 1.6325740 -0.60235272 -0.09552188 -0.01847084
## 308 -0.5339449 -0.6125297 1.51846664 1.63515778 0.66641609
## 560 -0.5339449 -0.6125297 -0.04424236 0.59674998 -0.70335777
## 561 -0.5339449 -0.6125297 -1.27208515 0.94288592 1.35130302
## 1173 -0.5339449 -0.6125297 -1.04884101 0.04293249 0.66641609
## 1190 1.8728523 -0.6125297 -0.82559686 -2.03388311 -0.70335777
## 1269 1.8728523 -0.6125297 0.51386800 0.87365873 -0.01847084
## 1453 -0.5339449 -0.6125297 1.96495493 0.31984124 0.66641609
## 1543 -0.5339449 -0.6125297 -0.37910858 0.66597717 -1.38824471
## 1681 -0.5339449 -0.6125297 -1.27208515 -1.13392968 -0.70335777
## 1800 1.8728523 1.6325740 -1.27208515 -0.92624812 0.66641609
## 1873 -0.5339449 -0.6125297 -0.82559686 -1.61851999 0.66641609
## 1971 -0.5339449 -0.6125297 -1.16046308 0.31984124 1.21432563
## 1974 1.8728523 -0.6125297 1.74171079 -0.16474907 -1.38824471
## 130 -0.5339449 -0.6125297 -0.15586443 0.73520436 -0.01847084
## 721 1.8728523 -0.6125297 -0.60235272 0.38906842 -1.38824471
## 837 -0.5339449 -0.6125297 2.74630943 -0.09552188 -0.70335777
## 1741 -0.5339449 -0.6125297 0.06737971 0.66597717 -0.70335777
## 1860 1.8728523 1.6325740 -1.16046308 0.25061405 -1.38824471
## 1957 -0.5339449 -0.6125297 -0.37910858 1.15056748 -0.70335777
## 1960 -0.5339449 -0.6125297 -0.60235272 1.49670341 -0.70335777
## 1965 1.8728523 -0.6125297 -1.38370722 -0.44165782 -1.38824471
## 1968 1.8728523 -0.6125297 0.06737971 0.80443154 -0.01847084
## 58 1.8728523 1.6325740 -1.04884101 -1.13392968 0.66641609
## 195 1.8728523 -0.6125297 -0.04424236 1.15056748 -0.01847084
## 1228 -0.5339449 -0.6125297 -0.60235272 3.01970152 -0.70335777
## 1257 -0.5339449 -0.6125297 -0.04424236 0.18138686 -0.01847084
## 1592 -0.5339449 1.6325740 -0.76978583 1.49670341 -0.70335777
## 1596 -0.5339449 -0.6125297 -1.16046308 1.70438497 -0.70335777
## 1750 -0.5339449 -0.6125297 -1.49532930 -2.24156467 -0.70335777
## 1837 -0.5339449 -0.6125297 0.73711214 2.18897528 0.66641609
## 1942 1.8728523 -0.6125297 -1.27208515 -0.30320344 1.35130302
## 1961 -0.5339449 -0.6125297 -0.93721894 -0.92624812 1.35130302
## 1962 1.8728523 1.6325740 -1.27208515 -0.58011219 -0.70335777
## 1963 -0.5339449 1.6325740 0.51386800 -1.06470250 -0.01847084
## 422 -0.5339449 -0.6125297 -0.15586443 -1.48006562 0.66641609
## 1401 1.8728523 -0.6125297 0.51386800 0.52752280 -1.38824471
## 1525 -0.5339449 -0.6125297 0.40224593 -0.44165782 0.66641609
## 1646 -0.5339449 -0.6125297 -0.82559686 1.49670341 -0.70335777
## 1929 -0.5339449 -0.6125297 -0.04424236 -0.30320344 0.66641609
## 1940 -0.5339449 -0.6125297 0.51386800 1.56593060 -0.70335777
## 1945 -0.5339449 -0.6125297 -0.49073065 1.56593060 -1.38824471
## 1946 -0.5339449 -0.6125297 -0.60235272 -0.58011219 -0.70335777
## 1948 -0.5339449 -0.6125297 -0.26748651 -0.51088500 -1.38824471
## 1949 -0.5339449 -0.6125297 -0.37910858 0.11215968 -0.70335777
## 1950 -0.5339449 1.6325740 -0.26748651 -0.23397626 -0.01847084
## 1953 -0.5339449 -0.6125297 0.17900178 0.31984124 -0.01847084
## 1955 -0.5339449 -0.6125297 -0.04424236 2.11974809 -0.70335777
## 202 -0.5339449 -0.6125297 -1.04884101 -0.71856656 -2.07313164
## 494 -0.5339449 -0.6125297 0.62549007 0.18138686 -0.01847084
## 740 -0.5339449 1.6325740 -0.60235272 0.18138686 -0.70335777
## 780 -0.5339449 1.6325740 0.40224593 0.73520436 1.35130302
## 1069 -0.5339449 -0.6125297 -1.04884101 -1.20315687 2.03618995
## 1537 -0.5339449 -0.6125297 -0.60235272 -1.13392968 0.66641609
## 1920 -0.5339449 -0.6125297 -0.37910858 1.01211310 -0.70335777
## 1926 -0.5339449 -0.6125297 -0.26748651 0.25061405 -0.70335777
## 1938 -0.5339449 -0.6125297 0.84873421 2.18897528 0.66641609
## 1939 -0.5339449 -0.6125297 -1.04884101 -0.16474907 0.66641609
## 1947 -0.5339449 -0.6125297 -0.60235272 -0.64933938 0.66641609
## 1951 1.8728523 -0.6125297 -1.16046308 0.38906842 -1.38824471
## 25 -0.5339449 -0.6125297 0.06737971 1.70438497 -1.38824471
## 134 -0.5339449 1.6325740 0.96035628 0.45829561 -0.01847084
## 323 -0.5339449 1.6325740 0.17900178 0.45829561 -0.01847084
## 354 -0.5339449 -0.6125297 1.07197836 -0.99547531 -0.01847084
## 381 -0.5339449 -0.6125297 -1.16046308 0.31984124 -0.01847084
## 1568 -0.5339449 -0.6125297 1.18360043 1.63515778 1.35130302
## 1766 1.8728523 -0.6125297 -1.16046308 -1.13392968 -0.01847084
## 1782 1.8728523 -0.6125297 0.06737971 -0.37243063 -0.01847084
## 1903 -0.5339449 -0.6125297 -0.49073065 -0.99547531 -0.01847084
## 1923 -0.5339449 -0.6125297 1.85333286 -0.16474907 -0.01847084
## 1925 1.8728523 1.6325740 1.74171079 -0.51088500 -0.01847084
## 1930 -0.5339449 -0.6125297 -1.38370722 0.31984124 -0.70335777
## 1932 -0.5339449 -0.6125297 0.06737971 -0.09552188 -0.70335777
## 1934 -0.5339449 -0.6125297 0.84873421 0.73520436 1.35130302
## 385 -0.5339449 1.6325740 -0.04424236 0.18138686 -0.15544823
## 548 -0.5339449 -0.6125297 -1.16046308 -0.02629470 -0.70335777
## 1078 -0.5339449 1.6325740 0.06737971 -1.06470250 0.66641609
## 1429 -0.5339449 -0.6125297 -0.60235272 1.84283934 0.66641609
## 1581 -0.5339449 -0.6125297 -1.38370722 0.87365873 -0.29242562
## 1609 1.8728523 -0.6125297 -0.60235272 0.73520436 -1.11428993
## 1905 -0.5339449 -0.6125297 -0.04424236 -1.61851999 2.72107688
## 1908 -0.5339449 1.6325740 -1.49532930 1.15056748 -0.01847084
## 1916 1.8728523 -0.6125297 0.73711214 0.59674998 0.66641609
## 1918 -0.5339449 -0.6125297 3.30441979 2.11974809 -0.01847084
## 1919 -0.5339449 -0.6125297 -0.04424236 0.38906842 0.66641609
## 1921 1.8728523 -0.6125297 -0.15586443 0.38906842 -0.70335777
## 1927 -0.5339449 1.6325740 -1.04884101 0.38906842 -0.01847084
## 1933 1.8728523 1.6325740 0.29062385 0.25061405 -1.38824471
## 77 1.8728523 1.6325740 0.40224593 -0.37243063 0.66641609
## 184 -0.5339449 -0.6125297 0.62549007 0.45829561 -0.70335777
## 498 -0.5339449 -0.6125297 0.29062385 -0.09552188 -0.70335777
## 555 -0.5339449 -0.6125297 1.40684457 -1.34161124 -0.01847084
## 574 -0.5339449 -0.6125297 1.07197836 -2.03388311 2.72107688
## 783 -0.5339449 -0.6125297 -0.71397479 -0.58011219 0.66641609
## 956 -0.5339449 -0.6125297 -0.49073065 0.73520436 -0.01847084
## 1584 1.8728523 -0.6125297 -0.37910858 -0.78779375 -0.01847084
## 1797 1.8728523 -0.6125297 0.62549007 -0.23397626 1.35130302
## 1863 -0.5339449 -0.6125297 0.40224593 0.66597717 -0.70335777
## 1874 1.8728523 -0.6125297 -0.71397479 -1.41083843 1.35130302
## 1882 -0.5339449 -0.6125297 -0.04424236 0.80443154 -1.38824471
## 1898 -0.5339449 1.6325740 -0.60235272 -0.58011219 2.03618995
## 1911 1.8728523 -0.6125297 1.07197836 -0.64933938 -0.70335777
## 1913 1.8728523 -0.6125297 -1.04884101 -0.23397626 -0.70335777
## 1914 -0.5339449 -0.6125297 1.85333286 -0.44165782 -0.70335777
## 1915 1.8728523 -0.6125297 -1.16046308 -0.51088500 0.25548393
## 115 -0.5339449 -0.6125297 -0.71397479 -0.02629470 -0.01847084
## 259 1.8728523 -0.6125297 -0.04424236 -0.23397626 1.35130302
## 321 1.8728523 -0.6125297 -1.16046308 -0.92624812 -1.38824471
## 825 -0.5339449 1.6325740 3.41604186 1.35824904 2.72107688
## 1370 -0.5339449 -0.6125297 0.62549007 1.15056748 -0.70335777
## 1621 -0.5339449 -0.6125297 -1.49532930 -0.16474907 -0.01847084
## 1643 -0.5339449 -0.6125297 1.74171079 -0.44165782 -1.38824471
## 1688 -0.5339449 -0.6125297 -0.60235272 -0.85702094 1.35130302
## 1865 -0.5339449 -0.6125297 -0.60235272 -0.16474907 0.66641609
## 1880 -0.5339449 1.6325740 -0.04424236 -1.13392968 -1.11428993
## 1886 -0.5339449 -0.6125297 0.62549007 1.49670341 0.66641609
## 1889 1.8728523 1.6325740 -0.60235272 -0.09552188 -1.38824471
## 1892 -0.5339449 -0.6125297 -0.71397479 -1.41083843 -0.01847084
## 1897 -0.5339449 -0.6125297 -0.93721894 -0.09552188 -0.01847084
## 1899 -0.5339449 -0.6125297 -0.60235272 -1.61851999 2.03618995
## 1900 -0.5339449 -0.6125297 0.51386800 -0.37243063 0.66641609
## 1904 1.8728523 -0.6125297 -0.26748651 -1.34161124 -0.01847084
## 1909 -0.5339449 -0.6125297 -0.60235272 -0.02629470 0.66641609
## 83 -0.5339449 -0.6125297 -0.26748651 1.08134029 -0.70335777
## 208 -0.5339449 -0.6125297 -0.15586443 1.56593060 -0.70335777
## 329 -0.5339449 -0.6125297 -1.27208515 -0.99547531 -0.15544823
## 449 -0.5339449 -0.6125297 1.18360043 0.31984124 -0.70335777
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## 619 -0.5339449 -0.6125297 0.40224593 0.52752280 -0.70335777
## 778 -0.5339449 -0.6125297 0.73711214 -0.37243063 -0.01847084
## 1008 -0.5339449 -0.6125297 -1.04884101 -0.44165782 -0.70335777
## 1082 1.8728523 1.6325740 -0.04424236 -0.44165782 0.66641609
## 1732 -0.5339449 -0.6125297 0.62549007 -1.61851999 -1.38824471
## 1784 -0.5339449 -0.6125297 0.06737971 -0.64933938 1.35130302
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## 82 1.8728523 1.6325740 2.18819907 -0.71856656 0.66641609
## 258 1.8728523 -0.6125297 0.73711214 0.66597717 -0.70335777
## 310 1.8728523 1.6325740 -1.60695137 -0.23397626 -1.38824471
## 345 -0.5339449 -0.6125297 0.40224593 1.42747622 -0.01847084
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## 595 -0.5339449 -0.6125297 1.29522250 0.80443154 -1.38824471
## 676 -0.5339449 1.6325740 1.85333286 1.01211310 2.72107688
## 841 -0.5339449 -0.6125297 1.63008872 -0.64933938 -0.01847084
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## 1858 -0.5339449 -0.6125297 0.29062385 -0.51088500 0.66641609
## 1859 -0.5339449 -0.6125297 1.85333286 -1.61851999 1.35130302
## 1866 1.8728523 -0.6125297 -1.16046308 -0.44165782 -1.38824471
## 50 -0.5339449 -0.6125297 -0.04424236 -0.58011219 0.66641609
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## 250 -0.5339449 -0.6125297 0.96035628 -0.44165782 -0.01847084
## 349 1.8728523 -0.6125297 -1.38370722 0.80443154 1.35130302
## 396 1.8728523 -0.6125297 -0.60235272 -0.02629470 -0.01847084
## 868 -0.5339449 -0.6125297 1.85333286 -1.34161124 1.35130302
## 979 -0.5339449 -0.6125297 1.40684457 0.31984124 -0.70335777
## 1191 -0.5339449 -0.6125297 0.40224593 -1.13392968 1.35130302
## 1409 1.8728523 -0.6125297 -0.49073065 0.87365873 -0.70335777
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## 1832 -0.5339449 -0.6125297 0.73711214 0.80443154 -0.01847084
## 1836 1.8728523 1.6325740 -0.04424236 -0.51088500 -0.70335777
## 1839 -0.5339449 -0.6125297 0.29062385 -0.92624812 1.35130302
## 1841 -0.5339449 -0.6125297 0.73711214 1.91206653 -0.70335777
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## 1845 -0.5339449 -0.6125297 0.84873421 -1.13392968 -0.70335777
## 1848 -0.5339449 -0.6125297 1.74171079 1.08134029 -0.70335777
## 1851 1.8728523 -0.6125297 -1.04884101 0.11215968 -0.70335777
## 1852 -0.5339449 -0.6125297 -0.04424236 -0.92624812 0.66641609
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## 2208 -0.5339449 -0.6125297 1.63008872 0.59674998 -0.70335777
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## 715 -0.5339449 -0.6125297 0.51386800 0.52752280 -0.70335777
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## 1574 -0.5339449 1.6325740 -0.15586443 -1.41083843 0.66641609
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## 1825 1.8728523 -0.6125297 -0.49073065 0.52752280 -0.70335777
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## 1789 -0.5339449 -0.6125297 -0.82559686 0.52752280 0.66641609
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## 1737 -0.5339449 -0.6125297 0.51386800 -0.09552188 -0.70335777
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## 1754 -0.5339449 -0.6125297 -1.16046308 1.08134029 -1.38824471
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## 1730 -0.5339449 -0.6125297 -1.60695137 -0.30320344 -0.70335777
## 1734 1.8728523 1.6325740 -1.16046308 0.80443154 -0.70335777
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## 1745 -0.5339449 -0.6125297 -1.16046308 -0.37243063 -0.01847084
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## 1725 -0.5339449 -0.6125297 -0.60235272 0.45829561 -0.01847084
## 1726 -0.5339449 -0.6125297 0.96035628 1.77361216 1.35130302
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## 1679 -0.5339449 -0.6125297 1.74171079 -0.44165782 0.66641609
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## 1711 -0.5339449 -0.6125297 -0.60235272 -0.23397626 -0.70335777
## 1712 -0.5339449 -0.6125297 1.63008872 0.18138686 0.39246131
## 1713 -0.5339449 -0.6125297 -0.93721894 -0.85702094 -0.01847084
## 175 -0.5339449 1.6325740 -0.37910858 0.52752280 -1.38824471
## 603 1.8728523 -0.6125297 1.63008872 -0.23397626 0.66641609
## 730 -0.5339449 1.6325740 -1.04884101 1.15056748 0.66641609
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## 713 -0.5339449 -0.6125297 1.74171079 -2.10311030 -0.70335777
## 13 -0.5339449 -0.6125297 -0.37910858 -0.51088500 -0.01847084
## 427 -0.5339449 -0.6125297 -1.38370722 2.32742965 -0.56638039
## 444 -0.5339449 -0.6125297 -0.60235272 -0.16474907 1.35130302
## 495 -0.5339449 1.6325740 -1.04884101 -0.58011219 1.35130302
## 504 -0.5339449 -0.6125297 0.06737971 -0.44165782 0.66641609
## 543 -0.5339449 -0.6125297 -0.49073065 -0.16474907 -0.70335777
## 658 -0.5339449 -0.6125297 -0.60235272 -0.71856656 -0.01847084
## 673 -0.5339449 1.6325740 -0.71397479 -0.02629470 -1.38824471
## 686 -0.5339449 -0.6125297 0.96035628 0.59674998 -0.01847084
## 196 -0.5339449 -0.6125297 -0.49073065 -0.71856656 -0.01847084
## 221 -0.5339449 -0.6125297 0.06737971 1.08134029 -0.70335777
## 497 -0.5339449 -0.6125297 0.62549007 0.59674998 -0.01847084
## 503 -0.5339449 1.6325740 -0.93721894 -0.23397626 -0.29242562
## 516 -0.5339449 1.6325740 -0.37910858 -0.09552188 -0.70335777
## 637 -0.5339449 -0.6125297 0.17900178 -0.64933938 -0.01847084
## 656 -0.5339449 -0.6125297 -0.04424236 0.04293249 0.80339347
## 661 -0.5339449 -0.6125297 -1.04884101 -0.51088500 -0.01847084
## 664 -0.5339449 -0.6125297 1.74171079 -1.20315687 -0.01847084
## 671 -0.5339449 1.6325740 -0.26748651 1.15056748 -0.01847084
## 674 -0.5339449 1.6325740 -0.49073065 0.11215968 -1.38824471
## 678 -0.5339449 -0.6125297 0.06737971 0.87365873 1.35130302
## 683 -0.5339449 -0.6125297 -0.04424236 -0.44165782 2.03618995
## 206 -0.5339449 -0.6125297 -0.60235272 -0.37243063 -0.01847084
## 256 1.8728523 -0.6125297 0.29062385 -0.44165782 -0.29242562
## 453 -0.5339449 1.6325740 0.73711214 1.56593060 0.66641609
## 456 -0.5339449 -0.6125297 -0.37910858 0.52752280 0.66641609
## 509 1.8728523 -0.6125297 0.84873421 -1.13392968 2.03618995
## 544 -0.5339449 1.6325740 -0.26748651 -0.71856656 -0.15544823
## 627 -0.5339449 -0.6125297 0.84873421 -0.71856656 0.66641609
## 634 -0.5339449 1.6325740 -0.93721894 1.21979466 -0.01847084
## 641 -0.5339449 -0.6125297 0.62549007 1.08134029 -0.29242562
## 642 -0.5339449 -0.6125297 0.40224593 1.15056748 -0.84033516
## 643 -0.5339449 -0.6125297 2.18819907 -1.48006562 0.66641609
## 653 -0.5339449 -0.6125297 -1.16046308 -2.17233748 -0.70335777
## 654 1.8728523 -0.6125297 -0.15586443 0.94288592 -1.38824471
## 660 -0.5339449 -0.6125297 -0.26748651 -0.85702094 -0.15544823
## 4 -0.5339449 -0.6125297 0.62549007 1.35824904 0.66641609
## 121 -0.5339449 1.6325740 -0.49073065 -0.71856656 -0.01847084
## 338 -0.5339449 1.6325740 -0.60235272 -0.44165782 0.66641609
## 496 -0.5339449 -0.6125297 -1.38370722 1.15056748 -1.38824471
## 580 -0.5339449 -0.6125297 0.29062385 -0.23397626 1.21432563
## 608 -0.5339449 -0.6125297 0.06737971 1.35824904 2.03618995
## 615 -0.5339449 -0.6125297 0.73711214 0.94288592 -0.42940300
## 616 -0.5339449 -0.6125297 -0.60235272 -0.09552188 1.48828040
## 623 -0.5339449 1.6325740 0.73711214 -1.27238406 -0.01847084
## 782 -0.5339449 -0.6125297 0.40224593 1.28902185 -0.01847084
## peak.vo2 interval cad bun sodium
## 249 -0.693924484 -1.049514102 1.210566 -0.641684402 -0.4769220790
## 318 -1.738238721 -1.235503717 1.210566 4.227534282 -0.1526089192
## 631 0.862694849 0.991835338 1.210566 -0.422390595 0.5500655214
## 883 -0.161915345 -0.369064288 -0.826060 -0.057513334 0.2721011650
## 1095 -1.088005328 0.102714249 1.210566 0.520623700 -0.7713182513
## 1405 -0.201323429 -0.369064288 -0.826060 -0.960977431 -2.4228010377
## 1554 -0.812148738 -0.913424139 -0.826060 4.546827311 -2.4228010377
## 1589 0.508022090 0.719655413 -0.826060 -0.561861145 -0.4769220790
## 1708 -1.620014468 -1.598410285 1.210566 0.954780740 -3.0714273572
## 1773 0.074533161 0.080032588 -0.826060 -1.040800688 -0.4769220790
## 1850 -1.107709371 -1.616555613 1.210566 3.588948225 -2.7471141974
## 1935 0.074533161 0.733264409 1.210566 -0.960977431 0.1717042406
## 2077 -0.693924484 -1.675527930 -0.826060 -0.242568117 0.8203305602
## 110 -1.541198299 -1.049514102 1.210566 2.152129597 -0.4769220790
## 116 0.173053372 0.447475488 -0.826060 -0.265705064 -0.5236517622
## 328 -0.654516400 -0.505154251 -0.826060 2.435139694 -0.7683400641
## 482 -1.186525539 -0.232974326 1.210566 1.825693057 -0.2163776266
## 723 0.862694849 0.855745376 -0.826060 -0.451209294 -0.0260617199
## 732 -0.457475978 -0.505154251 -0.826060 0.218509266 0.2691675741
## 737 -1.935279143 -1.979462180 -0.826060 0.813386626 0.0370445520
## 802 -1.541198299 -1.503147311 -0.826060 1.664010650 -0.0957450984
## 894 -0.989485117 -0.641244214 1.210566 0.436318974 0.3371765755
## 1204 -1.836758932 -1.593873952 1.210566 6.462585482 -1.7741747181
## 1564 -1.127413413 -1.185604064 1.210566 0.316194683 -0.4769220790
## 1632 -0.950077033 -0.641244214 1.210566 0.076724912 -0.4769220790
## 1794 -1.620014468 -1.339839355 1.210566 1.433720283 0.4960174004
## 2120 -1.974687227 -1.185604064 -0.826060 2.231952854 -4.0443668366
## 2127 -1.324453835 -1.299012367 -0.826060 1.034603997 -0.4769220790
## 3 -0.989485117 -1.253649046 -0.826060 0.371259290 0.1187684554
## 188 0.626246343 0.311385525 -0.826060 -0.466254413 0.4130236326
## 320 -1.068301286 -1.607482949 -0.826060 0.354270160 0.1496772962
## 539 0.173053372 -0.704752863 -0.826060 -0.162744860 -1.7741747181
## 632 -1.363861919 -1.807081561 -0.826060 0.635487712 0.1717042406
## 712 -0.398363851 -1.289939703 -0.826060 0.260539608 -0.4281594484
## 808 -0.772740653 -1.321694027 1.210566 1.671219097 -0.4785987099
## 1081 -0.871260864 -1.435102329 -0.826060 0.112358400 -0.4116439791
## 1232 -1.580606383 -1.457783990 -0.826060 4.626650568 -1.7741747181
## 1313 -1.068301286 -1.457783990 1.210566 0.197168570 0.1402657250
## 1674 -1.442678088 -1.602946617 1.210566 -0.322391374 -5.0173063159
## 1717 -1.344157877 -1.503147311 1.210566 3.349478454 -2.7471141974
## 1727 -1.541198299 -1.770790904 1.210566 -0.721507659 -0.4769220790
## 2104 -1.225933624 -0.931569467 1.210566 0.795134226 -0.4769220790
## 2154 -0.299843640 0.991835338 1.210566 -0.242568117 0.1717042406
## 2181 -1.186525539 -0.369064288 1.210566 2.152129597 -0.8012352387
## 2197 -1.817054890 -1.553046964 -0.826060 0.316194683 -0.8012352387
## 445 -1.403270004 -0.831770162 -0.826060 0.236371426 0.8203305602
## 777 -0.831852780 -1.185604064 1.210566 0.510033180 -0.4642852426
## 800 -1.028893202 -1.185604064 -0.826060 -0.801330916 -2.7471141974
## 904 0.842990807 1.300305920 -0.826060 -0.414540732 0.6962904638
## 906 -1.166821497 -0.845379158 1.210566 0.482846452 0.1875555555
## 933 -0.536292147 -0.641244214 1.210566 0.542034175 -0.1369259002
## 1155 0.724766554 0.447475488 1.210566 -0.280619464 0.3791838408
## 1162 -0.950077033 -1.185604064 -0.826060 1.912659826 2.1175831993
## 1424 -1.639718510 -1.489538314 1.210566 0.795134226 2.1175831993
## 1664 -0.339251725 -0.096884363 1.210566 0.015762907 -0.3555959421
## 2000 -0.536292147 -0.641244214 -0.826060 1.912659826 -0.8012352387
## 2035 -1.048597244 -0.958787460 1.210566 0.711284228 -0.1695319950
## 186 -0.871260864 -0.641244214 1.210566 -0.322391374 1.1446437200
## 375 0.330685710 0.547274793 -0.826060 0.135608416 0.0911609149
## 792 -0.812148738 -1.117559083 -0.826060 1.135552767 -0.4433520833
## 957 -1.127413413 -1.035905106 1.210566 0.628917596 0.0312427841
## 1014 -1.028893202 -0.369064288 1.210566 1.163037244 -0.1880021872
## 1112 -1.462382130 -0.913424139 1.210566 4.467004054 -2.0984878779
## 1253 -1.304749793 -0.831770162 1.210566 -0.082921602 -0.4769220790
## 1420 -0.910668949 -0.672998538 1.210566 -0.522383913 0.3985394696
## 1988 -1.166821497 -1.276330706 -0.826060 -0.082921602 -0.8012352387
## 88 -0.339251725 0.796773058 1.210566 0.275320240 0.2452810050
## 117 -0.122507261 -0.346382628 -0.826060 6.143292453 -0.1526089192
## 229 -0.831852780 -1.185604064 -0.826060 1.513543540 -1.7741747181
## 512 -1.068301286 -1.185604064 -0.826060 -0.111482702 -0.2401128517
## 583 -1.383565961 -1.185604064 1.210566 5.584529653 -2.0984878779
## 648 -0.516588105 -0.096884363 -0.826060 0.399078459 0.2202030067
## 1058 -1.147117455 -0.759188848 -0.826060 -0.162744860 -2.4228010377
## 1203 -0.142211303 -0.314628303 -0.826060 -0.881154174 1.1446437200
## 1225 -0.457475978 -0.913424139 -0.826060 0.954780740 1.7932700395
## 1415 -1.048597244 -0.976932788 1.210566 0.076724912 2.7662095189
## 1438 -1.442678088 -0.641244214 1.210566 1.114427254 -1.7741747181
## 1442 -0.969781075 -1.072195762 -0.826060 -0.242568117 0.1717042406
## 1824 -0.221027472 -0.096884363 1.210566 1.353897026 -0.8012352387
## 2178 -1.541198299 -1.444174994 1.210566 0.795134226 -2.4228010377
## 200 -1.580606383 -2.101943146 -0.826060 0.586570321 -0.3542692321
## 460 -1.403270004 -1.462320322 -0.826060 0.528913747 -1.0238251825
## 490 -0.221027472 -1.040441438 1.210566 0.829606155 -0.0684988785
## 505 -1.048597244 -0.777334176 -0.826060 0.455416733 0.6338229461
## 636 0.330685710 0.719655413 -0.826060 -0.194418247 0.4703422378
## 877 -1.954983185 -1.562119628 1.210566 0.912571268 -0.1396135352
## 885 -0.201323429 -0.913424139 -0.826060 -0.960977431 -0.8012352387
## 2214 -0.753036611 -1.299012367 -0.826060 3.908241254 -2.0984878779
## 157 -2.171727649 -1.630164609 -0.826060 1.753013311 -0.1526089192
## 170 -0.831852780 -0.096884363 -0.826060 1.556737585 -0.4113112792
## 317 -1.009189160 -0.464327262 1.210566 3.349478454 -1.1255483985
## 399 -0.004283007 0.175295562 -0.826060 0.290414853 0.2213276210
## 489 5.355216470 0.447475488 1.210566 -0.003098345 0.8203305602
## 552 -1.403270004 -1.235503717 -0.826060 0.339559283 0.1709368249
## 652 -1.442678088 -1.670991598 -0.826060 -0.058665973 0.4744651953
## 876 -0.280139598 -0.473399927 -0.826060 1.277877126 -0.3949955998
## 1573 0.389797836 0.175295562 1.210566 -0.561861145 -0.8012352387
## 1993 -0.221027472 -0.800015837 1.210566 -0.082921602 0.1717042406
## 2040 -1.639718510 -1.689136926 -0.826060 0.316194683 -2.4228010377
## 10 -0.240731514 -0.219365330 -0.826060 1.194250512 0.4960174004
## 238 -0.713628527 -1.457783990 1.210566 0.037792156 0.2040312510
## 360 -0.792444695 -0.913424139 -0.826060 1.470794546 -0.6624627014
## 416 -1.166821497 -1.085804759 -0.826060 0.677888864 0.3353765597
## 733 -1.521494257 -1.575728624 -0.826060 0.200894842 -0.0938702472
## 967 -1.403270004 -1.253649046 1.210566 0.532774962 -0.2344276777
## 968 -1.048597244 -0.777334176 -0.826060 0.550056088 0.2718885453
## 1134 -0.890964906 -0.232974326 -0.826060 -0.082921602 0.8203305602
## 1135 -0.477180020 0.007451275 1.210566 0.344821235 0.2421125595
## 1183 -0.457475978 -0.164929344 -0.826060 -0.561861145 0.8203305602
## 1184 -0.299843640 -0.228437994 -0.826060 -0.402214631 -0.4769220790
## 1686 0.192757414 0.447475488 1.210566 -1.200447202 -1.1255483985
## 2069 -0.102803218 -1.639237273 1.210566 1.606841534 -0.0008356013
## 2204 -1.265341708 -1.004150781 -0.826060 0.635487712 0.4960174004
## 38 1.158255482 1.808375114 -0.826060 -0.697277880 0.4532171441
## 392 0.113941246 0.002914943 -0.826060 0.236371426 0.1717042406
## 443 -0.280139598 0.066423592 -0.826060 4.227534282 -0.4769220790
## 532 -0.516588105 -0.210292665 -0.826060 0.393329299 0.0140374489
## 647 -1.304749793 -1.330766691 1.210566 1.368353004 -0.2158901904
## 724 0.448909963 -0.096884363 -0.826060 -0.272063857 -0.2553604284
## 1128 -0.654516400 -0.332773632 1.210566 -0.721507659 0.8203305602
## 1337 -0.122507261 0.447475488 -0.826060 -0.082921602 0.1717042406
## 1593 0.567134216 0.447475488 1.210566 0.134771571 -0.4105952185
## 1777 -0.240731514 -0.346382628 1.210566 0.555664455 -0.8012352387
## 8 -0.063395134 -0.096884363 -0.826060 0.171513492 0.1413939586
## 222 -0.221027472 0.030132935 1.210566 0.076724912 -2.4228010377
## 228 2.123753550 2.080555039 -0.826060 -0.047034713 0.0419669220
## 313 -0.910668949 -1.339839355 1.210566 0.316194683 -1.7741747181
## 342 -0.280139598 0.039205600 1.210566 0.227726503 0.0503992198
## 350 -1.206229582 -0.763725180 1.210566 1.302331740 -0.0930863337
## 459 1.572040368 1.808375114 1.210566 -1.429911809 0.5503892475
## 573 -0.457475978 -0.990541785 -0.826060 -0.108964250 0.3513907900
## 771 -0.772740653 -0.917960471 -0.826060 0.289174292 0.0106896095
## 788 -0.634812358 -0.232974326 -0.826060 1.814840500 -0.6204215973
## 789 -1.462382130 -0.976932788 1.210566 0.027261929 -0.3501481913
## 886 -0.004283007 -0.096884363 1.210566 0.130746849 -0.1644008873
## 1029 -1.383565961 -1.367057348 1.210566 0.954780740 -1.1255483985
## 1067 -1.560902341 -2.065652490 -0.826060 2.710892397 -4.6929931561
## 1143 -0.339251725 0.356748846 -0.826060 -1.040800688 -0.4769220790
## 1251 -1.245637666 -1.471392986 1.210566 0.246820233 0.0213235461
## 1360 -1.344157877 -1.049514102 1.210566 0.316194683 -1.4498615583
## 1547 -0.930372991 -1.049514102 -0.826060 1.114427254 -1.1255483985
## 1906 1.099143356 0.284167532 -0.826060 -0.561861145 -1.4498615583
## 2187 -0.496884062 -0.595880893 -0.826060 0.316194683 -0.1526089192
## 2193 0.113941246 -0.641244214 -0.826060 -0.482037888 -0.4769220790
## 2199 0.212461457 0.161686566 -0.826060 -1.040800688 -0.4769220790
## 74 -1.600310426 -1.525828971 -0.826060 -0.242568117 0.4960174004
## 241 0.980919102 2.080555039 -0.826060 -0.801330916 -1.4498615583
## 348 1.591744411 0.991835338 1.210566 -0.107494877 0.3927772290
## 442 -0.615108316 -1.457783990 1.210566 0.795134226 -0.4769220790
## 470 -0.102803218 0.125395909 1.210566 0.635487712 0.1717042406
## 474 0.488318047 1.400105226 -0.826060 -1.280270459 -1.1255483985
## 524 -1.462382130 -1.090341091 1.210566 0.296946815 -0.4064008375
## 711 -1.639718510 -1.321694027 1.210566 0.824586506 -0.0872079014
## 1299 -0.102803218 -0.423500274 1.210566 -0.322391374 -0.4769220790
## 1515 -0.930372991 -1.049514102 -0.826060 -0.402214631 0.1717042406
## 1624 -0.969781075 -0.641244214 1.210566 2.631069140 -1.7741747181
## 1736 -0.063395134 -0.323700968 -0.826060 -0.482037888 -0.1526089192
## 2044 0.173053372 0.515520469 -0.826060 1.673190054 -0.4769220790
## 2067 1.709968664 1.259478931 1.210566 -0.282755899 0.3309464127
## 2183 -0.457475978 0.039205600 1.210566 -0.641684402 -0.1526089192
## 2185 -0.319547683 -0.568662900 1.210566 0.475841197 -0.8012352387
## 2189 0.389797836 0.647074099 1.210566 0.156548169 -0.1526089192
## 2190 0.015421035 0.461084484 -0.826060 0.156548169 -0.1526089192
## 2191 -1.363861919 -0.908887807 1.210566 0.236371426 0.4960174004
## 2192 -0.280139598 -0.976932788 -0.826060 -0.242568117 0.1717042406
## 7 -2.014095312 -1.616555613 -0.826060 -1.519740231 -1.1255483985
## 62 -1.206229582 -1.943171523 1.210566 2.231952854 -1.4498615583
## 169 -0.831852780 -1.530365303 1.210566 -0.641684402 -1.1255483985
## 305 0.547430174 -0.096884363 1.210566 -1.200447202 0.4960174004
## 327 -0.871260864 -0.913424139 1.210566 0.236371426 -0.8012352387
## 542 1.414408031 2.080555039 -0.826060 -1.107436383 0.3206564199
## 613 -0.516588105 -0.854451822 1.210566 0.085143442 0.4453828667
## 635 0.842990807 0.719655413 -0.826060 -0.439063871 0.7936219834
## 812 -1.580606383 -0.995078117 -0.826060 -0.402214631 2.1175831993
## 903 -1.009189160 -0.641244214 1.210566 6.143292453 -3.0714273572
## 1355 -1.856462974 -1.661918934 -0.826060 -0.162744860 1.4689568797
## 1422 0.035125077 -0.160393012 1.210566 -0.322391374 0.4960174004
## 1509 1.177959524 0.787700394 -0.826060 1.912659826 0.4960174004
## 1601 -0.102803218 -0.577735565 1.210566 -0.561861145 0.8203305602
## 2175 -0.339251725 0.175295562 -0.826060 -0.801330916 0.4960174004
## 2177 1.059735271 1.400105226 -0.826060 -0.561861145 -1.4498615583
## 2184 -0.358955767 -0.505154251 1.210566 -0.322391374 1.1446437200
## 2186 -1.088005328 -1.534901635 1.210566 -1.200447202 -1.4498615583
## 2188 -0.181619387 -0.301019307 1.210566 -0.082921602 0.1717042406
## 174 -0.753036611 -0.641244214 1.210566 -0.561861145 -0.4769220790
## 253 -1.127413413 -0.641244214 -0.826060 -0.402214631 0.4960174004
## 286 -1.166821497 -0.899815143 1.210566 0.156548169 -1.4498615583
## 492 -0.102803218 0.447475488 -0.826060 1.099300605 0.0073406156
## 534 -1.245637666 -1.185604064 1.210566 0.521098798 -0.5511382170
## 607 -0.378659809 -1.144777076 -0.826060 -0.402214631 -0.1526089192
## 614 -0.083099176 0.447475488 1.210566 0.196920409 0.1757106692
## 663 -1.009189160 -1.185604064 1.210566 0.242082638 -0.3787407194
## 964 -1.186525539 -1.321694027 1.210566 0.567482844 -0.1343210363
## 1066 -0.831852780 -0.482472591 1.210566 -0.082921602 -2.4228010377
## 1433 0.035125077 0.583565450 1.210566 -1.280270459 1.4689568797
## 1524 -0.634812358 -1.543974299 -0.826060 1.353897026 0.8203305602
## 1555 -0.753036611 -0.096884363 1.210566 -0.561861145 1.4689568797
## 1675 0.094237203 -0.096884363 1.210566 0.442095989 -0.8498143178
## 1907 -0.812148738 -0.709289195 1.210566 3.588948225 -3.0714273572
## 2021 -1.560902341 -1.439638661 -0.826060 -0.881154174 0.4960174004
## 2113 -0.083099176 -0.096884363 -0.826060 -0.801330916 0.4960174004
## 2129 -1.068301286 -0.641244214 1.210566 4.387180796 0.1717042406
## 2170 0.921806976 0.021060271 1.210566 -0.322391374 0.8203305602
## 2172 0.724766554 1.082561980 1.210566 -0.645880293 0.6193139407
## 2173 0.251869541 0.197977223 -0.826060 -0.801330916 2.4418963591
## 2198 -0.378659809 -0.205756333 1.210566 0.715310969 -3.0714273572
## 2200 0.724766554 0.765018734 -0.826060 0.555664455 -2.7471141974
## 2201 -0.457475978 -0.432572938 1.210566 -0.554071588 -0.6419631585
## 1 -0.851556822 -0.577735565 1.210566 -0.003098345 0.4960174004
## 46 -0.023987050 0.266022204 -0.826060 -0.402214631 -1.1255483985
## 103 -0.358955767 -0.096884363 1.210566 1.230185693 -0.1831694217
## 220 -1.147117455 -1.185604064 1.210566 -0.801330916 0.1717042406
## 236 -0.102803218 -0.278337647 1.210566 0.076724912 -1.1255483985
## 514 -0.615108316 -0.890742479 -0.826060 0.112994814 0.1535050736
## 638 -0.851556822 -0.777334176 1.210566 0.633053740 -0.0363090072
## 1520 -1.580606383 -1.426029665 1.210566 2.790715654 0.4960174004
## 1722 -1.895871058 -1.897808202 1.210566 -0.881154174 -0.8719009650
## 1877 -0.950077033 -0.913424139 -0.826060 1.194250512 -1.4498615583
## 2145 -0.023987050 0.447475488 -0.826060 0.076724912 1.1446437200
## 2146 -0.831852780 -0.119566024 1.210566 -0.641684402 0.1717042406
## 172 0.468614005 -0.369064288 1.210566 -0.061798463 -0.0192864613
## 269 -0.890964906 -1.185604064 1.210566 0.316194683 2.7662095189
## 270 -1.147117455 -1.321694027 1.210566 -0.561861145 -0.8012352387
## 291 -0.792444695 -0.024303050 1.210566 0.156548169 -1.1255483985
## 343 -0.890964906 -0.096884363 -0.826060 1.912659826 -0.4769220790
## 707 -0.792444695 -1.249112714 -0.826060 0.924928429 -0.0532348753
## 745 -0.516588105 -0.641244214 1.210566 1.219598313 -0.5806686523
## 919 -1.344157877 -1.376130012 -0.826060 -0.143063008 -0.3378123273
## 1293 -1.797350848 -0.913424139 1.210566 -0.561861145 1.7932700395
## 1338 -0.358955767 -0.641244214 -0.826060 0.167789220 -0.1338320596
## 1952 0.448909963 0.297776529 1.210566 -0.003098345 1.1446437200
## 1975 2.044937381 0.769555066 -0.826060 -0.162744860 -0.1526089192
## 2108 0.330685710 -0.096884363 -0.826060 -0.960977431 0.4960174004
## 2156 1.788784833 2.311907976 -0.826060 -0.249306469 0.2373580572
## 2164 -0.142211303 0.991835338 -0.826060 -0.003098345 -1.1255483985
## 2171 -0.595404273 -0.096884363 1.210566 0.475841197 0.8203305602
## 2195 -0.358955767 0.025596603 1.210566 -0.881154174 -0.4769220790
## 237 0.350389752 0.447475488 -0.826060 -0.242568117 -0.4769220790
## 363 -0.122507261 -0.096884363 1.210566 0.148096582 -0.1075592073
## 411 -0.516588105 -0.301019307 -0.826060 -0.043006890 0.2046700344
## 806 1.158255482 0.991835338 -0.826060 0.749173811 0.1269233390
## 1854 -0.753036611 -1.521292639 -0.826060 -0.322391374 -0.1526089192
## 1879 -0.083099176 -0.255655986 1.210566 1.433720283 -0.1526089192
## 2015 -0.575700231 0.447475488 1.210566 0.236371426 -0.1526089192
## 2018 0.409501879 -0.164929344 1.210566 1.912659826 -0.8012352387
## 2056 0.803582723 0.579029118 -0.826060 -0.402214631 -1.1255483985
## 2081 0.035125077 0.719655413 -0.826060 -1.200447202 -0.1526089192
## 2132 0.133645288 0.311385525 1.210566 -0.322391374 -0.1526089192
## 2158 -0.575700231 -0.641244214 1.210566 2.391599368 0.8203305602
## 2159 -1.935279143 -1.779863568 -0.826060 -0.003098345 0.4960174004
## 2163 -0.378659809 -0.768261512 -0.826060 -0.402214631 0.8203305602
## 2165 -1.422974046 -1.185604064 -0.826060 0.076724912 -0.1526089192
## 2168 -0.910668949 -0.641244214 -0.826060 -0.162744860 -0.4769220790
## 2169 -1.679126594 -1.267258042 -0.826060 0.874957483 1.1446437200
## 2196 -1.225933624 -0.577735565 1.210566 0.954780740 -1.4498615583
## 161 0.173053372 0.279631200 -0.826060 -0.003098345 -0.8012352387
## 180 -1.383565961 -1.185604064 1.210566 0.316194683 -1.4498615583
## 367 -0.516588105 -0.369064288 -0.826060 -0.402214631 0.8203305602
## 685 -0.181619387 -0.301019307 -0.826060 1.387554577 0.0702586529
## 990 -0.831852780 -0.641244214 -0.826060 1.102577259 0.0859397163
## 1086 -1.304749793 -0.437109270 1.210566 0.735162977 -0.0575947403
## 1100 -1.068301286 -0.804552169 -0.826060 -0.045075490 0.2656275078
## 1969 -0.477180020 -0.641244214 1.210566 2.311776111 -0.4769220790
## 2135 1.138551440 0.583565450 -0.826060 -1.120623945 2.1175831993
## 2153 -0.536292147 -1.289939703 1.210566 -0.641684402 0.4960174004
## 2155 1.709968664 1.740330133 1.210566 -0.561861145 -0.1526089192
## 2166 -1.462382130 -1.643773605 -0.826060 0.316194683 1.1446437200
## 223 0.133645288 0.288703864 1.210566 0.316194683 -0.8012352387
## 681 -1.028893202 -0.836306494 1.210566 -0.265726415 -0.0758862944
## 963 -1.048597244 -0.096884363 1.210566 0.939452235 -0.8478268554
## 1192 0.330685710 0.447475488 -0.826060 -0.368547057 -0.3258421718
## 1500 1.453816115 2.624914890 1.210566 -1.200447202 0.8203305602
## 1522 -1.521494257 -1.607482949 1.210566 1.114427254 0.8203305602
## 1523 -0.260435556 -1.158386072 1.210566 0.316194683 0.4960174004
## 1545 -0.299843640 -0.369064288 1.210566 1.832836569 -0.4769220790
## 1560 -1.580606383 -1.729963915 -0.826060 0.316194683 1.1446437200
## 2019 2.182865676 0.447475488 1.210566 -0.003098345 -0.4769220790
## 2095 -1.560902341 -1.457783990 -0.826060 -0.721507659 1.7932700395
## 2121 -1.639718510 -1.258185378 -0.826060 -0.960977431 0.1717042406
## 2147 -1.344157877 -1.267258042 -0.826060 0.156548169 -1.7741747181
## 2151 -0.536292147 -0.913424139 1.210566 -0.242568117 0.8203305602
## 2157 0.310981668 -0.133175020 1.210566 -1.040800688 0.1717042406
## 2231 -0.004283007 0.447475488 -0.826060 -0.801330916 -0.8012352387
## 112 -1.639718510 -1.471392986 -0.826060 -0.003098345 -2.0984878779
## 176 -0.753036611 -0.986005453 1.210566 -0.162744860 0.8203305602
## 219 -0.733332569 -0.096884363 -0.826060 1.034603997 1.7932700395
## 382 -0.772740653 -0.890742479 1.210566 0.738402573 0.0698343605
## 684 -0.063395134 -0.709289195 -0.826060 -0.244598004 0.0475512670
## 1268 1.217367609 1.794766118 -0.826060 -1.120623945 0.4960174004
## 1617 0.074533161 -0.908887807 -0.826060 0.156548169 -0.1526089192
## 1786 -0.812148738 -0.369064288 1.210566 0.874957483 -1.1255483985
## 2143 -0.023987050 0.315921857 -0.826060 -0.721507659 -1.4498615583
## 2148 0.685358469 1.100707308 -0.826060 0.236371426 0.4960174004
## 2152 -1.659422552 -2.052043493 -0.826060 0.475841197 -1.4498615583
## 98 -0.122507261 -0.096884363 -0.826060 -0.641684402 -1.4498615583
## 99 -0.753036611 -1.185604064 1.210566 0.621600583 -0.6587949953
## 209 -0.969781075 -1.081268426 -0.826060 -0.881154174 1.1446437200
## 254 -1.265341708 -0.232974326 1.210566 0.644350596 -0.2401691168
## 331 0.350389752 0.107250581 -0.826060 -0.322391374 0.1717042406
## 357 -0.516588105 -0.573199232 1.210566 0.289683943 -0.0970827377
## 410 0.153349330 0.447475488 -0.826060 0.396017940 0.4960174004
## 1426 -0.753036611 -0.709289195 1.210566 3.429301711 1.4689568797
## 1709 0.626246343 0.057350928 1.210566 -0.242568117 -0.4769220790
## 1746 -0.358955767 -0.096884363 -0.826060 0.156548169 -0.1526089192
## 1796 0.192757414 0.447475488 -0.826060 -0.322391374 0.1717042406
## 2118 0.310981668 0.610783443 1.210566 -0.641684402 1.1446437200
## 2126 -1.817054890 -1.870590210 1.210566 0.283728832 -0.8044480276
## 2134 -0.792444695 -1.031368773 1.210566 3.509124968 -0.8012352387
## 2136 -1.028893202 -0.913424139 1.210566 0.236371426 0.4960174004
## 2137 1.276479735 0.338603517 -0.826060 -0.082921602 0.4960174004
## 2138 -1.324453835 -1.185604064 -0.826060 0.715310969 -4.0443668366
## 2141 -1.462382130 -1.063123098 -0.826060 0.076724912 -0.8012352387
## 2142 2.241977803 0.687901088 1.210566 -0.801330916 -0.4769220790
## 2144 0.389797836 0.129932241 -0.826060 -0.242568117 -0.4769220790
## 2220 -1.915575101 -1.693673258 1.210566 0.555664455 -0.8012352387
## 64 -0.122507261 -0.096884363 -0.826060 -0.561861145 -0.8012352387
## 261 -1.442678088 -1.262721710 1.210566 0.316194683 -1.1255483985
## 478 -1.028893202 -0.745579852 1.210566 0.839224037 -0.4113842692
## 485 -0.201323429 0.447475488 1.210566 -0.322391374 -1.7741747181
## 604 -1.186525539 -0.954251128 1.210566 1.042995453 -0.2435903547
## 828 -0.851556822 -0.981469120 1.210566 1.005644671 -0.4115125026
## 1392 -0.477180020 -0.641244214 1.210566 0.555664455 0.1717042406
## 1463 0.113941246 -0.242046990 -0.826060 0.715310969 0.4960174004
## 1647 2.576946520 1.536195189 -0.826060 -0.402214631 -0.8012352387
## 1881 -1.068301286 -1.394275341 1.210566 0.156548169 1.7932700395
## 2124 -1.915575101 -1.480465650 -0.826060 2.391599368 -2.4228010377
## 2131 1.177959524 1.536195189 -0.826060 0.795134226 1.1446437200
## 2140 -0.930372991 -1.049514102 1.210566 -0.003098345 -1.7741747181
## 2213 -0.772740653 0.447475488 -0.826060 -0.082921602 -1.4498615583
## 159 -0.161915345 -0.232974326 1.210566 0.221057046 -0.0187249001
## 454 -1.068301286 -1.049514102 1.210566 -0.003098345 -0.8012352387
## 458 -0.221027472 -0.096884363 -0.826060 0.277666878 -0.9077990395
## 475 -1.009189160 -0.437109270 1.210566 2.311776111 -3.7200536768
## 493 0.429205921 0.415721163 1.210566 0.032624811 0.0963645391
## 939 -0.713628527 -0.505154251 1.210566 0.465122999 0.0258141937
## 1150 -1.836758932 -1.480465650 -0.826060 0.782422572 -0.5011570447
## 1213 -0.753036611 -0.641244214 1.210566 -1.040800688 0.1717042406
## 1264 -1.225933624 -0.777334176 1.210566 -0.881154174 0.4960174004
## 1300 -1.245637666 -1.049514102 1.210566 1.194250512 0.8203305602
## 1562 -1.521494257 -0.836306494 -0.826060 5.345059882 1.4689568797
## 1661 -0.477180020 -0.800015837 -0.826060 0.076724912 -0.8012352387
## 1776 -1.462382130 -0.595880893 1.210566 -0.721507659 1.4689568797
## 2112 -1.225933624 -0.777334176 1.210566 2.950362168 0.4960174004
## 2115 -0.890964906 -0.654853210 1.210566 -0.082921602 1.4689568797
## 2122 -1.324453835 -1.534901635 -0.826060 1.753013311 -1.1255483985
## 2123 0.251869541 0.175295562 1.210566 -0.561861145 0.4960174004
## 2125 1.670560579 1.481759204 1.210566 -0.561861145 0.4960174004
## 2128 0.094237203 0.742337073 -0.826060 -1.040800688 -1.1255483985
## 45 0.508022090 0.991835338 -0.826060 1.274073769 0.1717042406
## 179 0.980919102 1.277624260 -0.826060 -0.641684402 0.1717042406
## 306 -0.280139598 -0.096884363 -0.826060 -0.003098345 -0.8012352387
## 1307 0.074533161 0.583565450 1.210566 0.396017940 0.8203305602
## 1872 -0.358955767 -0.096884363 1.210566 0.076724912 -0.1526089192
## 2082 -1.186525539 -0.641244214 1.210566 1.832836569 -0.4769220790
## 2117 3.010435449 2.284689983 -0.826060 -0.402214631 0.1717042406
## 2119 -1.186525539 -1.185604064 1.210566 -0.402214631 0.8203305602
## 304 -1.048597244 -0.972396456 -0.826060 0.635487712 -0.4769220790
## 334 -0.437771936 0.175295562 -0.826060 0.555664455 -0.1526089192
## 513 -0.418067894 0.175295562 1.210566 0.136799908 -0.0679441797
## 598 -0.950077033 -0.595880893 1.210566 -0.561861145 0.1717042406
## 909 -0.418067894 -0.323700968 1.210566 1.251675395 -1.4763433656
## 1030 -0.555996189 -0.369064288 1.210566 1.434263435 -0.5668050074
## 1238 -0.615108316 -0.505154251 1.210566 1.114427254 -1.7741747181
## 1700 -0.043691092 -0.301019307 -0.826060 0.236371426 1.4689568797
## 2017 -1.206229582 -0.940642132 -0.826060 0.076724912 0.1717042406
## 2087 0.350389752 0.225195215 1.210566 -0.082921602 -2.4228010377
## 2106 0.015421035 0.383966838 1.210566 -0.561861145 -0.1526089192
## 2109 0.645950385 0.356748846 -0.826060 -0.847179108 0.8318082494
## 2110 1.059735271 1.422786887 1.210566 -1.360093716 0.4960174004
## 2111 0.882398891 0.696973752 -0.826060 -0.402214631 -0.8012352387
## 2114 -0.043691092 -0.096884363 1.210566 -0.960977431 -0.1526089192
## 40 3.700076926 3.713634591 -0.826060 -0.641684402 -0.8012352387
## 95 -0.910668949 -0.641244214 1.210566 0.874957483 -0.4769220790
## 234 -0.004283007 0.093641585 1.210566 0.156548169 1.4689568797
## 358 -1.482086172 -1.426029665 -0.826060 1.353897026 -0.4769220790
## 450 0.232165499 0.039205600 -0.826060 -0.168284524 -0.0043269427
## 621 -0.043691092 -0.096884363 -0.826060 0.337002038 0.0808750409
## 934 -0.575700231 -0.641244214 1.210566 1.442347646 -0.1349011774
## 965 -0.654516400 -0.586808229 1.210566 0.440693162 0.3949135025
## 1033 -1.088005328 -0.981469120 1.210566 0.770364374 -0.0850651060
## 1068 -0.634812358 -0.042448378 -0.826060 2.072306340 0.8203305602
## 1120 -1.285045750 -1.230967385 1.210566 -0.721507659 1.1446437200
## 1234 -0.457475978 -0.555053904 1.210566 -0.721507659 0.4960174004
## 2011 5.434032639 0.719655413 -0.826060 -0.082921602 0.4960174004
## 2068 -0.378659809 -0.164929344 -0.826060 -0.881154174 0.1717042406
## 2078 2.005529297 -0.822697497 1.210566 1.513543540 -1.4498615583
## 2100 -1.324453835 -1.426029665 1.210566 -0.082921602 1.1446437200
## 2102 -0.437771936 -0.641244214 -0.826060 -0.881154174 0.8203305602
## 2105 -0.871260864 -1.117559083 -0.826060 -0.402214631 0.8203305602
## 2203 -1.127413413 -1.244576382 -0.826060 -0.322391374 1.1446437200
## 193 -0.457475978 -0.301019307 1.210566 1.433720283 -0.1526089192
## 387 -1.285045750 -0.913424139 1.210566 1.194250512 -0.4769220790
## 731 1.532632284 2.003437394 -0.826060 -0.561861145 -0.1526089192
## 1262 -0.575700231 0.075496256 -0.826060 -0.960977431 -0.8012352387
## 1342 -0.693924484 -0.940642132 -0.826060 -1.120623945 0.4960174004
## 1465 -0.575700231 -0.096884363 -0.826060 -0.082921602 -0.4769220790
## 2048 1.217367609 0.388503170 -0.826060 -0.242568117 -0.8012352387
## 2093 0.370093794 0.461084484 -0.826060 -1.120623945 -0.8012352387
## 2099 -0.595404273 -0.432572938 1.210566 -0.881154174 1.1446437200
## 2101 0.724766554 0.901108696 -0.826060 -0.322391374 -0.4769220790
## 2103 -0.555996189 -0.369064288 -0.826060 -0.482037888 -0.8012352387
## 2205 -1.679126594 -1.312621363 -0.826060 -0.641684402 0.4960174004
## 72 -0.831852780 -1.049514102 1.210566 2.631069140 0.8203305602
## 299 1.237071651 0.991835338 1.210566 -0.322391374 -0.4769220790
## 1043 0.113941246 0.447475488 1.210566 -0.561861145 0.1717042406
## 1049 -1.620014468 -1.757181908 -0.826060 1.600765377 -0.2124324673
## 1240 0.094237203 0.089105252 -0.826060 -0.721507659 1.1446437200
## 1561 -1.363861919 -1.013223445 -0.826060 1.673190054 -3.3957405170
## 1723 -0.339251725 0.111786913 1.210566 -1.040800688 -1.1255483985
## 1992 0.882398891 0.447475488 -0.826060 -0.960977431 -0.8012352387
## 2005 -0.181619387 0.388503170 -0.826060 -1.040800688 -0.8012352387
## 2088 0.389797836 0.964617346 1.210566 -0.960977431 0.1717042406
## 2094 -0.181619387 -0.355455292 -0.826060 -0.242568117 -1.1255483985
## 2097 -0.989485117 -0.999614449 -0.826060 -0.881154174 -1.7741747181
## 84 -0.792444695 -1.915953531 1.210566 -0.402214631 -2.0984878779
## 273 -0.457475978 0.175295562 1.210566 -0.322391374 0.8203305602
## 370 0.350389752 0.447475488 1.210566 -0.641684402 0.8203305602
## 418 -1.166821497 -0.809088501 1.210566 -0.641684402 0.8203305602
## 473 0.823286765 0.991835338 1.210566 0.684377392 -0.2940401109
## 646 -1.797350848 -1.661918934 -0.826060 0.475841197 0.4960174004
## 670 -0.299843640 -0.505154251 1.210566 0.155542761 0.1711875449
## 1381 0.271573583 0.061887260 1.210566 -0.561861145 1.1446437200
## 1512 -1.088005328 -1.185604064 1.210566 -0.561861145 -3.0714273572
## 1738 1.256775693 0.991835338 -0.826060 -0.482037888 0.4960174004
## 1928 -0.713628527 -0.573199232 -0.826060 -0.402214631 -0.1526089192
## 1973 0.192757414 -0.559590236 1.210566 -0.082921602 -0.8012352387
## 1976 -0.102803218 -0.346382628 -0.826060 3.988064511 -0.1526089192
## 2024 0.153349330 0.157150234 -0.826060 -0.881154174 -0.8012352387
## 2049 -0.083099176 0.107250581 -0.826060 -1.200447202 1.7932700395
## 2070 -0.713628527 -1.571192292 -0.826060 -0.801330916 0.4960174004
## 2079 1.217367609 0.991835338 -0.826060 -0.721507659 -1.7741747181
## 2080 0.035125077 -0.414427609 -0.826060 0.715310969 0.4960174004
## 2083 -1.048597244 -1.457783990 -0.826060 -0.721507659 0.8203305602
## 2085 -1.225933624 -1.625628277 1.210566 0.555664455 1.4689568797
## 2086 -0.772740653 -1.103950087 1.210566 -0.482037888 -0.1526089192
## 2090 -0.753036611 -0.496081587 1.210566 3.269655197 -1.1255483985
## 2091 -2.368768071 -0.641244214 1.210566 -0.402214631 0.4960174004
## 2092 1.256775693 0.969153678 -0.826060 0.156548169 -0.8012352387
## 75 -1.088005328 -0.800015837 -0.826060 -1.040800688 -0.1526089192
## 106 -0.063395134 -0.432572938 1.210566 1.034603997 -1.1255483985
## 413 -0.634812358 -1.126631747 1.210566 -0.801330916 0.4960174004
## 432 -0.536292147 -0.096884363 1.210566 0.635487712 0.4960174004
## 531 0.606542301 0.175295562 -0.826060 0.217410801 -0.6436338674
## 839 1.631152495 2.080555039 -0.826060 -0.809537998 0.4829681188
## 1062 -0.950077033 -0.641244214 1.210566 4.626650568 -0.4769220790
## 1163 0.941511018 0.823991051 -0.826060 -0.641684402 0.4960174004
## 1177 1.059735271 0.801309390 -0.826060 -0.162744860 -0.1526089192
## 1855 1.000623145 0.093641585 -0.826060 -0.561861145 -0.1526089192
## 2033 0.054829119 -0.260192318 -0.826060 -0.082921602 0.4960174004
## 2071 0.173053372 0.991835338 -0.826060 -0.561861145 1.1446437200
## 2073 -0.930372991 -1.362521016 1.210566 0.316194683 0.1717042406
## 2076 0.291277625 0.447475488 1.210566 0.446808031 -0.5367815091
## 2084 0.488318047 0.175295562 1.210566 -0.561861145 -0.1526089192
## 78 0.015421035 -0.319164636 -0.826060 -0.003098345 0.4960174004
## 264 0.941511018 0.447475488 -0.826060 -0.482037888 -0.4769220790
## 315 -0.950077033 -1.448711326 1.210566 -1.040800688 0.4960174004
## 439 0.232165499 0.256949540 -0.826060 0.646890272 -0.0959001946
## 529 -0.181619387 -0.037912046 1.210566 0.236371426 -1.1255483985
## 758 1.118847398 1.808375114 -0.826060 -0.960977431 -0.8012352387
## 1092 -0.536292147 -0.827233829 1.210566 2.072306340 1.1446437200
## 1130 0.251869541 -0.096884363 -0.826060 -0.082921602 -1.4498615583
## 1412 -0.418067894 -0.096884363 -0.826060 -0.641684402 0.8203305602
## 1614 -0.831852780 -0.963323792 1.210566 1.912659826 -1.7741747181
## 1802 -0.339251725 0.157150234 1.210566 0.076724912 0.1717042406
## 1970 -0.615108316 -0.586808229 1.210566 2.152129597 1.1446437200
## 1983 0.370093794 0.447475488 1.210566 -0.960977431 1.7932700395
## 2012 -0.142211303 -0.777334176 -0.826060 3.509124968 -2.0984878779
## 2053 1.512928242 1.141534297 -0.826060 -0.881154174 -0.4769220790
## 2057 1.611448453 0.719655413 -0.826060 -0.881154174 1.1446437200
## 2058 0.586838258 0.256949540 1.210566 -0.322391374 -0.1526089192
## 2059 -0.023987050 -0.632171550 -0.826060 -0.561861145 -1.7741747181
## 2074 0.015421035 -0.096884363 1.210566 -0.162744860 -4.6929931561
## 2075 -0.378659809 -1.553046964 -0.826060 -0.402214631 0.4960174004
## 2215 0.015421035 0.574492786 -0.826060 -0.402214631 1.4689568797
## 37 -0.615108316 -0.641244214 1.210566 -0.082921602 -0.4769220790
## 65 0.527726132 0.256949540 1.210566 -0.960977431 -0.1526089192
## 100 -0.004283007 1.871883763 1.210566 -0.274119100 -0.0679787904
## 108 -0.004283007 0.175295562 1.210566 -0.504028056 0.3537284717
## 173 1.473520157 0.742337073 1.210566 -0.082921602 0.8203305602
## 207 0.783878680 0.991835338 -0.826060 -0.322391374 -0.4769220790
## 824 -0.023987050 0.447475488 1.210566 0.593721355 0.1585724667
## 1141 -0.083099176 -0.096884363 -0.826060 3.668771482 -3.0714273572
## 1145 1.000623145 1.127925301 -0.826060 -0.493876817 0.1424350850
## 1618 -1.166821497 -0.573199232 -0.826060 -0.482037888 -0.1526089192
## 1812 -0.102803218 -0.777334176 1.210566 -0.721507659 0.1717042406
## 1856 -1.127413413 -0.913424139 -0.826060 0.076724912 1.1446437200
## 1937 -2.014095312 -1.185604064 1.210566 -0.082921602 -1.1255483985
## 2047 0.389797836 -0.078739035 -0.826060 -0.482037888 0.1717042406
## 2061 1.414408031 1.731257469 1.210566 -0.721507659 0.4960174004
## 2062 -1.620014468 -1.539437967 -0.826060 2.551245883 -1.7741747181
## 2063 -0.319547683 -0.228437994 1.210566 -0.960977431 -0.8012352387
## 2221 -0.240731514 -0.260192318 -0.826060 -0.361556468 -0.1183027877
## 295 1.138551440 0.774091398 -0.826060 -0.455177704 -0.0018161621
## 337 1.177959524 1.150606961 -0.826060 -0.833383176 0.5797224323
## 372 -1.186525539 -1.094877423 -0.826060 -0.026592274 -0.2362352825
## 546 -1.028893202 -1.675527930 -0.826060 1.453198658 -0.6786689932
## 874 -0.615108316 -0.505154251 1.210566 0.141269294 0.4290909562
## 972 0.862694849 0.311385525 1.210566 -0.130350434 -0.1847414325
## 1210 0.173053372 0.796773058 1.210566 -0.482037888 0.1717042406
## 1298 -0.615108316 -0.096884363 1.210566 -0.082921602 0.1717042406
## 1498 -1.403270004 -1.344375688 1.210566 0.236371426 0.4960174004
## 1979 0.468614005 -0.346382628 1.210566 0.236371426 0.4960174004
## 2066 0.232165499 0.334067185 -0.826060 0.236371426 -0.4769220790
## 383 1.296183778 0.787700394 -0.826060 -0.721507659 -0.4769220790
## 434 0.153349330 0.411184831 -0.826060 0.475841197 1.1446437200
## 843 0.567134216 1.400105226 1.210566 -0.130945083 0.2314500963
## 865 0.330685710 0.252413208 1.210566 -0.756211251 0.2907023570
## 922 -0.240731514 -1.081268426 1.210566 0.367266987 0.1857836121
## 1418 1.493224200 1.146070629 -0.826060 0.156548169 -0.1526089192
## 1485 0.823286765 0.719655413 -0.826060 -1.439916973 0.4960174004
## 1842 -0.753036611 -1.589337620 -0.826060 -0.242568117 0.1717042406
## 2037 -0.418067894 -1.258185378 1.210566 -0.162744860 0.4960174004
## 2052 0.685358469 0.860281708 -0.826060 -0.801330916 -0.1526089192
## 371 -0.280139598 -0.382673285 -0.826060 -0.027047315 -0.0753005427
## 376 -0.831852780 -0.369064288 -0.826060 0.353492154 -0.1871408796
## 377 -0.989485117 -1.457783990 1.210566 -0.242568117 0.1717042406
## 506 0.567134216 0.765018734 1.210566 0.169028487 0.3144963055
## 728 -0.181619387 -0.995078117 -0.826060 0.079403229 -0.0886462757
## 826 1.079439313 0.991835338 -0.826060 -0.626033285 0.4666304607
## 1642 1.000623145 0.098177917 -0.826060 -0.801330916 0.1717042406
## 1707 -0.772740653 -0.777334176 -0.826060 0.236371426 0.1717042406
## 1751 1.276479735 1.654139823 -0.826060 -0.561861145 0.1717042406
## 1864 0.389797836 -0.314628303 -0.826060 1.274073769 -2.0984878779
## 1895 -0.693924484 -0.995078117 -0.826060 3.269655197 -1.1255483985
## 1998 0.113941246 0.447475488 1.210566 -0.561861145 0.1717042406
## 31 -1.068301286 -1.367057348 1.210566 -0.003098345 -0.1526089192
## 35 -1.088005328 -0.727434523 -0.826060 0.076724912 -0.1526089192
## 90 0.015421035 0.447475488 -0.826060 -0.254537424 0.3710079676
## 312 -0.477180020 -0.437109270 1.210566 0.344606684 0.3136411598
## 457 1.572040368 1.536195189 1.210566 -0.881154174 0.8203305602
## 466 -1.304749793 -1.684600594 -0.826060 -0.162744860 -0.8012352387
## 870 -0.555996189 -1.335303023 1.210566 0.236371426 -1.4498615583
## 913 -1.422974046 -1.457783990 -0.826060 0.280713395 0.4239907167
## 1013 0.783878680 0.683364756 -0.826060 -0.488367890 -0.0755789850
## 1117 -0.398363851 -0.777334176 -0.826060 -0.242568117 0.4960174004
## 1306 0.074533161 0.447475488 -0.826060 -0.801330916 0.1717042406
## 1619 -0.477180020 -0.641244214 -0.826060 2.391599368 -1.1255483985
## 1756 -0.043691092 0.315921857 1.210566 -0.881154174 0.4960174004
## 2026 0.409501879 -0.505154251 1.210566 0.909516218 -0.0708582502
## 2028 -0.043691092 -1.185604064 1.210566 0.715310969 1.4689568797
## 2038 2.852803111 2.361807629 -0.826060 -0.589821891 0.6057748263
## 2039 1.315887820 0.447475488 -0.826060 -0.482037888 0.1717042406
## 2043 -0.733332569 -0.922496803 1.210566 -0.561861145 -0.1526089192
## 2227 -1.028893202 -1.022296109 1.210566 0.076724912 -1.7741747181
## 162 0.882398891 0.991835338 -0.826060 -0.881154174 0.4960174004
## 528 0.330685710 0.991835338 -0.826060 -0.324848109 0.3906334672
## 581 -0.654516400 0.039205600 -0.826060 0.963453208 -0.3621189305
## 600 0.724766554 0.538202129 1.210566 0.076724912 0.1717042406
## 601 -0.674220442 -0.468863594 1.210566 1.657170961 -0.4890657225
## 814 0.409501879 -0.437109270 -0.826060 0.127495253 -0.0829816923
## 1318 -0.437771936 -0.437109270 -0.826060 -0.402214631 -0.1526089192
## 1477 0.448909963 0.447475488 -0.826060 -0.881154174 -0.1526089192
## 1982 0.094237203 -0.641244214 1.210566 -0.721507659 0.1717042406
## 2029 0.173053372 -0.695680199 1.210566 -0.561861145 0.4960174004
## 2034 0.133645288 0.197977223 -0.826060 -0.610506048 0.1919833187
## 2041 1.138551440 -0.369064288 -0.826060 -0.641684402 0.8203305602
## 2042 0.724766554 1.195970282 1.210566 -0.641684402 -0.1526089192
## 2224 2.734578858 2.624914890 1.210566 -0.881154174 0.1717042406
## 2229 0.705062512 0.515520469 1.210566 -0.721507659 1.7932700395
## 612 -0.161915345 0.039205600 1.210566 0.797869086 -0.3379138471
## 703 0.370093794 -0.096884363 -0.826060 -1.120623945 -1.4498615583
## 916 -0.890964906 0.129932241 1.210566 0.173273751 0.4185602420
## 994 -1.206229582 -1.258185378 -0.826060 -0.605399202 0.8698942183
## 1188 0.921806976 -0.096884363 -0.826060 -0.689923455 0.1485326588
## 1216 -1.166821497 -0.895278811 1.210566 0.396017940 -1.1255483985
## 1890 -0.516588105 -0.999614449 -0.826060 -0.881154174 -0.8012352387
## 1991 -0.437771936 -1.376130012 -0.826060 0.076724912 -1.1255483985
## 2031 -0.319547683 -0.822697497 -0.826060 -0.641684402 0.4960174004
## 2032 0.724766554 -0.477936259 1.210566 -0.641684402 -0.1526089192
## 97 -0.102803218 -0.369064288 1.210566 0.421846156 -0.2204756283
## 201 1.887305044 0.660683096 -0.826060 -0.721507659 1.1446437200
## 341 -1.186525539 -1.040441438 1.210566 1.992483083 -4.0443668366
## 717 -0.713628527 1.313914916 -0.826060 0.068672165 0.1987962334
## 1364 -0.418067894 -1.049514102 -0.826060 -0.242568117 0.8203305602
## 1439 -0.221027472 0.447475488 -0.826060 -0.142065285 0.2860120299
## 1474 -0.280139598 -0.954251128 -0.826060 -0.402214631 -0.4769220790
## 1489 -0.693924484 -0.487008923 -0.826060 -0.801330916 0.4960174004
## 1669 -0.201323429 0.447475488 1.210566 -0.881154174 -0.4769220790
## 1687 -0.812148738 -1.185604064 -0.826060 0.076724912 0.4960174004
## 1704 -0.634812358 -0.967860124 1.210566 -0.082921602 -1.1255483985
## 1706 0.232165499 -0.232974326 -0.826060 -0.721507659 -0.8012352387
## 1780 1.256775693 1.037198659 -0.826060 -1.280270459 0.1717042406
## 1984 0.980919102 0.569956454 -0.826060 -0.482037888 -0.1526089192
## 2022 0.074533161 -0.096884363 1.210566 0.316194683 -0.1526089192
## 2025 0.803582723 0.175295562 -0.826060 -1.280270459 1.4689568797
## 2217 -0.221027472 -0.777334176 -0.826060 -0.003098345 0.4960174004
## 2222 1.650856537 1.127925301 -0.826060 -0.808847918 0.3685942897
## 135 -0.890964906 -0.641244214 -0.826060 -0.127557645 0.3383090090
## 166 -0.930372991 -0.232974326 -0.826060 1.593366797 -0.4769220790
## 468 -0.713628527 -1.072195762 -0.826060 -0.402214631 2.4418963591
## 626 -0.457475978 -0.096884363 1.210566 2.631069140 0.4960174004
## 1031 -1.107709371 -0.777334176 1.210566 0.054019104 -0.3933443367
## 1362 -1.186525539 -1.321694027 1.210566 0.715310969 0.8203305602
## 1510 -0.831852780 -0.981469120 -0.826060 -0.641684402 -1.4498615583
## 1625 0.941511018 0.855745376 -0.826060 -0.596816033 0.3204490346
## 1696 0.626246343 0.039205600 -0.826060 2.471422626 1.7932700395
## 1699 -0.989485117 -1.081268426 -0.826060 0.635487712 -0.8012352387
## 1705 2.616354605 2.021582722 -0.826060 -1.180611881 0.5630943907
## 1715 1.118847398 1.218651943 -0.826060 -0.482037888 0.8203305602
## 1861 1.493224200 1.291233256 1.210566 -0.721507659 0.1717042406
## 1941 -0.260435556 -0.473399927 -0.826060 -0.561861145 -0.1526089192
## 1964 -0.477180020 0.061887260 -0.826060 -0.641684402 -0.8012352387
## 2218 -0.713628527 -0.986005453 -0.826060 -1.126176037 -0.6106800116
## 2228 -0.575700231 -0.128638688 1.210566 0.926215870 -1.3497784783
## 2230 1.650856537 1.254942599 -0.826060 -1.200447202 1.4689568797
## 255 0.094237203 -0.500617919 1.210566 -0.561861145 0.4960174004
## 819 -0.713628527 -0.845379158 -0.826060 0.479908749 0.2608882288
## 917 -0.812148738 -0.232974326 1.210566 0.379722597 -0.2860813784
## 1085 0.527726132 0.855745376 1.210566 0.822797235 -0.0405476893
## 1187 -0.634812358 -0.641244214 -0.826060 -0.801330916 1.1446437200
## 1394 -0.063395134 0.107250581 -0.826060 -0.721507659 0.1717042406
## 1479 -1.620014468 -1.580264956 -0.826060 -0.322391374 -5.6659326355
## 1517 0.665654427 0.991835338 -0.826060 -0.561861145 0.1717042406
## 2009 -0.043691092 -0.251119654 1.210566 -0.801330916 -0.8012352387
## 2223 -0.969781075 -1.185604064 -0.826060 0.076724912 -1.4498615583
## 2225 0.645950385 0.991835338 -0.826060 -0.881154174 0.8203305602
## 2226 1.256775693 0.833063715 -0.826060 0.795134226 -0.8012352387
## 27 -0.437771936 -0.187611005 1.210566 -0.482037888 -2.7471141974
## 105 1.670560579 1.681357816 -0.826060 -1.280270459 -0.1526089192
## 147 0.547430174 0.039205600 1.210566 -0.653005850 0.7207017910
## 224 0.389797836 0.719655413 -0.826060 0.116536595 0.5283475404
## 274 -0.772740653 -1.303548699 -0.826060 -0.881154174 0.8203305602
## 356 -0.555996189 -1.135704411 1.210566 -0.402214631 0.1717042406
## 431 -0.950077033 -1.553046964 -0.826060 1.371373730 -0.6029988166
## 551 1.769080790 1.536195189 -0.826060 -0.402214631 0.8203305602
## 962 -0.418067894 -0.006157721 1.210566 1.042582192 -0.8361445894
## 997 -0.437771936 -0.505154251 -0.826060 -0.227139899 0.6541237597
## 1389 -0.122507261 -0.777334176 -0.826060 -0.881154174 0.4960174004
## 1831 0.567134216 -0.387209617 -0.826060 1.353897026 -1.1255483985
## 1996 -0.260435556 -0.840842826 1.210566 -0.641684402 -0.4769220790
## 1999 2.892211196 2.080555039 -0.826060 -0.322391374 -0.1526089192
## 2008 0.291277625 -1.131168079 -0.826060 -1.120623945 1.1446437200
## 2216 0.192757414 0.801309390 -0.826060 0.795134226 -0.8012352387
## 107 0.842990807 0.034669267 -0.826060 -0.322391374 -0.1526089192
## 148 -1.048597244 -1.049514102 -0.826060 -0.641684402 -1.7741747181
## 182 0.882398891 0.447475488 -0.826060 -0.561861145 0.1717042406
## 414 -1.698830637 -1.911417199 1.210566 1.034603997 -0.1526089192
## 511 -0.713628527 -0.378136953 1.210566 0.156548169 -0.1526089192
## 699 0.606542301 0.311385525 -0.826060 -0.828092763 0.0572998222
## 1017 -1.068301286 -0.641244214 1.210566 0.503396004 0.0203177051
## 1366 1.650856537 1.254942599 -0.826060 -0.482037888 -0.1526089192
## 1672 -0.753036611 -1.294476035 1.210566 -0.561861145 -1.1255483985
## 1931 1.040031229 0.583565450 -0.826060 -0.801330916 0.1717042406
## 1966 2.301089930 1.672285151 -0.826060 -0.634194809 -0.0170965367
## 1994 1.493224200 1.014516999 -0.826060 -1.200447202 -3.0714273572
## 2002 2.163161634 0.896572364 -0.826060 -0.561861145 -0.1526089192
## 2007 -0.851556822 -0.641244214 -0.826060 -0.591934456 0.3408738818
## 2 1.532632284 1.150606961 -0.826060 -0.402214631 -0.4769220790
## 143 0.153349330 -0.550517572 -0.826060 -0.003098345 -1.1255483985
## 160 4.172973938 4.144586140 -0.826060 -0.402214631 -0.1526089192
## 688 -0.398363851 -0.096884363 1.210566 0.733964084 -0.1376790267
## 776 -0.615108316 -0.709289195 -0.826060 -0.579552560 0.5030291125
## 850 -1.324453835 -0.641244214 -0.826060 2.551230473 -0.1486053835
## 1194 0.173053372 -0.355455292 1.210566 -0.162744860 -1.1255483985
## 1849 1.138551440 0.991835338 -0.826060 -0.385816358 -0.1809764097
## 5 0.192757414 0.356748846 1.210566 1.114427254 1.1446437200
## 55 -0.221027472 -0.210292665 -0.826060 -0.402214631 -0.4769220790
## 347 1.611448453 1.332060245 -0.826060 0.874957483 -0.1526089192
## 856 -0.280139598 -0.437109270 -0.826060 1.533315314 -0.0697576935
## 1972 0.330685710 0.447475488 -0.826060 0.396017940 1.7932700395
## 1977 0.350389752 0.175295562 -0.826060 1.753013311 -1.1255483985
## 1980 -0.733332569 -1.058586766 -0.826060 1.513543540 0.8203305602
## 1981 0.192757414 -0.913424139 -0.826060 -1.200447202 0.4960174004
## 1985 -0.023987050 -0.595880893 -0.826060 0.635487712 1.1446437200
## 1987 0.468614005 0.188904558 1.210566 -0.641684402 1.1446437200
## 1995 0.133645288 0.433866491 1.210566 1.753013311 0.4960174004
## 2207 0.035125077 0.538202129 -0.826060 -0.308227468 0.4833708866
## 244 -0.536292147 -0.232974326 1.210566 0.255367798 0.0425916756
## 308 -0.930372991 -1.775327236 -0.826060 -0.721507659 0.1717042406
## 560 0.547430174 0.991835338 -0.826060 -1.120623945 -0.1526089192
## 561 0.448909963 0.991835338 1.210566 -0.784868999 0.1531053090
## 1173 0.823286765 0.674292092 -0.826060 -0.003098345 0.8203305602
## 1190 -0.575700231 -1.090341091 -0.826060 1.593366797 -2.4228010377
## 1269 -0.102803218 -1.185604064 -0.826060 0.156548169 -0.1526089192
## 1453 0.133645288 -0.423500274 -0.826060 -0.482037888 2.4418963591
## 1543 0.409501879 1.127925301 -0.826060 -0.960977431 0.1717042406
## 1681 -0.240731514 -0.437109270 -0.826060 2.471422626 0.1717042406
## 1800 -0.575700231 -0.804552169 -0.826060 0.137761575 0.0602770592
## 1873 2.478426309 1.894565424 -0.826060 -0.710727120 0.4422842718
## 1971 1.237071651 1.717648472 -0.826060 -0.169128921 -0.1524381261
## 1974 -0.358955767 -1.680064262 1.210566 -0.162744860 -0.1526089192
## 130 0.567134216 -0.232974326 -0.826060 -0.482037888 -1.1255483985
## 721 -1.403270004 -1.729963915 1.210566 0.378871890 -0.4016198550
## 837 -0.280139598 -0.096884363 1.210566 0.558481515 -0.2003064923
## 1741 0.173053372 -0.423500274 -0.826060 -0.721507659 -0.1526089192
## 1860 -1.580606383 -1.593873952 1.210566 -0.003098345 0.4960174004
## 1957 0.350389752 0.175295562 -0.826060 -1.120623945 0.1717042406
## 1960 -0.142211303 0.175295562 1.210566 0.076724912 0.1717042406
## 1965 -1.403270004 -1.017759777 1.210566 -0.482037888 0.1717042406
## 1968 0.035125077 0.882963368 -0.826060 -0.162744860 0.1717042406
## 58 -0.043691092 0.039205600 1.210566 0.795134226 -0.8012352387
## 195 0.310981668 -0.509690583 1.210566 -0.721507659 1.1446437200
## 1228 0.291277625 0.175295562 -0.826060 -0.482037888 0.1717042406
## 1257 -2.033799354 -1.661918934 -0.826060 -0.202519686 0.2037192650
## 1592 0.744470596 -1.185604064 1.210566 1.194250512 -2.7471141974
## 1596 -0.812148738 -0.359991624 -0.826060 -0.482037888 1.1446437200
## 1750 -0.496884062 -0.981469120 -0.826060 -0.881154174 -0.1526089192
## 1837 -0.280139598 -0.369064288 -0.826060 0.236371426 -0.4769220790
## 1942 -0.181619387 0.447475488 1.210566 3.828417997 -2.7471141974
## 1961 -0.536292147 -0.323700968 -0.826060 -0.402214631 1.1446437200
## 1962 -0.319547683 0.243340544 1.210566 0.316194683 -1.4498615583
## 1963 0.330685710 -0.369064288 1.210566 -0.801330916 0.1717042406
## 422 2.044937381 2.080555039 -0.826060 -0.402214631 -0.4769220790
## 1401 -0.753036611 -0.641244214 -0.826060 2.311776111 -0.4769220790
## 1525 0.133645288 -0.641244214 -0.826060 -0.082921602 -0.1526089192
## 1646 -0.792444695 -0.672998538 -0.826060 0.715310969 -1.7741747181
## 1929 -0.615108316 -0.845379158 1.210566 -0.881154174 -0.8012352387
## 1940 -1.186525539 -1.185604064 1.210566 -0.003098345 -1.7741747181
## 1945 1.828192917 1.536195189 -0.826060 -0.003098345 -2.4228010377
## 1946 0.035125077 -0.505154251 -0.826060 -0.881154174 0.1717042406
## 1948 0.685358469 0.447475488 -0.826060 -0.960977431 -0.1526089192
## 1949 -0.339251725 -0.777334176 -0.826060 -0.402214631 -1.1255483985
## 1950 0.803582723 -1.416957001 1.210566 -0.641684402 -1.1255483985
## 1953 1.769080790 1.808375114 -0.826060 -1.120623945 -1.1255483985
## 1955 0.586838258 0.139004905 -0.826060 0.156548169 1.1446437200
## 202 -0.950077033 -1.729963915 -0.826060 -0.641684402 -5.6659326355
## 494 0.212461457 0.107250581 -0.826060 -0.721507659 -0.1526089192
## 740 -1.186525539 -1.049514102 -0.826060 1.236892489 -0.2671468929
## 780 -0.043691092 0.052814596 1.210566 0.036739866 0.3046969369
## 1069 3.266587997 2.897094815 -0.826060 -0.641684402 0.1717042406
## 1537 0.409501879 0.855745376 -0.826060 -1.120623945 1.7932700395
## 1920 0.271573583 -0.096884363 -0.826060 -0.480858846 -0.3263238125
## 1926 0.980919102 0.628928771 -0.826060 -1.200447202 -1.1255483985
## 1938 -0.654516400 -1.049514102 -0.826060 0.156548169 0.4960174004
## 1939 1.118847398 0.855745376 -0.826060 -0.960977431 -0.4769220790
## 1947 0.291277625 0.515520469 -0.826060 -1.120623945 -0.1526089192
## 1951 0.251869541 -0.400818613 1.210566 -0.242568117 -0.4769220790
## 25 1.788784833 2.080555039 -0.826060 -1.068930942 0.0717932768
## 134 -0.792444695 -0.695680199 1.210566 -0.162744860 0.1717042406
## 323 -0.043691092 0.388503170 1.210566 0.200097678 -0.2511857948
## 354 -0.418067894 -0.391745949 -0.826060 -0.162744860 1.1446437200
## 381 -1.127413413 -0.777334176 -0.826060 -0.482037888 -0.1526089192
## 1568 -1.580606383 -1.974925848 -0.826060 -0.322391374 0.4960174004
## 1766 0.212461457 -0.096884363 -0.826060 -1.519740231 -0.8012352387
## 1782 0.370093794 -0.096884363 1.210566 -0.641684402 0.4960174004
## 1903 0.429205921 -0.096884363 -0.826060 -0.721507659 -0.8012352387
## 1923 0.941511018 0.991835338 -0.826060 0.795134226 -0.1526089192
## 1925 -0.378659809 -0.096884363 1.210566 -0.561861145 1.7932700395
## 1930 1.000623145 0.765018734 -0.826060 -0.082921602 -0.4769220790
## 1932 2.399610141 1.808375114 -0.826060 -0.801330916 0.1717042406
## 1934 -0.201323429 -0.573199232 -0.826060 1.194250512 0.4960174004
## 385 -0.083099176 -0.096884363 1.210566 0.920140862 -0.2688148161
## 548 -0.299843640 -0.863524486 -0.826060 3.988064511 0.4960174004
## 1078 -0.063395134 -0.096884363 1.210566 1.211843971 -0.2269575067
## 1429 -1.028893202 -0.868060818 -0.826060 -0.881154174 1.4689568797
## 1581 -0.950077033 -1.344375688 -0.826060 0.316194683 -1.7741747181
## 1609 1.887305044 3.033184778 -0.826060 -0.561861145 -1.1255483985
## 1905 -0.378659809 -0.069666371 1.210566 -0.721507659 -0.4769220790
## 1908 -1.107709371 -1.090341091 1.210566 1.753013311 -0.1526089192
## 1916 1.079439313 1.159679625 -0.826060 -1.120623945 1.1446437200
## 1918 0.626246343 -0.096884363 -0.826060 -0.960977431 0.4960174004
## 1919 -0.221027472 -0.890742479 -0.826060 0.475841197 0.4960174004
## 1921 0.389797836 0.048278264 -0.826060 -0.801330916 0.4960174004
## 1927 1.493224200 0.243340544 1.210566 -0.482037888 -0.8012352387
## 1933 -0.595404273 -1.122095415 1.210566 -0.162744860 -1.4498615583
## 77 0.547430174 0.329530853 -0.826060 -0.721507659 0.8203305602
## 184 1.276479735 0.447475488 -0.826060 -0.638789473 -0.0248322520
## 498 0.212461457 0.175295562 -0.826060 -0.103480165 0.0353519681
## 555 0.291277625 -0.359991624 -0.826060 0.102699730 -0.3674674675
## 574 0.192757414 -0.641244214 -0.826060 -0.960977431 -0.8012352387
## 783 0.054829119 0.447475488 -0.826060 -0.322391374 1.1446437200
## 956 -0.457475978 -0.369064288 1.210566 1.347313242 -0.6585558305
## 1584 -0.615108316 -0.355455292 1.210566 1.353897026 -0.1526089192
## 1797 -1.068301286 -0.505154251 1.210566 -0.162744860 -0.4769220790
## 1863 1.966121212 1.808375114 -0.826060 -0.048492288 -0.0253852416
## 1874 0.468614005 0.592638114 -0.826060 0.076724912 -0.8012352387
## 1882 -0.536292147 -0.328237300 -0.826060 -0.881154174 -0.8012352387
## 1898 0.291277625 0.447475488 1.210566 -0.881154174 0.1717042406
## 1911 0.074533161 -0.169465677 -0.826060 0.316194683 0.8203305602
## 1913 -0.772740653 -0.641244214 -0.826060 2.072306340 0.4960174004
## 1914 1.887305044 1.395568894 -0.826060 -0.795489111 0.4798807418
## 1915 -0.201323429 0.402112167 -0.826060 0.316194683 -0.4769220790
## 115 -0.398363851 -0.096884363 1.210566 1.034603997 -1.1255483985
## 259 -0.201323429 -0.337309964 1.210566 -0.402214631 -0.4769220790
## 321 -1.068301286 -0.369064288 -0.826060 -0.402214631 -0.1526089192
## 825 -0.693924484 -0.278337647 -0.826060 -0.429500990 0.4945844616
## 1370 -0.693924484 -0.777334176 1.210566 -0.402214631 -0.1526089192
## 1621 -0.654516400 -0.405354945 -0.826060 0.954780740 0.4960174004
## 1643 -0.536292147 -0.813624833 1.210566 -0.482037888 -1.4498615583
## 1688 1.946417170 1.264015263 -0.826060 -1.273375586 0.3618740767
## 1865 0.232165499 0.855745376 -0.826060 -0.402214631 -0.8012352387
## 1880 -0.201323429 -0.096884363 1.210566 1.034603997 -0.4769220790
## 1886 0.921806976 0.719655413 -0.826060 -0.561861145 0.8203305602
## 1889 -0.989485117 -0.913424139 1.210566 0.555664455 0.1717042406
## 1892 -1.304749793 -1.072195762 -0.826060 0.156548169 1.1446437200
## 1897 0.902102934 1.264015263 -0.826060 -0.003098345 0.1717042406
## 1899 -0.358955767 -1.312621363 -0.826060 -0.721507659 1.1446437200
## 1900 0.744470596 0.175295562 -0.826060 -1.120623945 1.1446437200
## 1904 -0.260435556 -0.033375714 -0.826060 0.954780740 2.4418963591
## 1909 -0.280139598 -0.369064288 -0.826060 -1.040800688 -0.1526089192
## 83 0.685358469 0.991835338 -0.826060 -0.881154174 -0.4769220790
## 208 1.690264622 0.787700394 1.210566 -0.402214631 0.8203305602
## 329 -0.496884062 0.447475488 -0.826060 0.151503876 -0.2106727668
## 449 -0.930372991 1.876420095 -0.826060 2.231952854 0.8203305602
## 451 0.744470596 0.098177917 1.210566 0.076724912 -1.7741747181
## 605 0.902102934 0.991835338 1.210566 -0.322391374 0.8203305602
## 754 -0.102803218 0.011987607 1.210566 0.874957483 -1.7741747181
## 822 -1.147117455 -1.521292639 -0.826060 0.795134226 0.1717042406
## 844 1.256775693 0.855745376 -0.826060 -0.842986742 0.4607757390
## 1074 -0.595404273 0.175295562 -0.826060 -0.078931467 0.6365102374
## 1185 0.113941246 -0.369064288 -0.826060 0.076724912 -1.1255483985
## 1829 0.251869541 -0.096884363 -0.826060 -0.482037888 -0.4769220790
## 1843 -0.102803218 -1.067659430 1.210566 0.715310969 -0.1526089192
## 1888 3.168067786 2.624914890 -0.826060 -0.322391374 -0.4769220790
## 1893 -0.004283007 -0.096884363 1.210566 -0.242568117 -0.4769220790
## 1894 -0.969781075 -0.641244214 1.210566 -0.003098345 1.1446437200
## 30 -0.615108316 -0.700216531 -0.826060 0.143327248 -0.1803602114
## 211 0.094237203 -0.096884363 -0.826060 -0.881154174 -1.1255483985
## 666 -0.437771936 -0.142247684 -0.826060 0.568913453 0.0045611545
## 895 0.035125077 -0.096884363 -0.826060 0.374949460 -0.0943768341
## 1165 1.079439313 0.991835338 1.210566 -0.801330916 0.8203305602
## 1452 -0.142211303 -0.477936259 -0.826060 -0.019253551 -0.2043228968
## 1526 0.113941246 -0.355455292 -0.826060 -0.421643081 0.0997569131
## 1698 -0.418067894 -1.085804759 -0.826060 -0.322391374 0.1717042406
## 1862 0.567134216 1.195970282 -0.826060 -0.436402570 0.3054780646
## 1883 0.350389752 0.991835338 1.210566 -0.242568117 0.8203305602
## 1884 1.315887820 0.878427036 1.210566 -0.003098345 0.4960174004
## 1885 0.094237203 -1.185604064 -0.826060 -0.482037888 0.1717042406
## 1887 -0.890964906 -1.035905106 1.210566 -0.881154174 -0.8012352387
## 96 0.074533161 0.510984137 -0.826060 0.460943254 0.4271790296
## 311 -0.812148738 -0.641244214 1.210566 -0.082921602 1.4689568797
## 437 1.020327187 1.241333603 1.210566 -0.733706387 0.0118434671
## 599 -0.378659809 -0.455254598 -0.826060 -0.628211669 -0.0796079177
## 619 1.138551440 1.536195189 -0.826060 -0.767838265 0.3496908085
## 778 0.429205921 0.039205600 -0.826060 -0.571065702 0.4331622280
## 1008 0.448909963 0.447475488 1.210566 0.032619676 0.5216928581
## 1082 -0.496884062 -0.527835912 1.210566 0.156548169 -1.4498615583
## 1732 2.202569719 1.640530827 -0.826060 -0.881154174 -0.1526089192
## 1784 -0.280139598 -0.641244214 -0.826060 2.231952854 -1.7741747181
## 1820 -0.595404273 -0.831770162 1.210566 1.753013311 0.1717042406
## 1834 0.015421035 0.447475488 1.210566 -0.003098345 0.8203305602
## 1869 -1.265341708 -0.849915490 1.210566 2.152129597 -1.4498615583
## 1870 -0.555996189 -0.641244214 -0.826060 -1.120623945 -1.1255483985
## 1871 1.237071651 0.901108696 -0.826060 -0.721507659 0.1717042406
## 1875 -0.615108316 -0.641244214 -0.826060 -1.040800688 0.4960174004
## 1876 0.764174638 0.801309390 1.210566 -0.561861145 -0.1526089192
## 82 0.606542301 0.397575835 -0.826060 -0.960977431 1.4689568797
## 258 -1.127413413 -1.652846270 -0.826060 0.489537994 -0.1965957076
## 310 -0.398363851 -0.232974326 1.210566 0.510165464 0.2081779969
## 345 0.803582723 0.896572364 -0.826060 -0.641684402 -0.1526089192
## 464 -0.378659809 0.583565450 -0.826060 -0.916545335 0.1762938653
## 595 -0.674220442 -0.369064288 -0.826060 0.076724912 -0.1526089192
## 676 -0.477180020 -0.777334176 1.210566 0.064098560 0.4021671134
## 841 0.448909963 0.447475488 1.210566 -0.591174146 0.3257782750
## 1434 -0.910668949 -1.367057348 -0.826060 -1.120623945 1.7932700395
## 1605 -0.063395134 0.157150234 1.210566 -0.960977431 -0.1526089192
## 1616 -0.831852780 -0.505154251 1.210566 0.475841197 -0.4769220790
## 1769 0.389797836 0.011987607 -0.826060 -0.960977431 0.4960174004
## 1833 1.473520157 1.536195189 -0.826060 -1.280270459 2.4418963591
## 1846 -0.575700231 -1.525828971 -0.826060 0.316194683 1.4689568797
## 1858 1.059735271 0.805845723 -0.826060 0.236371426 0.4960174004
## 1859 -0.083099176 0.084568920 -0.826060 -0.960977431 -0.8012352387
## 1866 -0.910668949 -1.185604064 -0.826060 -0.402214631 0.1717042406
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## 221 -0.023987050 0.447475488 -0.826060 -0.451739437 0.1338714954
## 497 0.842990807 0.991835338 -0.826060 0.506700412 -0.5986145340
## 503 -0.555996189 -0.096884363 1.210566 -0.085940442 0.1423820987
## 516 -1.245637666 -1.004150781 1.210566 0.368843919 0.2259706957
## 637 -1.147117455 -1.367057348 -0.826060 0.198778137 0.0229689619
## 656 0.783878680 0.628928771 -0.826060 -0.207390018 0.0946157484
## 661 0.468614005 0.447475488 1.210566 -0.406664697 0.2158469815
## 664 0.961215060 0.991835338 -0.826060 -0.501966823 0.5410303092
## 671 -1.304749793 -0.949714796 -0.826060 -0.118340098 -0.0455645383
## 674 -0.201323429 0.025596603 1.210566 -0.051476504 0.0430754081
## 678 3.266587997 3.713634591 -0.826060 -1.653719745 0.7282520997
## 683 0.251869541 -0.096884363 1.210566 -0.212617614 0.2138163239
## 206 1.059735271 0.991835338 -0.826060 -0.192959479 0.4520010897
## 256 -0.221027472 0.175295562 -0.826060 0.197398751 0.5170643029
## 453 0.705062512 0.719655413 1.210566 0.345391573 -0.2289016441
## 456 0.409501879 0.901108696 -0.826060 -0.401139370 0.2255161935
## 509 -0.418067894 -0.096884363 -0.826060 0.384861272 0.7014566265
## 544 0.448909963 0.991835338 1.210566 -0.390742029 0.3559020412
## 627 -0.969781075 -0.505154251 -0.826060 0.466107186 0.2478472626
## 634 -0.280139598 -0.096884363 1.210566 0.077204437 -0.1319225719
## 641 1.335591862 0.855745376 -0.826060 -1.146055619 0.1515693586
## 642 -0.122507261 -0.505154251 -0.826060 -0.508966556 0.3103908933
## 643 1.276479735 1.264015263 -0.826060 -0.805275665 0.5652952836
## 653 -0.260435556 -0.228437994 1.210566 -0.087027024 -0.1756305191
## 654 0.035125077 0.447475488 -0.826060 -0.231464841 0.3053889531
## 660 -0.496884062 -0.641244214 1.210566 0.191634437 0.1349939076
## 4 -0.122507261 -0.437109270 -0.826060 0.226580522 -0.0037650896
## 121 -0.063395134 -0.232974326 1.210566 1.397737543 -0.4298818367
## 338 0.764174638 0.855745376 1.210566 0.011472544 -0.2262614529
## 496 0.626246343 0.560883790 -0.826060 -0.722249870 -0.0864096273
## 580 0.113941246 0.669755760 -0.826060 -0.002276930 -0.1113245934
## 608 0.842990807 0.923790357 -0.826060 -1.148281692 0.3174094614
## 615 0.547430174 0.651610432 -0.826060 -0.486736984 0.2818641197
## 616 0.074533161 0.202513555 -0.826060 -0.167304389 0.2915729984
## 623 0.094237203 0.243340544 1.210566 0.182848047 0.3859888649
## 782 1.788784833 1.876420095 -0.826060 -1.121411650 0.5632643438
## hgb glucose male crcl
## 249 -0.7495234781 -8.724623e-01 0.6064784 -0.1317682722
## 318 -1.7420906990 1.710539e+00 0.6064784 -0.7421109797
## 631 0.6331693113 -3.715915e-01 0.6064784 0.5717210286
## 883 -0.0116602710 -3.428591e-01 0.6064784 -0.6744751413
## 1095 -0.5630893558 -5.522727e-01 0.6064784 -0.5717129059
## 1405 -1.8838860163 2.310165e+00 0.6064784 0.0132141255
## 1554 -0.1823422090 8.572262e-01 -1.6488632 -1.6116524451
## 1589 1.0938156464 -3.189619e-01 -1.6488632 0.0534702276
## 1708 -0.6786258195 1.572164e+00 0.6064784 -0.6142916844
## 1773 0.3848390601 -4.573370e-01 -1.6488632 1.7809115145
## 1850 -2.1674766508 -2.036494e-01 0.6064784 -1.1944129455
## 1935 -0.2532398676 -5.495871e-01 0.6064784 2.4691475878
## 2077 1.6609969155 -1.033900e+00 0.6064784 0.0267687661
## 110 -2.8055555786 -2.728369e-01 0.6064784 -1.4777075269
## 116 0.0045601647 -5.544574e-01 0.6064784 -0.4366428617
## 328 -1.2658655449 1.056390e+00 0.6064784 -1.1594996036
## 482 -0.2682909939 6.705646e-01 0.6064784 -1.2250689331
## 723 0.5153346745 -3.571493e-01 0.6064784 0.7262500168
## 732 0.0790890950 -5.337742e-01 0.6064784 -0.7446251221
## 737 -1.1182007111 -3.205041e-01 0.6064784 -1.1760103023
## 802 -0.7767979889 -3.669177e-01 0.6064784 -0.8645531773
## 894 -0.2354897285 -1.720189e-01 0.6064784 -1.0998676824
## 1204 -3.5854298236 -2.036494e-01 0.6064784 -1.6204089903
## 1564 0.3848390601 -1.805869e-01 0.6064784 -0.9252900173
## 1632 -0.5368305022 -9.416498e-01 0.6064784 -0.2896814729
## 1794 -0.3956958107 -4.573370e-01 0.6064784 -1.0601797215
## 2120 -2.0256813336 -7.110247e-01 -1.6488632 -0.8587581602
## 2127 -2.7346579199 3.037259e-01 -1.6488632 -0.8078949117
## 3 -0.4835350299 -2.556703e-01 0.6064784 -1.3335427256
## 188 0.5505319490 -2.848298e-01 0.6064784 0.2672273469
## 320 -0.8856001959 -6.821022e-01 -1.6488632 -1.3446725965
## 539 1.1647133051 -2.497744e-01 0.6064784 -0.3763025179
## 632 1.3065086224 1.987289e+00 0.6064784 0.4556655322
## 712 -0.3301932491 -3.007871e-01 0.6064784 -0.1058987136
## 808 -1.4424330088 1.277462e+00 -1.6488632 -1.3807969424
## 1081 -0.2189560691 -2.845960e-01 0.6064784 -0.6108242014
## 1232 -4.0108157754 -3.881495e-01 0.6064784 -0.4612602127
## 1313 -0.3403860849 -2.354833e-01 0.6064784 0.4889455764
## 1674 -1.9547836749 -1.805869e-01 0.6064784 0.2952536788
## 1717 -2.8764532372 -6.879622e-01 0.6064784 -0.5350887961
## 1727 -1.5293977231 -1.113993e-01 0.6064784 -0.7225172338
## 2104 0.1721460842 7.880387e-01 0.6064784 -0.4961098137
## 2154 -0.0405468917 9.725388e-01 0.6064784 -0.6283865073
## 2181 -2.8764532372 -6.187746e-01 0.6064784 -1.1764543449
## 2197 -2.3092719681 -8.032747e-01 0.6064784 -0.9979999859
## 445 -0.0405468917 -6.648996e-01 0.6064784 -0.1127491480
## 777 -0.1424924721 8.295107e-01 0.6064784 -0.6391643511
## 800 -0.4659328436 -2.267119e-01 0.6064784 0.0143946564
## 904 0.6209605452 -3.441645e-01 0.6064784 0.1879334414
## 906 -0.4142735439 5.223006e-02 0.6064784 -0.3402546005
## 933 -0.5351115828 7.658899e-01 0.6064784 -0.5426619610
## 1155 0.3175913887 -5.243551e-02 0.6064784 0.1653468491
## 1162 -1.7420906990 -3.881495e-01 0.6064784 -1.6921721619
## 1424 -1.0331141127 -7.802122e-01 0.6064784 -0.8843486162
## 1664 -0.2507556966 -3.363121e-01 0.6064784 -1.0820291888
## 2000 0.1721460842 1.203164e+00 0.6064784 -0.5029871951
## 2035 -0.4797418634 -1.078188e-01 0.6064784 -0.9396954117
## 186 2.5117688192 -3.189619e-01 0.6064784 -0.4165874877
## 375 0.0246465717 -4.472596e-01 0.6064784 -0.3315391315
## 792 -0.1961517074 -1.084130e-01 0.6064784 -0.4599958418
## 957 -0.4884751710 -4.822058e-01 0.6064784 -1.2100477501
## 1014 -0.3112262530 -2.361588e-01 0.6064784 -0.5458972627
## 1112 -1.4585000645 2.114759e-01 0.6064784 -1.3349618673
## 1253 3.2916430642 -5.957121e-01 0.6064784 -1.0182344636
## 1420 -0.6639924158 -1.415862e-01 -1.6488632 -0.1977055267
## 1988 -1.0331141127 -4.342745e-01 -1.6488632 3.1260900111
## 88 0.0881157389 -7.148138e-01 0.6064784 -0.9767041095
## 117 -2.1674766508 -4.573370e-01 0.6064784 -1.2015214611
## 229 -1.1749094299 -6.418371e-01 0.6064784 1.2168496557
## 512 -0.0761900211 1.265472e+00 -1.6488632 -0.0660794003
## 583 0.5975320360 -1.575243e-01 0.6064784 -0.9692481825
## 648 0.0713587695 -3.781677e-01 0.6064784 -1.1743711047
## 1058 0.0303507669 3.913244e-03 0.6064784 0.3599682340
## 1203 -0.4659328436 -3.189619e-01 0.6064784 0.3078980425
## 1225 -0.9622164540 -9.647123e-01 -1.6488632 -1.0067707248
## 1415 0.5975320360 -6.879622e-01 0.6064784 0.0383272364
## 1438 -1.7420906990 5.077666e+00 -1.6488632 -1.0183069053
## 1442 0.8102250119 -2.036494e-01 0.6064784 0.7232631895
## 1824 0.2430437428 -9.647123e-01 -1.6488632 -0.8229379922
## 2178 -0.9622164540 2.697576e-02 0.6064784 -0.5554895401
## 200 -1.3786525484 1.995710e-01 -1.6488632 -1.2071917033
## 460 -0.5239184951 -3.686309e-01 -1.6488632 0.2296600006
## 490 0.1664819866 1.106338e+00 0.6064784 0.9584494840
## 505 -0.3239851594 -4.617157e-01 0.6064784 -0.3175939655
## 636 -0.3160456724 -6.475393e-01 -1.6488632 -0.8427002065
## 877 -0.8325921569 4.790793e-01 0.6064784 -0.9763077185
## 885 0.5975320360 2.697576e-02 0.6064784 0.9526617987
## 2214 -0.0405468917 3.913244e-03 -1.6488632 -1.2563711477
## 157 -0.5368305022 5.574136e-01 0.6064784 -0.8289841620
## 170 -1.1604962606 9.226950e-01 0.6064784 -1.0671438161
## 317 -0.6786258195 -2.728369e-01 0.6064784 -1.1099613278
## 399 0.0333264749 -5.313981e-01 0.6064784 0.0103116930
## 489 -2.1674766508 -7.340872e-01 0.6064784 -0.4465489820
## 552 -0.2129792291 -3.984235e-01 0.6064784 -0.7780954072
## 652 -0.1505998327 -4.581505e-01 0.6064784 -0.0814223756
## 876 -0.2307537266 -2.663811e-01 0.6064784 -0.2587408604
## 1573 1.1647133051 1.502977e+00 0.6064784 -0.6625539322
## 1993 1.5192015983 -4.112120e-01 0.6064784 -0.0259043742
## 2040 -1.6002953817 -5.726496e-01 0.6064784 -0.1165369476
## 10 2.7244617951 1.192258e-01 -1.6488632 -0.8836868742
## 238 0.0940582225 -2.059188e-01 0.6064784 0.0879251392
## 360 -0.3720548184 -3.095569e-01 0.6064784 -0.5203041930
## 416 -0.3304860047 -3.964480e-01 0.6064784 -1.2002784870
## 733 0.0932042652 9.175189e-01 0.6064784 0.2804552568
## 967 -0.6947999491 -3.106494e-01 0.6064784 -0.8428361299
## 968 -0.3655259197 -5.503660e-01 0.6064784 -1.1681126013
## 1134 -0.7748245306 -3.881495e-01 -1.6488632 -0.0726329075
## 1135 -0.2667147589 -2.175751e-01 0.6064784 -0.9951414664
## 1183 -0.2532398676 -8.263372e-01 0.6064784 0.0199485174
## 1184 2.5826664778 -2.728369e-01 0.6064784 -0.4717479497
## 1686 0.5266343774 -8.032747e-01 -1.6488632 1.3462917018
## 2069 -0.1653867319 -2.809557e-01 0.6064784 -0.3938639416
## 2204 -1.3876024058 9.616330e-02 0.6064784 1.5048685213
## 38 0.8208136212 -1.428519e-01 0.6064784 -0.3185810573
## 392 -0.2532398676 3.913244e-03 0.6064784 0.8978219940
## 443 0.0303507669 1.192258e-01 0.6064784 -0.9892098860
## 532 -1.1914868658 -5.336099e-01 -1.6488632 -0.7513986192
## 647 -0.4699503594 -1.574050e-02 0.6064784 -0.5908957377
## 724 0.3753606947 -5.138370e-01 0.6064784 -0.1236858476
## 1128 -2.0256813336 -1.126150e+00 0.6064784 1.1049905244
## 1337 -0.0405468917 4.916229e+00 -1.6488632 -0.5502881309
## 1593 0.0523752146 -5.611531e-02 0.6064784 -0.1200843953
## 1777 -0.1114445504 -5.034620e-01 0.6064784 -0.8809076788
## 8 -0.0691467733 -5.968804e-01 0.6064784 -0.8899219857
## 222 0.8811226705 1.064789e+00 0.6064784 0.2962195678
## 228 0.8297996257 -4.646445e-01 0.6064784 -0.1064198383
## 313 1.6609969155 3.729135e-01 0.6064784 0.0546400264
## 342 -0.0184954257 -2.618155e-01 0.6064784 -0.4606532829
## 350 -1.1034936441 -5.095547e-01 0.6064784 -1.4047001094
## 459 1.1736485810 -1.771841e-01 0.6064784 5.3297021814
## 573 0.1616357948 -2.208109e-01 0.6064784 -0.0767324321
## 771 0.0452626573 -4.464931e-01 0.6064784 -0.8887073817
## 788 -1.0688477074 1.264874e+00 0.6064784 -1.1625326467
## 789 -0.9313566196 5.075557e-01 -1.6488632 -0.6140575523
## 886 -0.6892645052 7.226193e-01 -1.6488632 -1.0959707754
## 1029 -0.5368305022 6.035386e-01 -1.6488632 -0.9425984650
## 1067 1.8736898915 3.094290e+00 0.6064784 -1.1747218775
## 1143 -0.4659328436 -4.112120e-01 0.6064784 1.6452041266
## 1251 0.0576091059 1.475354e-01 0.6064784 0.0056327632
## 1360 -0.0405468917 1.595227e+00 0.6064784 -0.3456672544
## 1547 0.5266343774 1.549102e+00 -1.6488632 1.1028065422
## 1906 0.4557367187 -1.575243e-01 -1.6488632 0.1946603764
## 2187 -0.4659328436 3.729135e-01 0.6064784 -0.0591780078
## 2193 0.1012484255 1.041726e+00 0.6064784 0.0047260111
## 2199 -0.3950351849 -7.340872e-01 -1.6488632 -0.4945053650
## 74 -0.7495234781 -6.648996e-01 -1.6488632 0.3174691230
## 241 2.0863828674 -6.187746e-01 0.6064784 0.3578754747
## 348 0.6673254367 -1.677280e-01 0.6064784 0.8046452847
## 442 4.0006196506 -2.958994e-01 0.6064784 -0.7439890970
## 470 1.4483039396 2.010352e+00 0.6064784 -0.9239717579
## 474 0.9520203292 -4.112120e-01 0.6064784 -0.0527882814
## 524 -0.7448695906 5.697787e-01 0.6064784 0.3340071052
## 711 -1.4262932688 6.326999e-01 -1.6488632 -1.1589102550
## 1299 -0.3950351849 -5.495871e-01 0.6064784 -0.3909484554
## 1515 0.5266343774 9.616330e-02 -1.6488632 -0.2731683504
## 1624 0.6684296946 2.114759e-01 0.6064784 -0.9884126444
## 1736 0.2430437428 -4.342745e-01 0.6064784 0.1284124728
## 2044 -0.3241375263 -1.679650e+00 0.6064784 -1.7241648315
## 2067 0.6315261623 -1.576432e-01 0.6064784 -0.2066900946
## 2183 -0.2532398676 -6.527430e-02 -1.6488632 -0.2969714298
## 2185 -0.6786258195 -6.187746e-01 0.6064784 -0.6558249063
## 2189 1.0229179878 -2.958994e-01 0.6064784 -0.1106830755
## 2190 -0.6786258195 -8.833681e-02 -1.6488632 -0.5770848398
## 2191 -0.3950351849 2.379352e+00 -1.6488632 -0.5353845996
## 2192 0.3139414014 -4.803995e-01 0.6064784 0.0356656759
## 7 -3.0891462131 -9.877748e-01 -1.6488632 -0.1311283089
## 62 -3.0891462131 -1.472088e+00 0.6064784 -0.5148265938
## 169 2.0863828674 2.697576e-02 0.6064784 1.4451121958
## 305 -0.3950351849 -7.340872e-01 -1.6488632 -0.3516503994
## 327 0.4557367187 3.809228e+00 0.6064784 0.8315366500
## 542 0.0226901409 -5.772852e-01 -1.6488632 0.1525766165
## 613 -0.1858321420 -3.569257e-01 0.6064784 -0.8992300615
## 635 0.2751170077 -2.993844e-01 0.6064784 0.1860199431
## 812 1.3065086224 -9.185873e-01 0.6064784 -0.5182394012
## 903 -1.3876024058 3.555540e+00 0.6064784 -1.0662375312
## 1355 0.1721460842 -1.241462e+00 0.6064784 -0.6035595857
## 1422 0.1012484255 -1.113993e-01 0.6064784 0.1018344304
## 1509 -1.6002953817 -6.648996e-01 0.6064784 -1.6120548988
## 1601 1.0229179878 -3.650870e-01 0.6064784 -0.5229078641
## 2175 0.3139414014 -1.113993e-01 -1.6488632 0.3010490121
## 2177 -0.4659328436 7.310078e-02 -1.6488632 3.3817902346
## 2184 -0.4659328436 -2.958994e-01 0.6064784 0.5222375660
## 2186 -0.2532398676 -2.958994e-01 -1.6488632 2.1770466918
## 2188 -1.0331141127 5.169916e+00 0.6064784 -0.4562078708
## 174 0.3139414014 -2.267119e-01 0.6064784 0.1387796801
## 253 0.8811226705 2.114759e-01 0.6064784 -0.1813588236
## 286 -0.5368305022 3.913244e-03 -1.6488632 -0.6720858245
## 492 -0.4628995248 5.950590e-01 0.6064784 -0.6080546311
## 534 -0.9927269977 -1.695149e-01 0.6064784 -0.8874335708
## 607 0.3848390601 -4.573370e-01 -1.6488632 -0.7765207711
## 614 -0.1012639904 -1.851472e-01 0.6064784 -0.4197807684
## 663 -0.0057913813 -2.910428e-02 0.6064784 -0.8102453471
## 964 -0.8917086900 -1.573581e-01 0.6064784 -1.1072592537
## 1066 -0.1823422090 4.651635e-01 0.6064784 0.6883704535
## 1433 -0.0405468917 -8.955248e-01 -1.6488632 -0.7175756134
## 1524 1.1647133051 -5.265246e-01 0.6064784 -0.2595743588
## 1555 0.2430437428 -3.420245e-01 0.6064784 -0.6805417551
## 1675 -0.2056519986 1.099645e+00 0.6064784 -0.0310932401
## 1907 -0.3950351849 1.318477e+00 0.6064784 -0.0699673050
## 2021 -1.6002953817 -5.265246e-01 0.6064784 1.0101545456
## 2113 1.3065086224 2.863665e+00 0.6064784 0.3245845046
## 2129 -2.3801696267 8.572262e-01 -1.6488632 -1.6943733978
## 2170 -0.4031987036 -7.110247e-01 -1.6488632 -0.7682982761
## 2172 0.2387559191 -1.204708e-01 0.6064784 0.0604892262
## 2173 0.3848390601 4.421010e-01 0.6064784 2.9356719174
## 2198 -0.8204211367 -6.879622e-01 0.6064784 -0.4024400564
## 2200 0.6684296946 -6.418371e-01 0.6064784 -0.1051609229
## 2201 0.3063287420 -2.265698e-01 0.6064784 0.3990159722
## 1 0.7393273533 -4.803995e-01 0.6064784 -0.9553148521
## 46 0.7393273533 1.041726e+00 0.6064784 -0.0279971334
## 103 -0.4627605434 -3.057891e-01 0.6064784 -0.5996579446
## 220 -1.8838860163 -1.805869e-01 -1.6488632 0.5042478855
## 236 0.2430437428 -4.112120e-01 -1.6488632 -0.7952421855
## 514 -0.7011077577 -6.904883e-01 -1.6488632 -0.8782697879
## 638 -0.2084842920 -3.555446e-01 0.6064784 -0.5849907268
## 1520 -1.3876024058 -1.333713e+00 -1.6488632 -1.2654808884
## 1722 -1.6711930404 1.884134e-01 0.6064784 -0.8301958885
## 1877 -0.1823422090 3.163478e+00 -1.6488632 -1.8103035917
## 2145 -1.6002953817 -5.726496e-01 0.6064784 -0.5078457764
## 2146 -1.1040117713 1.318477e+00 0.6064784 -0.4008302416
## 172 0.4674096857 -4.834775e-02 0.6064784 -0.4714783436
## 269 -0.7495234781 -3.650870e-01 0.6064784 -0.9356107189
## 270 -0.5368305022 -2.267119e-01 -1.6488632 -0.9942738338
## 291 1.0229179878 -4.112120e-01 0.6064784 -0.6267102557
## 343 -1.6711930404 -5.265246e-01 -1.6488632 -0.7202818910
## 707 -0.6127902806 -6.037619e-01 -1.6488632 -1.0919669792
## 745 -0.3977718664 7.741943e-01 0.6064784 -0.5773112321
## 919 0.5919097907 -1.738072e-01 0.6064784 0.2229899100
## 1293 -0.8204211367 -2.728369e-01 0.6064784 -0.2691226244
## 1338 0.0811030104 9.510980e-01 0.6064784 0.5719581574
## 1952 1.4483039396 -1.113993e-01 0.6064784 -0.9212709840
## 1975 -0.2532398676 -4.112120e-01 -1.6488632 0.0493287108
## 2108 2.0863828674 -2.958994e-01 0.6064784 1.8957315552
## 2156 0.3822560039 -2.837224e-01 0.6064784 0.2434559626
## 2164 1.0229179878 1.595227e+00 0.6064784 -0.3754917566
## 2171 -0.4659328436 3.498509e-01 0.6064784 -0.5840023997
## 2195 0.5266343774 -8.833681e-02 0.6064784 0.5647366767
## 237 -1.3876024058 3.959760e-01 -1.6488632 -0.7118665841
## 363 -0.1474881125 -2.601607e-01 0.6064784 -0.7033911991
## 411 -0.6033067928 -7.504266e-01 -1.6488632 -1.0868682128
## 806 0.5337413324 -4.332741e-01 0.6064784 -0.1803214779
## 1854 -0.3241375263 -3.420245e-01 0.6064784 0.0328538661
## 1879 0.0303507669 -6.418371e-01 0.6064784 -0.1916348081
## 2015 -1.5293977231 -7.802122e-01 0.6064784 -1.3759020665
## 2018 -0.3950351849 -6.648996e-01 0.6064784 -1.1907224610
## 2056 1.4483039396 -1.914927e-02 0.6064784 0.8010574896
## 2081 0.2430437428 2.697576e-02 -1.6488632 0.3024564503
## 2132 0.3139414014 -2.036494e-01 0.6064784 0.5551743768
## 2158 -0.2532398676 1.226226e+00 0.6064784 -1.1522806891
## 2159 -0.6786258195 -5.034620e-01 0.6064784 -0.9128922075
## 2163 0.9520203292 -2.958994e-01 -1.6488632 0.0762987431
## 2165 0.5975320360 -1.113993e-01 -1.6488632 -0.7436805491
## 2168 1.1647133051 -3.650870e-01 0.6064784 0.8704258731
## 2169 -1.8838860163 -4.342745e-01 -1.6488632 -1.3913026895
## 2196 -3.3018391890 -3.650870e-01 -1.6488632 -1.1422787432
## 161 -0.3950351849 -5.034620e-01 0.6064784 -0.7598189425
## 180 -0.6786258195 -9.877748e-01 0.6064784 0.1587413836
## 367 0.8102250119 -2.267119e-01 0.6064784 1.2021062608
## 685 -0.2021932174 -3.660879e-01 0.6064784 -0.6148103430
## 990 -0.3613640371 -4.456463e-01 0.6064784 -0.7876928662
## 1086 -0.4041868043 -2.730672e-01 0.6064784 -1.1911483101
## 1100 -0.4648383361 -3.632657e-01 -1.6488632 -0.4319687802
## 1969 -1.4585000645 1.422883e-01 0.6064784 -1.1148233518
## 2135 0.3139414014 -1.575243e-01 -1.6488632 0.1842951813
## 2153 0.4557367187 -1.113993e-01 0.6064784 -0.1128343735
## 2155 -0.8204211367 3.555540e+00 -1.6488632 -0.6833974565
## 2166 -0.3241375263 -5.495871e-01 -1.6488632 -0.2523718822
## 223 -0.0405468917 8.802888e-01 0.6064784 0.4685770725
## 681 0.1469781598 -7.090201e-02 0.6064784 1.6854791993
## 963 -1.2131035662 -4.096223e-01 0.6064784 -0.6939136594
## 1192 0.6571910283 -2.468664e-01 0.6064784 0.1473679210
## 1500 1.0938156464 -5.034620e-01 0.6064784 0.5020343902
## 1522 -1.4585000645 -2.728369e-01 0.6064784 -0.4936123912
## 1523 2.4408711605 6.727261e-01 0.6064784 0.2356905313
## 1545 -1.9547836749 -3.650870e-01 0.6064784 -1.2417617206
## 1560 -0.2532398676 -6.418371e-01 0.6064784 -0.2285800578
## 2019 -0.8913187954 -5.726496e-01 0.6064784 -0.1326672919
## 2095 0.1721460842 -5.495871e-01 0.6064784 1.1307878066
## 2121 -0.5368305022 -7.571497e-01 -1.6488632 0.6258023182
## 2147 -0.2532398676 -7.802122e-01 -1.6488632 -0.2166497203
## 2151 0.4557367187 3.729135e-01 0.6064784 -0.2886632995
## 2157 0.9520203292 -4.803995e-01 0.6064784 2.0627230106
## 2231 -2.1674766508 -3.881495e-01 0.6064784 -0.2489808017
## 112 -1.3167047472 2.725290e+00 -1.6488632 -0.5176455584
## 176 0.5266343774 -3.881495e-01 0.6064784 -1.0418354985
## 219 -0.3950351849 -2.267119e-01 0.6064784 -1.1288177645
## 382 -0.7587622442 -4.248422e-01 0.6064784 -1.0879271889
## 684 0.3584672977 -2.479270e-01 0.6064784 -0.1846937254
## 1268 -0.0405468917 2.114759e-01 -1.6488632 0.8454213026
## 1617 1.7318945742 -6.879622e-01 0.6064784 -0.5167076380
## 1786 -0.7495234781 3.267884e-01 0.6064784 -0.4290556611
## 2143 -0.7495234781 5.112885e-01 -1.6488632 0.1957961007
## 2148 1.4483039396 -7.110247e-01 0.6064784 0.5735906580
## 2152 1.8736898915 -6.879622e-01 0.6064784 -0.3211444089
## 98 -1.8129883577 -7.802122e-01 0.6064784 -0.4158161540
## 99 -0.9464037890 -3.364869e-01 0.6064784 -0.7412531996
## 209 0.4557367187 -7.802122e-01 -1.6488632 -0.3267465644
## 254 -0.4034206728 1.221192e+00 0.6064784 -0.6236089630
## 331 -0.3950351849 3.324915e+00 -1.6488632 -0.1510513769
## 357 -0.0559289442 1.146735e+00 0.6064784 -0.2416681183
## 410 0.8811226705 5.343511e-01 0.6064784 -0.9313715399
## 1426 -1.1749094299 -4.342745e-01 0.6064784 -1.1729717199
## 1709 1.0938156464 2.217915e+00 0.6064784 2.8262045109
## 1746 -2.5928626027 1.595227e+00 0.6064784 -1.1727163166
## 1796 0.5266343774 -1.575243e-01 0.6064784 -0.1840253373
## 2118 1.7318945742 -2.958994e-01 0.6064784 -0.0749715294
## 2126 -1.0552733701 -3.950361e-01 -1.6488632 -1.0251619263
## 2134 -1.2458070886 -7.802122e-01 0.6064784 -1.8679547046
## 2136 0.5266343774 -2.728369e-01 0.6064784 0.2425322440
## 2137 1.0938156464 1.133976e+00 0.6064784 0.8291755883
## 2138 -1.0331141127 -4.221178e-02 0.6064784 -0.6326033278
## 2141 1.8027922328 -4.342745e-01 -1.6488632 -1.2649453928
## 2142 1.2356109637 -1.805869e-01 0.6064784 1.5963060020
## 2144 0.8811226705 -1.113993e-01 0.6064784 -0.0002009978
## 2220 0.7393273533 1.653508e-01 0.6064784 -0.0940986058
## 64 -0.6077281608 4.421010e-01 -1.6488632 -0.6691742955
## 261 0.4557367187 -3.189619e-01 0.6064784 -1.0754633798
## 478 -0.3700537029 1.473655e+00 0.6064784 -0.3540039556
## 485 0.8811226705 3.913244e-03 0.6064784 0.5531460102
## 604 -1.6980285777 -2.843587e-01 -1.6488632 -1.3821218256
## 828 -0.3502130045 8.832762e-01 0.6064784 -0.5660827561
## 1392 0.9520203292 -2.497744e-01 0.6064784 -0.4603176333
## 1463 2.5826664778 -6.648996e-01 0.6064784 -0.3530417393
## 1647 1.0229179878 -8.724623e-01 0.6064784 0.6289682873
## 1881 -0.5368305022 7.310078e-02 -1.6488632 -0.8620919404
## 2124 -2.0965789922 1.226226e+00 0.6064784 -1.4490417212
## 2131 0.3848390601 -3.420245e-01 -1.6488632 1.1028977651
## 2140 -1.4585000645 3.037259e-01 0.6064784 -0.7363156464
## 2213 -0.6077281608 -5.265246e-01 -1.6488632 -0.7037775743
## 159 0.0696348972 -5.573272e-01 0.6064784 -0.2678749498
## 454 -0.1114445504 4.190385e-01 0.6064784 1.3010337710
## 458 -0.1529928839 1.196565e+00 0.6064784 0.2575120632
## 475 -1.8129883577 -7.802122e-01 0.6064784 -0.7616587308
## 493 0.1230764647 -2.402647e-01 0.6064784 -0.6593636152
## 939 -0.2773805622 -1.161626e-01 0.6064784 -0.7991437694
## 1150 -0.6355925543 1.636983e-01 -1.6488632 -0.1767333118
## 1213 0.1721460842 7.310078e-02 -1.6488632 2.0205630343
## 1264 0.3848390601 -2.267119e-01 0.6064784 0.6931998980
## 1300 -2.3092719681 8.111012e-01 0.6064784 -0.9183941732
## 1562 -2.7346579199 -8.032747e-01 -1.6488632 -1.3218052899
## 1661 -1.1040117713 4.651635e-01 0.6064784 0.0109603848
## 1776 -0.3241375263 -1.113993e-01 -1.6488632 0.0300259581
## 2112 -0.7495234781 9.264138e-01 0.6064784 -0.7975805983
## 2115 -0.1823422090 -9.416498e-01 0.6064784 0.6837709826
## 2122 1.5192015983 4.190385e-01 0.6064784 0.2360326168
## 2123 3.0080524296 -6.187746e-01 0.6064784 1.5684025455
## 2125 1.3774062810 2.806634e-01 0.6064784 0.0674970806
## 2128 -1.2458070886 -4.112120e-01 -1.6488632 0.4351465851
## 45 0.1721460842 3.959760e-01 0.6064784 -0.8246077967
## 179 1.0229179878 -6.187746e-01 0.6064784 1.4036290569
## 306 -0.1114445504 6.266011e-01 0.6064784 1.1514117294
## 1307 -0.5368305022 -9.877748e-01 0.6064784 -0.6932964416
## 1872 0.6684296946 -2.497744e-01 0.6064784 -0.3103338834
## 2082 -2.0965789922 3.417165e+00 0.6064784 -1.3804204356
## 2117 1.8736898915 -7.110247e-01 0.6064784 0.0875875694
## 2119 -0.4659328436 9.616330e-02 0.6064784 -0.4528093729
## 304 -0.2532398676 -7.571497e-01 0.6064784 -0.6014489396
## 334 0.9520203292 8.802888e-01 0.6064784 -1.0657595109
## 513 0.0269964009 8.684314e-01 0.6064784 0.2778269899
## 598 -0.7495234781 -2.958994e-01 0.6064784 -0.0360462074
## 909 -0.5958301760 1.538904e+00 0.6064784 -0.5429935218
## 1030 -0.6677465631 -3.509569e-01 0.6064784 -1.0024346833
## 1238 -0.3241375263 3.209603e+00 0.6064784 -0.4482996556
## 1700 -1.5293977231 -5.726496e-01 -1.6488632 -1.0970139706
## 2017 -1.9547836749 -1.010837e+00 0.6064784 -0.5322716202
## 2087 1.8736898915 7.245542e+00 0.6064784 1.1085321168
## 2106 1.2356109637 -5.034620e-01 0.6064784 0.1115416136
## 2109 -0.1575656900 -1.000514e-01 -1.6488632 0.0155292484
## 2110 0.9520203292 -9.416498e-01 -1.6488632 -0.5142094981
## 2111 -0.1823422090 -3.189619e-01 -1.6488632 0.1335638801
## 2114 -0.5368305022 -1.914927e-02 0.6064784 1.6571570015
## 40 1.3774062810 -5.265246e-01 0.6064784 0.9246912665
## 95 -0.6786258195 -3.420245e-01 0.6064784 -0.9826173111
## 234 0.4557367187 1.884134e-01 0.6064784 -0.8034248010
## 358 0.3139414014 -2.497744e-01 0.6064784 -0.0169040460
## 450 0.2025437134 -2.780062e-01 0.6064784 0.1999103618
## 621 -0.3412724368 -3.040338e-01 -1.6488632 -0.0821040083
## 934 -0.7828608284 -2.745877e-01 0.6064784 -1.3725004247
## 965 -0.2460829872 -2.490642e-01 0.6064784 -0.5977723242
## 1033 -0.5915898558 -1.910845e-01 0.6064784 -1.2643043656
## 1068 -0.1823422090 6.496636e-01 -1.6488632 -1.3482485103
## 1120 1.6609969155 -5.265246e-01 0.6064784 0.0822751805
## 1234 0.1721460842 -2.036494e-01 0.6064784 0.0534099628
## 2011 -0.3950351849 9.616330e-02 0.6064784 0.5466819847
## 2068 -0.4659328436 -5.495871e-01 0.6064784 -0.2173113542
## 2078 -2.0965789922 1.422883e-01 -1.6488632 -0.1782129772
## 2100 1.8736898915 1.192258e-01 0.6064784 0.4454347651
## 2102 -0.7495234781 1.041726e+00 -1.6488632 2.1045245350
## 2105 -0.3241375263 2.576009e-01 -1.6488632 0.0066598795
## 2203 -0.3241375263 7.310078e-02 0.6064784 0.1648091471
## 193 -2.6637602613 5.003827e-02 0.6064784 -1.3517548444
## 387 -2.2383743095 2.079539e+00 0.6064784 -1.6319092813
## 731 2.0863828674 -3.420245e-01 0.6064784 -0.4735294781
## 1262 0.8811226705 6.727261e-01 0.6064784 1.5443626444
## 1342 0.2430437428 -7.110247e-01 -1.6488632 0.6445942231
## 1465 1.5900992569 -4.803995e-01 0.6064784 0.2401980126
## 2048 0.3848390601 -2.036494e-01 0.6064784 2.9128125473
## 2093 0.3996916403 -5.034620e-01 0.6064784 0.3067236094
## 2099 -1.0331141127 -1.805869e-01 0.6064784 -0.4796253101
## 2101 2.2281781846 -2.497744e-01 0.6064784 -0.0263873186
## 2103 1.3065086224 -4.342745e-01 -1.6488632 0.7692046208
## 2205 0.8102250119 -1.472088e+00 -1.6488632 0.9861021238
## 72 -1.1749094299 -2.036494e-01 0.6064784 -1.2444805189
## 299 0.4557367187 3.729135e-01 0.6064784 1.0188976288
## 1043 0.5266343774 -4.803995e-01 -1.6488632 0.4765389483
## 1049 0.0033980563 6.840555e-01 0.6064784 -0.5943800791
## 1240 -1.3876024058 -5.495871e-01 -1.6488632 -0.6329512266
## 1561 -3.0891462131 -2.958994e-01 0.6064784 -1.2315751703
## 1723 0.3139414014 2.345384e-01 0.6064784 0.4444835110
## 1992 1.2356109637 -3.881495e-01 0.6064784 3.2986851553
## 2005 -0.7229027182 2.702227e+00 -1.6488632 0.2794976165
## 2088 1.0938156464 -1.113993e-01 0.6064784 1.8999573189
## 2094 0.1721460842 -4.221178e-02 0.6064784 1.3215642758
## 2097 -0.0405468917 -3.420245e-01 0.6064784 0.1806992575
## 84 -2.3801696267 -2.728369e-01 0.6064784 0.0931414304
## 273 -0.9314495891 7.310078e-02 -1.6488632 -0.8703617053
## 370 -1.5293977231 -4.221178e-02 0.6064784 0.0153390810
## 418 1.1647133051 -2.267119e-01 0.6064784 -0.1138539229
## 473 0.3871206694 -2.597198e-01 0.6064784 -0.2923039491
## 646 -0.1823422090 -1.575243e-01 0.6064784 -0.2819228811
## 670 -0.2341107017 6.407558e-01 0.6064784 -0.3102587945
## 1381 -0.3241375263 -4.573370e-01 -1.6488632 -0.7372493390
## 1512 -3.0891462131 -3.650870e-01 0.6064784 -0.2073626987
## 1738 1.1647133051 -7.802122e-01 0.6064784 2.6918171711
## 1928 0.2430437428 -6.648996e-01 -1.6488632 -0.4501710653
## 1973 0.1721460842 1.192258e-01 0.6064784 -0.7891405692
## 1976 -0.2532398676 1.318477e+00 0.6064784 -1.6749603286
## 2024 0.1721460842 2.576009e-01 0.6064784 0.8379954221
## 2049 -1.7420906990 -3.189619e-01 0.6064784 0.3470897155
## 2070 -1.2458070886 5.804761e-01 0.6064784 3.2003970181
## 2079 -0.9622164540 -4.112120e-01 0.6064784 1.2781261064
## 2080 -0.6786258195 1.192258e-01 -1.6488632 0.1466999689
## 2083 0.8811226705 7.649762e-01 0.6064784 1.1230204500
## 2085 0.6684296946 -9.647123e-01 0.6064784 -0.8484660776
## 2086 -0.3950351849 -4.112120e-01 -1.6488632 0.4930150370
## 2090 0.5975320360 -9.877748e-01 0.6064784 -0.5021197890
## 2091 0.5266343774 -4.573370e-01 0.6064784 -0.4612442914
## 2092 2.0863828674 -1.033900e+00 0.6064784 -0.3219493163
## 75 -1.5293977231 -7.110247e-01 -1.6488632 -0.1982439922
## 106 0.0593388237 2.806634e-01 0.6064784 -0.9583091076
## 413 0.0303507669 -4.342745e-01 0.6064784 1.4047380405
## 432 0.3848390601 -2.497744e-01 0.6064784 -1.0147492146
## 531 -0.3387106733 -2.574509e-01 0.6064784 0.9393561632
## 839 0.5894209972 -4.345934e-01 0.6064784 0.4291227230
## 1062 -1.5293977231 -3.881495e-01 0.6064784 -1.4311653755
## 1163 0.2430437428 -1.914927e-02 0.6064784 2.0526884984
## 1177 -0.8204211367 -4.803995e-01 0.6064784 3.5111002183
## 1855 0.1721460842 -5.957121e-01 0.6064784 0.7341882594
## 2033 0.6684296946 -7.340872e-01 -1.6488632 1.3989462845
## 2071 0.3139414014 -8.493998e-01 0.6064784 0.1586340626
## 2073 -0.8204211367 3.729135e-01 0.6064784 1.3438870410
## 2076 -0.0818071667 1.433876e+00 0.6064784 -0.4114934813
## 2084 1.3774062810 -1.805869e-01 0.6064784 0.1909376797
## 78 1.5900992569 -4.221178e-02 0.6064784 -0.5298878560
## 264 -0.3950351849 -6.418371e-01 0.6064784 -0.4908188891
## 315 -0.3950351849 1.422883e-01 0.6064784 1.8934778143
## 439 0.2157523960 -3.624574e-01 0.6064784 -0.2539000900
## 529 -0.3950351849 4.431916e+00 0.6064784 -0.3373115490
## 758 -0.6786258195 2.863665e+00 0.6064784 5.2173160969
## 1092 -0.5348835724 -7.110247e-01 0.6064784 -1.0132737843
## 1130 0.2430437428 5.574136e-01 0.6064784 -0.2986193849
## 1412 0.5266343774 -6.418371e-01 0.6064784 0.3906717927
## 1614 1.5900992569 4.616416e+00 0.6064784 -0.8200260161
## 1802 -0.0405468917 1.884134e-01 0.6064784 0.0461616685
## 1970 -0.4659328436 -9.877748e-01 0.6064784 -1.9791902382
## 1983 -0.8204211367 1.192258e-01 -1.6488632 -0.5166725148
## 2012 -1.3167047472 2.576009e-01 0.6064784 -0.7966128696
## 2053 1.3774062810 -3.189619e-01 0.6064784 1.3361599300
## 2057 1.0229179878 -5.957121e-01 0.6064784 0.3715296311
## 2058 0.5266343774 -1.805869e-01 0.6064784 1.6542593348
## 2059 -0.0405468917 -2.267119e-01 0.6064784 4.2192309197
## 2074 -2.1674766508 4.639478e+00 0.6064784 0.8735869638
## 2075 -0.7495234781 -6.527430e-02 0.6064784 0.5081882731
## 2215 -0.6077281608 -2.036494e-01 -1.6488632 1.1246392083
## 37 -0.0405468917 -8.955248e-01 0.6064784 -0.6422146539
## 65 -0.3241375263 -4.342745e-01 -1.6488632 -0.2943320490
## 100 0.0897055054 8.109027e-01 0.6064784 -0.1856226756
## 108 0.6542587061 1.136927e-01 0.6064784 0.3008846709
## 173 0.5266343774 -4.342745e-01 0.6064784 0.0116628494
## 207 -0.3241375263 3.913244e-03 0.6064784 0.5184947465
## 824 -0.0578351518 -2.599659e-01 0.6064784 -0.6360145006
## 1141 -0.0843815387 2.310165e+00 0.6064784 -0.1909103914
## 1145 -0.2288393594 -6.830397e-01 -1.6488632 -0.5325831285
## 1618 0.1012484255 -2.728369e-01 0.6064784 0.8338977116
## 1812 -0.7495234781 -3.420245e-01 0.6064784 0.1722235826
## 1856 -1.9547836749 3.037259e-01 0.6064784 0.1123572531
## 1937 -1.8838860163 -4.573370e-01 0.6064784 0.2383540430
## 2047 1.8736898915 6.957886e-01 0.6064784 1.1874130422
## 2061 1.5192015983 -3.881495e-01 0.6064784 0.4149031639
## 2062 0.5266343774 -1.113993e-01 0.6064784 -1.0249847098
## 2063 1.2356109637 3.278790e+00 0.6064784 1.9919984802
## 2221 0.1174628952 6.361838e-01 0.6064784 0.5044248970
## 295 0.7260641716 -4.836585e-01 0.6064784 0.3542131013
## 337 0.5981741944 -4.606581e-01 0.6064784 0.7016957377
## 372 -0.8903706874 -5.632262e-01 -1.6488632 -1.1448349402
## 546 -0.1215642872 7.854448e-01 0.6064784 -0.9731172151
## 874 -0.7727976601 -3.905125e-01 -1.6488632 -0.8976072201
## 972 0.4295455887 -2.538487e-01 0.6064784 1.0250484258
## 1210 -1.7420906990 -7.340872e-01 0.6064784 -0.0133917228
## 1298 -0.7495234781 -1.575243e-01 0.6064784 0.3268704416
## 1498 -2.5219649440 -2.958994e-01 -1.6488632 -0.9127761151
## 1979 0.1721460842 -8.263372e-01 0.6064784 -0.2156210486
## 2066 1.5192015983 6.727261e-01 0.6064784 1.0837731653
## 383 1.3065086224 -5.265246e-01 0.6064784 1.0167726732
## 434 -0.1823422090 -4.573370e-01 0.6064784 -0.9587920520
## 843 0.2738950192 9.163161e-02 0.6064784 -0.5476623780
## 865 0.4466433609 -3.442337e-01 0.6064784 0.5667127034
## 922 -0.3308085422 -3.332508e-01 -1.6488632 -0.7248708183
## 1418 0.5975320360 -4.342745e-01 0.6064784 -0.1287715401
## 1485 -0.6786258195 -8.724623e-01 -1.6488632 2.9908195730
## 1842 -0.0293480677 -1.056962e+00 0.6064784 -0.0577787073
## 2037 1.1647133051 -5.265246e-01 0.6064784 -0.0695303553
## 2052 -2.6637602613 -1.344618e-01 -1.6488632 -0.0089870093
## 371 0.0825407079 -2.607257e-01 0.6064784 -0.5012130430
## 376 -0.0107659460 7.484398e-01 0.6064784 -0.7200164645
## 377 0.3139414014 1.687477e+00 0.6064784 -0.5266257697
## 506 0.5021765263 -2.281353e-01 0.6064784 -0.4288375512
## 728 0.2366525025 -6.459497e-01 0.6064784 -0.5318030125
## 826 0.1105749182 -4.870829e-01 -1.6488632 -0.5580454839
## 1642 0.3848390601 -2.267119e-01 -1.6488632 0.1997916611
## 1707 -1.6002953817 8.572262e-01 -1.6488632 -0.5512763783
## 1751 0.0303507669 -4.342745e-01 0.6064784 -0.0439343000
## 1864 1.4483039396 -1.113993e-01 -1.6488632 -1.0351997164
## 1895 -2.0965789922 -7.571497e-01 0.6064784 -1.1455475145
## 1998 -0.6786258195 -1.264525e+00 0.6064784 0.1955524821
## 31 -1.5293977231 -5.265246e-01 0.6064784 -0.1763635195
## 35 -1.6002953817 -8.263372e-01 0.6064784 -0.7662582017
## 90 -0.4821664768 -3.590723e-01 -1.6488632 -0.9132323839
## 312 -0.3101972756 -3.802805e-01 0.6064784 -0.4683186747
## 457 0.0303507669 -6.879622e-01 0.6064784 0.7658204324
## 466 0.8811226705 -3.650870e-01 0.6064784 -0.7068931851
## 870 -1.6711930404 7.419137e-01 -1.6488632 -0.4585179550
## 913 -1.0051587419 -5.879935e-01 -1.6488632 -0.8824316965
## 1013 0.4898738520 -3.903087e-01 0.6064784 -0.0868354145
## 1117 -2.0965789922 -7.110247e-01 0.6064784 1.4031282258
## 1306 0.3848390601 1.526039e+00 -1.6488632 0.3215741509
## 1619 0.8102250119 4.882260e-01 0.6064784 -0.9573556019
## 1756 0.9520203292 -4.342745e-01 0.6064784 -0.2026620395
## 2026 0.4763998527 -3.828844e-01 0.6064784 0.0736766825
## 2028 -0.1114445504 -5.726496e-01 0.6064784 -0.6981535661
## 2038 1.0534590550 -4.418251e-01 0.6064784 0.7837957679
## 2039 1.2356109637 -8.833681e-02 0.6064784 0.4590612783
## 2043 -0.6077281608 -8.955248e-01 -1.6488632 1.3242271776
## 2227 -2.2383743095 3.913244e-03 0.6064784 -1.1533435369
## 162 -1.2458070886 -8.032747e-01 -1.6488632 0.5617769173
## 528 0.4609991194 -2.946182e-01 0.6064784 0.6695664283
## 581 -0.1736498654 1.106888e+00 0.6064784 -0.5837931358
## 600 0.5975320360 5.343511e-01 0.6064784 -0.1379035804
## 601 -0.9744206869 1.420392e+00 0.6064784 -0.7905687382
## 814 0.1420741971 -4.651524e-01 0.6064784 -0.3520775846
## 1318 -0.3241375263 -1.010837e+00 0.6064784 1.5279507888
## 1477 -0.7495234781 -2.728369e-01 -1.6488632 0.8141774804
## 1982 1.3774062810 3.498509e-01 0.6064784 0.7246644600
## 2029 0.0303507669 3.498509e-01 0.6064784 0.1973769389
## 2034 0.2575626212 -1.012409e-01 -1.6488632 2.3888594097
## 2041 0.1012484255 5.003827e-02 0.6064784 -0.1707438023
## 2042 1.1647133051 -4.803995e-01 0.6064784 -0.1405553844
## 2224 1.2356109637 -5.957121e-01 0.6064784 3.0615134403
## 2229 -0.0405468917 -2.958994e-01 0.6064784 -0.2596866330
## 612 -0.3702139572 1.094949e+00 0.6064784 -1.0637597320
## 703 -2.8055555786 -6.418371e-01 -1.6488632 -0.1375172248
## 916 -0.4910024662 -4.943792e-01 0.6064784 -0.9631002707
## 994 -0.2806824385 1.890067e-01 -1.6488632 0.0490781126
## 1188 0.9958240158 -2.578842e-01 0.6064784 0.0926485200
## 1216 1.0938156464 9.616330e-02 0.6064784 -0.4641189607
## 1890 0.5975320360 -6.187746e-01 0.6064784 0.8895513091
## 1991 -0.6786258195 -1.344618e-01 0.6064784 -0.1286642191
## 2031 -0.7495234781 -3.420245e-01 0.6064784 -0.2044006395
## 2032 1.4483039396 -4.342745e-01 0.6064784 1.0778383147
## 97 -0.8278780546 1.038550e+00 0.6064784 -0.7424325677
## 201 1.7318945742 -4.221178e-02 0.6064784 0.3953841594
## 341 -1.5293977231 1.318477e+00 0.6064784 -0.9636214965
## 717 0.0961100568 -5.660364e-01 0.6064784 -0.7209974437
## 1364 -0.0405468917 -3.650870e-01 0.6064784 2.3040521364
## 1439 -0.3248956612 -4.887097e-01 -1.6488632 -0.8694987729
## 1474 -1.3876024058 -7.340872e-01 0.6064784 0.5763675887
## 1489 0.6684296946 -6.187746e-01 -1.6488632 -1.2340629533
## 1669 0.9520203292 3.670853e+00 0.6064784 0.8346131849
## 1687 0.3139414014 8.111012e-01 -1.6488632 -0.8641817727
## 1704 0.3139414014 -5.726496e-01 -1.6488632 -0.7286287807
## 1706 -0.3950351849 -5.495871e-01 -1.6488632 0.1157363311
## 1780 0.4557367187 -8.833681e-02 0.6064784 0.9623931292
## 1984 0.6684296946 -4.573370e-01 -1.6488632 0.9435444976
## 2022 0.8102250119 -3.650870e-01 0.6064784 -0.0232964742
## 2025 1.0938156464 -5.265246e-01 -1.6488632 -0.1040976786
## 2217 -1.5293977231 -4.112120e-01 -1.6488632 -0.4353929654
## 2222 0.4351046769 -3.323013e-01 -1.6488632 2.2977824004
## 135 -1.1672440431 2.260644e-01 -1.6488632 -1.2094272018
## 166 -3.1600438718 3.267884e-01 0.6064784 -1.3128593191
## 468 0.5266343774 -8.955248e-01 0.6064784 1.0576619692
## 626 -1.8129883577 3.301853e+00 0.6064784 -1.2849405310
## 1031 0.0633329958 -3.684865e-01 0.6064784 -0.8452616616
## 1362 1.7318945742 1.018664e+00 0.6064784 -0.4405831977
## 1510 -1.9547836749 -3.650870e-01 0.6064784 -0.3523748161
## 1625 0.6242500072 -2.864494e-01 0.6064784 0.6437230929
## 1696 -0.4659328436 1.422883e-01 0.6064784 -0.0131063465
## 1699 -0.2532398676 -1.010837e+00 0.6064784 0.4593474676
## 1705 1.2636651630 -6.423827e-01 0.6064784 0.0697341988
## 1715 -0.1823422090 -1.575243e-01 0.6064784 0.4418756110
## 1861 1.1647133051 -6.187746e-01 0.6064784 0.9893414290
## 1941 0.5975320360 2.310165e+00 0.6064784 0.2660541151
## 1964 0.5975320360 1.249289e+00 -1.6488632 0.3276860810
## 2218 1.3821546708 -5.455031e-01 -1.6488632 1.4703063989
## 2228 -0.7587708426 1.247937e+00 0.6064784 -0.4483608006
## 2230 1.3065086224 -7.110247e-01 0.6064784 2.3535628850
## 255 1.3065086224 -6.187746e-01 0.6064784 0.3219766046
## 819 -0.4295823612 -2.882506e-01 0.6064784 -0.7508093498
## 917 -0.3787870552 7.420615e-01 0.6064784 -0.6335160010
## 1085 0.2086875102 1.246345e+00 0.6064784 -0.1486573855
## 1187 -0.6786258195 1.041726e+00 0.6064784 1.0808433023
## 1394 0.9520203292 -5.726496e-01 0.6064784 0.2548795235
## 1479 1.3065086224 -1.805869e-01 0.6064784 -0.8825673212
## 1517 0.9520203292 1.157039e+00 -1.6488632 0.0144434386
## 2009 0.7393273533 1.064789e+00 0.6064784 -0.0403555578
## 2223 0.1012484255 -5.265246e-01 0.6064784 -0.4676251727
## 2225 -0.5368305022 -4.112120e-01 -1.6488632 2.4778865822
## 2226 0.3848390601 -1.033900e+00 0.6064784 0.1049735692
## 27 0.6684296946 -4.803995e-01 0.6064784 -1.0949823843
## 105 0.9520203292 -4.573370e-01 0.6064784 1.3760833370
## 147 0.6915586903 -4.410241e-01 0.6064784 0.4016420769
## 224 0.1565816986 -5.771358e-01 0.6064784 -1.0811715264
## 274 -0.1823422090 -4.573370e-01 0.6064784 0.8279853010
## 356 1.3774062810 2.056477e+00 0.6064784 0.4093358879
## 431 -0.0537298661 1.414284e+00 0.6064784 -0.3713031910
## 551 0.3848390601 -1.195337e+00 0.6064784 0.3225034530
## 962 -0.8383952111 -1.601245e-01 0.6064784 -0.9002695662
## 997 0.4245619101 -1.065979e-01 0.6064784 -0.6843632386
## 1389 0.5266343774 5.804761e-01 0.6064784 -0.7846100904
## 1831 -1.0331141127 -4.342745e-01 0.6064784 -0.7778488683
## 1996 0.2430437428 9.494763e-01 0.6064784 0.1474726800
## 1999 1.5900992569 -3.189619e-01 0.6064784 -0.2597138761
## 2008 0.5975320360 -6.879622e-01 0.6064784 0.7003290778
## 2216 -1.1749094299 -4.342745e-01 -1.6488632 -1.2848810917
## 107 -1.2458070886 -8.955248e-01 0.6064784 -0.0969264763
## 148 1.8027922328 -7.110247e-01 0.6064784 0.5607658001
## 182 -0.1768015705 -2.958994e-01 -1.6488632 -0.1587140954
## 414 -3.0182485545 -3.650870e-01 0.6064784 -1.2010691798
## 511 -0.0405468917 -1.914927e-02 0.6064784 -0.4577681528
## 699 0.6583120698 -4.152127e-01 0.6064784 2.2731788542
## 1017 -0.4892799367 -2.361457e-01 0.6064784 -1.0494281317
## 1366 -1.5293977231 -4.112120e-01 -1.6488632 2.1487024527
## 1672 -0.8204211367 -1.575243e-01 -1.6488632 -0.0749259179
## 1931 0.3848390601 -2.497744e-01 0.6064784 -0.2530492428
## 1966 0.9028921710 -2.008534e-01 0.6064784 1.8914026186
## 1994 0.1721460842 -2.958994e-01 0.6064784 0.6846448822
## 2002 0.4557367187 -5.034620e-01 0.6064784 2.0148578506
## 2007 0.1073902798 -1.896484e-01 0.6064784 -0.2924979750
## 2 -0.3241375263 -4.573370e-01 -1.6488632 -0.7137138466
## 143 1.4483039396 -1.344618e-01 0.6064784 0.0658674527
## 160 1.6609969155 -7.340872e-01 0.6064784 0.8358294893
## 688 -0.8668652606 -2.360700e-01 0.6064784 -1.1389963594
## 776 0.1951344997 -2.605057e-01 0.6064784 1.5174761059
## 850 -1.2126460771 6.244720e-01 0.6064784 -1.2281429161
## 1194 0.7393273533 9.956013e-01 0.6064784 1.2390702105
## 1849 -0.0550486725 -7.825202e-01 -1.6488632 -0.1596778488
## 5 -0.7495234781 -1.344618e-01 0.6064784 -1.0770383153
## 55 -1.4585000645 -3.881495e-01 0.6064784 0.0766408287
## 347 -1.1040117713 9.494763e-01 0.6064784 -0.7421109797
## 856 -0.7241787222 1.194754e+00 -1.6488632 -0.9793936093
## 1972 3.0789500883 -6.648996e-01 -1.6488632 -0.9345011850
## 1977 -1.9547836749 -3.189619e-01 -1.6488632 -1.5787930586
## 1980 0.0303507669 8.802888e-01 -1.6488632 -0.7573943826
## 1981 0.2430437428 -7.110247e-01 -1.6488632 -0.2444635638
## 1985 -1.3876024058 9.494763e-01 0.6064784 -0.8871874896
## 1987 1.0229179878 -5.495871e-01 0.6064784 -0.1106830755
## 1995 0.9520203292 5.343511e-01 0.6064784 -0.4708974309
## 2207 -0.1621835084 -5.328688e-01 -1.6488632 -0.3629342754
## 244 -0.0357672523 3.372700e-02 0.6064784 -0.5071842111
## 308 -2.4510672854 -6.648996e-01 0.6064784 0.4927779550
## 560 0.1721460842 -8.032747e-01 -1.6488632 0.5807040272
## 561 0.5699264395 -1.525443e-01 0.6064784 0.3215997851
## 1173 0.5975320360 -3.189619e-01 0.6064784 0.1745175687
## 1190 -1.4585000645 -4.342745e-01 0.6064784 0.1075492729
## 1269 -0.3241375263 -6.187746e-01 -1.6488632 -0.7776002413
## 1453 -0.0405468917 -4.573370e-01 -1.6488632 -0.3136104756
## 1543 -0.0405468917 -4.573370e-01 0.6064784 0.6331001452
## 1681 -0.0405468917 -9.647123e-01 0.6064784 -0.7796686589
## 1800 -0.0848237203 -4.234339e-01 0.6064784 0.0143785280
## 1873 1.1642613361 -2.395192e-01 0.6064784 2.5922940319
## 1971 1.1647133051 1.325183e+00 0.6064784 0.3282985323
## 1974 -0.3241375263 6.266011e-01 0.6064784 -0.0156882418
## 130 0.8811226705 -4.342745e-01 0.6064784 0.2528189606
## 721 1.1559941217 -3.173597e-01 -1.6488632 0.3607149870
## 837 -0.4317770784 5.974603e-01 0.6064784 -0.9961651160
## 1741 1.4483039396 -5.034620e-01 0.6064784 2.3084075797
## 1860 -0.8913187954 9.264138e-01 0.6064784 -0.5434891025
## 1957 0.7393273533 -1.344618e-01 -1.6488632 -0.0079618383
## 1960 -0.0405468917 -3.189619e-01 0.6064784 -0.0819259293
## 1965 1.0229179878 -8.032747e-01 0.6064784 -0.3376181804
## 1968 1.7318945742 -4.803995e-01 0.6064784 0.4586820776
## 58 1.9445875501 -9.877748e-01 0.6064784 -0.8804745619
## 195 -0.6786258195 8.802888e-01 0.6064784 0.1811909828
## 1228 0.7534646513 6.266011e-01 0.6064784 2.5114535208
## 1257 -0.0405468917 -2.330995e-01 0.6064784 0.6470875349
## 1592 0.8102250119 3.763103e+00 0.6064784 -0.5479673145
## 1596 0.8811226705 -2.958994e-01 0.6064784 0.0961342225
## 1750 0.5975320360 -6.418371e-01 -1.6488632 -0.2243945393
## 1837 -0.9622164540 4.570291e+00 -1.6488632 3.0564804692
## 1942 -0.7495234781 1.871977e+00 -1.6488632 -0.9695548139
## 1961 0.8102250119 -5.034620e-01 -1.6488632 0.1940837177
## 1962 -1.8129883577 -5.495871e-01 0.6064784 -0.3569554709
## 1963 1.1647133051 -5.726496e-01 0.6064784 1.3973328925
## 422 0.6684296946 -4.342745e-01 0.6064784 0.3242303453
## 1401 0.6684296946 1.641352e+00 0.6064784 -0.4668326485
## 1525 2.0154852087 -7.571497e-01 0.6064784 0.2218956567
## 1646 -0.6786258195 -5.957121e-01 0.6064784 -0.5302056912
## 1929 -0.7495234781 2.240977e+00 -1.6488632 1.0567473017
## 1940 2.0863828674 2.148727e+00 0.6064784 0.4662160108
## 1945 -1.1040117713 -5.726496e-01 0.6064784 0.3349839082
## 1946 -0.3950351849 -8.263372e-01 -1.6488632 -0.0761004120
## 1948 0.9520203292 -2.036494e-01 0.6064784 0.0402590142
## 1949 1.7318945742 -9.185873e-01 0.6064784 -0.0843406515
## 1950 0.1012484255 -1.080025e+00 0.6064784 -0.0669546516
## 1953 0.5975320360 -6.527430e-02 0.6064784 1.0913714912
## 1955 1.3774062810 -2.036494e-01 0.6064784 -0.3869858343
## 202 1.3065086224 -6.418371e-01 0.6064784 0.8223070451
## 494 1.0938156464 1.595227e+00 0.6064784 0.6728089105
## 740 -1.1152430891 1.277483e+00 0.6064784 -1.1241156364
## 780 -0.2961529648 -3.323190e-01 0.6064784 -0.8851633900
## 1069 1.3065086224 -5.495871e-01 -1.6488632 0.0483435041
## 1537 -0.5368305022 7.310078e-02 -1.6488632 0.5802690898
## 1920 0.3095457076 -3.506023e-01 0.6064784 0.0892508614
## 1926 -0.6077281608 -5.034620e-01 -1.6488632 0.4721830578
## 1938 -0.7495234781 -9.185873e-01 0.6064784 -0.1886566507
## 1939 0.8102250119 -1.575243e-01 0.6064784 0.1892205439
## 1947 -0.2532398676 -2.728369e-01 -1.6488632 2.7897123091
## 1951 2.6535641365 -2.267119e-01 0.6064784 0.4794164921
## 25 0.6499357972 -4.426689e-01 0.6064784 0.2329704747
## 134 -0.0405468917 -7.340872e-01 0.6064784 0.8759138780
## 323 0.1498265909 1.349416e+00 0.6064784 -0.0699320500
## 354 -2.0256813336 4.190385e-01 0.6064784 0.1348283165
## 381 -2.8055555786 -4.342745e-01 -1.6488632 -0.3338619458
## 1568 0.8811226705 -4.573370e-01 0.6064784 -1.4494521051
## 1766 -0.1823422090 7.419137e-01 -1.6488632 0.9047161478
## 1782 0.3139414014 -8.032747e-01 0.6064784 -0.6264296880
## 1903 0.3848390601 -3.189619e-01 -1.6488632 -0.0753820321
## 1923 -4.2944064099 -4.573370e-01 0.6064784 -1.0042348242
## 1925 0.5975320360 -7.340872e-01 0.6064784 0.4397506554
## 1930 1.5900992569 -1.113993e-01 0.6064784 0.1088371247
## 1932 0.7393273533 -4.573370e-01 0.6064784 -0.6531418816
## 1934 -1.3876024058 -9.416498e-01 0.6064784 -1.8257003204
## 385 -0.3657487723 1.133657e+00 0.6064784 0.0593292279
## 548 -0.6077281608 -1.805869e-01 0.6064784 -0.9469330830
## 1078 -0.0677493981 1.049209e+00 0.6064784 -0.4334113200
## 1429 -0.2532398676 5.343511e-01 -1.6488632 -0.1817022507
## 1581 0.5975320360 4.109041e+00 0.6064784 0.4920315863
## 1609 0.8102250119 -8.493998e-01 0.6064784 0.4852923163
## 1905 1.0229179878 2.697576e-02 0.6064784 0.7520313115
## 1908 -0.7495234781 -5.034620e-01 0.6064784 -0.9400578323
## 1916 0.7393273533 -3.189619e-01 0.6064784 -0.2729036253
## 1918 1.3774062810 3.267884e-01 0.6064784 5.5160762602
## 1919 0.5266343774 2.114759e-01 0.6064784 -0.3480283161
## 1921 1.3065086224 -7.110247e-01 0.6064784 1.1395612972
## 1927 1.0229179878 -2.497744e-01 0.6064784 -0.3338619458
## 1933 0.7393273533 -3.881495e-01 0.6064784 -0.5806319203
## 77 0.8102250119 -3.650870e-01 0.6064784 -0.4638606827
## 184 0.7317743187 -2.230051e-01 0.6064784 2.3030739551
## 498 -0.3166487413 -3.170750e-01 -1.6488632 -0.8467466946
## 555 0.4340736130 6.122724e-01 0.6064784 0.4560700295
## 574 1.0938156464 2.840602e+00 0.6064784 1.4753910236
## 783 0.7393273533 -4.112120e-01 -1.6488632 0.0532877820
## 956 -0.2741935863 1.222503e+00 0.6064784 -0.7836789397
## 1584 0.6684296946 -4.573370e-01 0.6064784 -0.9392443009
## 1797 0.2430437428 -3.881495e-01 0.6064784 -0.3205183698
## 1863 1.5192015983 -1.057669e-01 0.6064784 0.9393979304
## 1874 -0.2532398676 -6.527430e-02 0.6064784 -0.1113855402
## 1882 -0.0405468917 1.180101e+00 -1.6488632 3.8455645407
## 1898 -0.4659328436 1.180101e+00 -1.6488632 1.9156932588
## 1911 -0.3241375263 5.003827e-02 0.6064784 -0.1866282840
## 1913 -0.1823422090 -9.877748e-01 -1.6488632 -0.4175624090
## 1914 1.0925981316 -5.011621e-01 0.6064784 0.6839614188
## 1915 0.8811226705 -8.032747e-01 0.6064784 0.0281164111
## 115 2.7953594537 -9.877748e-01 0.6064784 0.1624976182
## 259 0.5266343774 1.192258e-01 0.6064784 -0.2382849413
## 321 1.6609969155 2.697576e-02 0.6064784 -0.2798014379
## 825 -0.6478668766 -1.126832e-01 -1.6488632 -0.4933500236
## 1370 0.8811226705 -1.344618e-01 0.6064784 0.0931414304
## 1621 1.9445875501 -8.263372e-01 -1.6488632 -1.2987076672
## 1643 1.4483039396 4.062916e+00 0.6064784 2.0552105417
## 1688 0.3429730079 -3.651952e-01 -1.6488632 0.5445036445
## 1865 0.1097733982 -1.113993e-01 -1.6488632 1.0688602435
## 1880 -0.5368305022 -1.805869e-01 0.6064784 -0.4235905463
## 1886 1.8736898915 -5.726496e-01 0.6064784 1.1016789053
## 1889 0.1721460842 7.649762e-01 0.6064784 -0.7165243938
## 1892 -0.5368305022 5.343511e-01 -1.6488632 -0.4652228337
## 1897 -0.6786258195 5.343511e-01 -1.6488632 2.0621059150
## 1899 1.0229179878 -8.493998e-01 -1.6488632 -0.2483405345
## 1900 -0.1823422090 -5.495871e-01 -1.6488632 0.3440149692
## 1904 -0.1114445504 -7.340872e-01 0.6064784 -0.5007130585
## 1909 0.3139414014 -5.034620e-01 -1.6488632 0.8585508754
## 83 -0.0405468917 -4.342745e-01 -1.6488632 0.0216334569
## 208 0.4764015968 -8.833681e-02 0.6064784 -0.1495381510
## 329 0.3480268468 -2.040157e-01 0.6064784 -0.2792299541
## 449 -0.1114445504 -9.877748e-01 0.6064784 -1.9231939728
## 451 1.5900992569 -5.495871e-01 0.6064784 2.1304425534
## 605 -0.1823422090 -8.955248e-01 0.6064784 0.4789657441
## 754 0.5975320360 5.585041e+00 0.6064784 -0.2458970656
## 822 2.4408711605 -2.036494e-01 0.6064784 -0.5944641321
## 844 0.0051235465 -5.331303e-01 -1.6488632 -0.1853868923
## 1074 -0.9662448540 -7.461925e-01 -1.6488632 -1.2206181980
## 1185 1.0229179878 -5.265246e-01 0.6064784 0.1997916611
## 1829 2.3699735019 -2.497744e-01 0.6064784 0.1635708280
## 1843 0.2430437428 7.310078e-02 0.6064784 -1.2239745447
## 1888 1.1647133051 -1.033900e+00 0.6064784 1.9891008136
## 1893 -2.0256813336 -5.265246e-01 0.6064784 -0.9652313113
## 1894 -2.3801696267 -6.648996e-01 0.6064784 -0.5912023406
## 30 -0.2471762752 -4.955058e-01 0.6064784 -0.0229671846
## 211 0.8811226705 -7.802122e-01 -1.6488632 3.3606630251
## 666 -0.4605913551 -1.798594e-01 -1.6488632 -0.1066541596
## 895 -0.0415173212 1.296045e+00 0.6064784 -0.5651087689
## 1165 0.5266343774 -4.342745e-01 0.6064784 0.4318303667
## 1452 -0.3805885349 -3.796735e-01 0.6064784 -1.1055503792
## 1526 0.0597156143 -2.325654e-01 -1.6488632 -0.0511455348
## 1698 -0.8204211367 -3.189619e-01 0.6064784 -1.2424414202
## 1862 0.6060276996 -2.563752e-01 0.6064784 0.0964931783
## 1883 -0.4659328436 2.345384e-01 0.6064784 0.4372393444
## 1884 0.0303507669 -2.728369e-01 0.6064784 -0.0968614333
## 1885 -1.2458070886 2.806634e-01 -1.6488632 0.2035546032
## 1887 -1.3876024058 3.959760e-01 0.6064784 0.2789945493
## 96 -0.8083731481 -6.774257e-01 0.6064784 -0.9879573746
## 311 0.5266343774 -6.187746e-01 -1.6488632 -1.0125598665
## 437 0.2700619259 -6.208447e-01 -1.6488632 0.3672903501
## 599 0.6630629149 -4.400354e-01 0.6064784 0.2298060066
## 619 1.0433787699 -3.181994e-01 0.6064784 1.1311762842
## 778 0.7263768431 -2.901905e-01 0.6064784 0.8406849157
## 1008 0.2533065497 -2.654563e-01 0.6064784 -0.6194205708
## 1082 -0.1883280026 6.346104e+00 0.6064784 -0.5534159185
## 1732 1.5900992569 -1.914927e-02 0.6064784 2.5530994297
## 1784 -1.0331141127 5.654229e+00 -1.6488632 -0.7367533148
## 1820 -1.3876024058 -1.010837e+00 0.6064784 -0.5093317593
## 1834 1.0229179878 -6.418371e-01 0.6064784 -0.1384304289
## 1869 1.0229179878 -1.310650e+00 0.6064784 -0.6966337115
## 1870 0.3848390601 2.079539e+00 -1.6488632 2.3799445298
## 1871 0.7393273533 -6.648996e-01 0.6064784 1.8673585692
## 1875 1.5192015983 -1.033900e+00 -1.6488632 -0.3710026731
## 1876 -0.0405468917 -7.110247e-01 0.6064784 1.1589423176
## 82 1.0229179878 -4.803995e-01 0.6064784 1.2080186717
## 258 -1.4207515555 2.043410e-01 -1.6488632 -0.7990056845
## 310 -0.5178432350 -4.973937e-01 0.6064784 -0.9141926991
## 345 -0.1823422090 2.576009e-01 0.6064784 0.5683030356
## 464 0.4449574040 -1.712783e-01 0.6064784 1.8991422037
## 595 0.4557367187 4.570291e+00 0.6064784 0.0081271107
## 676 -0.1490974447 8.588373e-02 0.6064784 -0.6786104715
## 841 0.4483656317 8.238358e-01 0.6064784 -0.0966937575
## 1434 0.0303507669 -5.034620e-01 0.6064784 3.8787819193
## 1605 -0.8913187954 -1.033900e+00 0.6064784 0.3174691230
## 1616 -1.7420906990 -4.112120e-01 0.6064784 -0.9102216397
## 1769 -3.1600438718 -5.957121e-01 -1.6488632 -0.5829002957
## 1833 -1.1040117713 -8.955248e-01 -1.6488632 1.7950510544
## 1846 -0.1823422090 -2.036494e-01 0.6064784 -0.7643264239
## 1858 -0.3950351849 -4.112120e-01 -1.6488632 -0.7672616529
## 1859 0.2430437428 -7.110247e-01 -1.6488632 0.2764188457
## 1866 2.5117688192 -5.495871e-01 0.6064784 0.4072645927
## 50 1.0229179878 -2.267119e-01 -1.6488632 -0.0155854613
## 52 0.5266343774 8.341637e-01 0.6064784 -0.6101354353
## 53 -0.8913187954 -8.032747e-01 0.6064784 -0.4211461044
## 250 -0.4659328436 7.649762e-01 0.6064784 0.4037551966
## 349 -2.0965789922 -3.650870e-01 -1.6488632 -1.1758487479
## 396 1.2356109637 -1.344618e-01 0.6064784 -0.0550420221
## 868 0.0728239647 -2.928032e-01 -1.6488632 -0.5023064635
## 979 0.8811226705 -7.340872e-01 0.6064784 -0.1538846510
## 1191 0.0936853235 -4.722792e-01 -1.6488632 -1.0330235651
## 1409 0.7393273533 2.114759e-01 0.6064784 1.5959035483
## 1803 0.4557367187 -5.957121e-01 0.6064784 1.0544423396
## 1832 1.3774062810 -4.573370e-01 0.6064784 1.4930766278
## 1836 0.4557367187 7.419137e-01 0.6064784 -0.3691540396
## 1839 -0.6786258195 -5.726496e-01 -1.6488632 -0.0581409156
## 1841 0.1721460842 3.693915e+00 0.6064784 2.7817736223
## 1844 1.3774062810 -5.726496e-01 0.6064784 0.4602450613
## 1845 -0.6077281608 -7.110247e-01 -1.6488632 -0.2369148739
## 1848 -0.8913187954 -3.650870e-01 0.6064784 1.6442427093
## 1851 -0.3950351849 -1.056962e+00 0.6064784 -0.7138855601
## 1852 1.2356109637 -3.650870e-01 0.6064784 1.6868133676
## 1857 0.8811226705 -3.189619e-01 0.6064784 0.3427968761
## 2208 -0.6077281608 3.959760e-01 -1.6488632 0.6556452182
## 204 -0.6077281608 -2.958994e-01 0.6064784 -0.8351582752
## 369 1.5192015983 9.264138e-01 0.6064784 -0.1115611563
## 715 -0.7689677367 5.992804e-01 0.6064784 -0.9771421579
## 926 0.1213914458 8.817244e-01 0.6064784 0.2833103449
## 1219 1.8027922328 6.957886e-01 0.6064784 1.1259181167
## 1468 -0.1823422090 -1.379838e+00 0.6064784 1.1152933391
## 1480 -0.7495234781 -5.726496e-01 -1.6488632 -1.0877060214
## 1574 0.0198175067 -5.671319e-02 0.6064784 -0.3906431345
## 1823 0.8102250119 -8.032747e-01 0.6064784 1.8593497406
## 1827 1.3065086224 1.653508e-01 0.6064784 -0.3255982299
## 1847 1.2356109637 -1.010837e+00 0.6064784 0.5771859113
## 47 -0.6027692773 9.272789e-01 -1.6488632 -0.5940849122
## 66 0.1016714577 -4.931036e-01 0.6064784 -0.9298946472
## 405 0.3711056958 -4.218638e-01 0.6064784 -0.1644641527
## 818 -1.0984877632 -5.856672e-01 0.6064784 -1.1904439799
## 1133 -1.1040117713 -3.650870e-01 0.6064784 -0.7773659238
## 1576 0.8811226705 -6.418371e-01 0.6064784 1.8304374668
## 1811 -0.7495234781 -1.033900e+00 -1.6488632 -0.6069831252
## 1813 -1.7420906990 5.003827e-02 -1.6488632 -0.4690541929
## 1825 -0.8913187954 -2.728369e-01 0.6064784 -1.2769834464
## 1826 -1.1749094299 5.003827e-02 -1.6488632 -0.6160826034
## 1828 1.0229179878 -8.833681e-02 0.6064784 0.3943538781
## 181 0.0303507669 7.649762e-01 0.6064784 -0.1736853730
## 279 1.1647133051 -8.955248e-01 0.6064784 1.3501250733
## 307 0.5975320360 2.697576e-02 0.6064784 -0.1923445901
## 863 0.5115041158 -3.134422e-01 0.6064784 -0.0676364164
## 1011 0.1767504114 1.469528e+00 0.6064784 1.1658088879
## 1330 -0.0405468917 -4.803995e-01 -1.6488632 1.1284401600
## 1755 1.3774062810 -3.650870e-01 0.6064784 1.3466058393
## 1795 0.6684296946 -7.340872e-01 0.6064784 1.2470119637
## 1806 -0.6077281608 -8.833681e-02 0.6064784 -0.3746439208
## 1814 -0.0925081402 8.943485e-01 -1.6488632 4.3267625634
## 1815 0.4952785386 1.226226e+00 0.6064784 0.7026656090
## 1816 0.8102250119 -8.955248e-01 0.6064784 -0.7025095356
## 1818 0.3139414014 -5.265246e-01 0.6064784 0.1987721117
## 1821 -0.4449925813 -2.837686e-01 0.6064784 -0.8915630208
## 1990 0.7344706729 -2.119033e-01 0.6064784 -0.1109932239
## 126 -0.0405468917 -2.958994e-01 0.6064784 -0.5407231479
## 165 -0.1823422090 -8.724623e-01 -1.6488632 -0.1646739875
## 167 0.2333861417 -9.298053e-02 0.6064784 -0.7177468901
## 263 1.5900992569 -5.265246e-01 0.6064784 1.4754983445
## 268 -0.4659328436 -3.189619e-01 0.6064784 0.7260043462
## 781 -0.0552224816 -3.422698e-01 0.6064784 -0.3001832048
## 973 -0.1823422090 -5.265246e-01 0.6064784 -1.0664515127
## 1528 1.5900992569 -7.802122e-01 0.6064784 -0.1025637551
## 1673 1.7318945742 -2.728369e-01 0.6064784 -0.8849882350
## 1768 -0.7495234781 -1.113993e-01 -1.6488632 0.4351465851
## 1788 -0.8204211367 -5.726496e-01 -1.6488632 0.6622914537
## 1798 -0.2070349957 -7.571497e-01 0.6064784 -0.7587292217
## 1801 -0.1466337638 -1.805869e-01 -1.6488632 -1.1373527099
## 1804 -1.8838860163 3.071228e+00 0.6064784 -0.8503852294
## 1807 1.5900992569 1.526039e+00 0.6064784 -0.3981165538
## 1809 1.5192015983 -3.189619e-01 0.6064784 0.9008660074
## 43 -0.1114445504 -1.402900e+00 0.6064784 -0.8542920740
## 280 2.0154852087 -5.495871e-01 0.6064784 -0.0594690128
## 440 0.1012484255 -1.287588e+00 0.6064784 -0.3646201407
## 572 0.9520203292 -3.189619e-01 0.6064784 0.8700291106
## 1063 0.4624865948 -4.053534e-01 0.6064784 -0.1467053756
## 1111 -1.7420906990 3.498509e-01 0.6064784 -0.4753110065
## 1496 0.4557367187 -5.034620e-01 0.6064784 0.1290939610
## 1718 0.1012484255 2.114759e-01 0.6064784 0.1386558482
## 1719 -0.6077281608 6.727261e-01 0.6064784 1.4894545445
## 1774 -0.6786258195 1.064789e+00 -1.6488632 -0.8137396893
## 1779 -0.1829481025 8.639959e-01 -1.6488632 0.2806641522
## 1785 -0.0405468917 -5.495871e-01 -1.6488632 0.7331031823
## 1787 0.9134571025 -2.773449e-01 0.6064784 0.9443580852
## 1789 1.0229179878 -5.265246e-01 -1.6488632 0.2513021575
## 32 -0.2532398676 -5.034620e-01 -1.6488632 0.0520714775
## 226 -0.8176089279 -9.901383e-01 -1.6488632 -1.2216234254
## 361 0.1116373544 -2.975396e-01 -1.6488632 -0.5643632128
## 620 0.6730005702 -6.879622e-01 0.6064784 1.3326505337
## 710 0.5649832435 -3.814109e-01 0.6064784 -0.3911995627
## 790 -0.0405468917 -8.833681e-02 0.6064784 -0.3148661312
## 1344 -1.4585000645 6.323042e+00 -1.6488632 -1.4378169436
## 1383 -1.1040117713 -9.647123e-01 -1.6488632 -0.7334179797
## 1388 -1.1749094299 9.956013e-01 0.6064784 -1.5644503753
## 1417 -0.8913187954 -8.833681e-02 0.6064784 -0.1723975212
## 1443 -1.8838860163 -5.495871e-01 -1.6488632 -1.5518112385
## 1667 1.6609969155 -4.342745e-01 -1.6488632 6.8316008182
## 1720 -0.8204211367 -5.265246e-01 0.6064784 -0.7236671015
## 1748 0.4557367187 -3.650870e-01 -1.6488632 -0.5736502999
## 1765 -0.1114445504 -6.648996e-01 0.6064784 0.7160592682
## 1778 0.7393273533 -5.034620e-01 0.6064784 -0.4157095299
## 1781 0.1721460842 1.653508e-01 0.6064784 -0.9545421410
## 1783 1.4483039396 1.295414e+00 0.6064784 0.0663380140
## 2206 -1.9547836749 -8.833681e-02 0.6064784 0.5715438935
## 80 -0.8913187954 -2.958994e-01 0.6064784 -0.6015051554
## 297 -1.1047279312 1.186239e+00 0.6064784 -0.9134124766
## 424 -0.5368305022 -5.957121e-01 -1.6488632 0.9414333404
## 438 0.9499897203 -4.789356e-01 0.6064784 0.8078737816
## 875 -0.8913187954 8.111012e-01 0.6064784 -0.6957281907
## 1073 -0.3241375263 1.422883e-01 0.6064784 1.6803383346
## 1227 1.7318945742 -2.365340e-01 0.6064784 0.6755198703
## 1419 -0.1114445504 5.003827e-02 0.6064784 -0.7305663078
## 1772 0.3712410529 -2.627826e-01 -1.6488632 0.7738020290
## 1775 1.3774062810 -5.726496e-01 0.6064784 0.1243450074
## 69 -0.7511889754 -4.112120e-01 0.6064784 -1.0982020139
## 217 1.0229179878 -4.573370e-01 0.6064784 0.8279735933
## 388 -0.4071981873 4.651635e-01 -1.6488632 0.6416568475
## 579 0.2972325256 -3.710941e-01 0.6064784 -0.4713012913
## 942 0.2686586195 -5.748728e-01 -1.6488632 0.1287990047
## 1093 0.5975320360 6.496636e-01 0.6064784 1.7499283289
## 1380 1.5900992569 -1.914927e-02 0.6064784 0.6765651449
## 1729 1.0938156464 -2.958994e-01 0.6064784 -0.1422939844
## 1752 0.5266343774 3.117353e+00 0.6064784 -0.3200175386
## 71 1.0938156464 -2.497744e-01 0.6064784 0.9094516864
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## 686 -0.1366480569 -4.129930e-01 -1.6488632 0.1712308726
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## 221 0.1295012992 -3.758429e-01 0.6064784 -0.0602122180
## 497 0.2548854122 -3.135322e-01 0.6064784 0.0554922891
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## 516 -0.5292257102 -3.049963e-01 0.6064784 -1.1544634052
## 637 -0.9560106678 2.530573e-01 -1.6488632 -1.1026113823
## 656 0.0182473476 -5.231380e-01 0.6064784 -0.4983613693
## 661 0.6914093032 -2.526266e-01 0.6064784 0.2515696564
## 664 0.4987775791 -7.408751e-01 0.6064784 -0.4767603668
## 671 -1.0595269567 4.378936e-01 -1.6488632 0.2274292990
## 674 -0.0226142308 -4.255965e-01 0.6064784 -0.4191182077
## 678 0.5050393348 -5.328019e-01 -1.6488632 0.3019222877
## 683 0.4447422792 -2.888979e-01 0.6064784 0.7337066763
## 206 0.4849309353 -2.424597e-01 0.6064784 -0.1748428964
## 256 -0.0752867395 -5.598441e-01 0.6064784 -0.7851643594
## 453 -0.0162991878 8.175788e-01 0.6064784 0.7280894674
## 456 0.5001506853 -4.132635e-01 0.6064784 0.0149838290
## 509 -0.2261727410 -4.986365e-01 0.6064784 -0.8661794783
## 544 0.4134156695 -2.841487e-01 0.6064784 0.5012999991
## 627 -0.3681799048 4.733204e-01 0.6064784 -0.5389642023
## 634 -0.1352446477 -1.848809e-01 0.6064784 -0.0328889405
## 641 0.8822457930 -2.850536e-01 0.6064784 2.6552763107
## 642 0.3802484764 -2.339460e-01 0.6064784 0.5374143185
## 643 0.6249891309 -4.395182e-01 0.6064784 -0.0189047098
## 653 0.4092344701 4.259007e-01 0.6064784 -0.3585395195
## 654 0.0610888530 -6.193205e-01 0.6064784 -0.0998230639
## 660 0.0434207574 -3.754514e-01 0.6064784 -0.4230690797
## 4 -0.2800035934 6.236986e-01 0.6064784 -0.5073736244
## 121 -0.3863233118 1.417291e+00 0.6064784 -0.8816783495
## 338 -0.0676471524 -2.200221e-01 0.6064784 0.0226286950
## 496 0.5296109376 -3.866031e-01 0.6064784 0.8488016947
## 580 -0.6272614272 1.989815e+00 -1.6488632 -0.0021515220
## 608 0.1388223434 7.541137e-01 -1.6488632 4.0087934416
## 615 0.4353505585 -3.022925e-01 0.6064784 0.1305470608
## 616 -0.3668968039 -4.762521e-01 -1.6488632 -0.6457420816
## 623 -0.3200971942 -3.652017e-01 0.6064784 -1.0072229791
## 782 0.6821294130 -3.005859e-01 0.6064784 0.9686349538
## attr(,"center")
## [1] 54.27563025 0.64369748 0.76526611 0.13221289 0.22072829
## [6] 0.40168067 0.46666667 0.05714286 0.84257703 0.12549020
## [11] 0.09579832 0.22184874 0.27282913 110.79271709 76.37983193
## [16] 20.13484594 16.22173669 501.35742297 0.40560224 25.03881507
## [21] 139.47056037 13.55719073 109.83032013 0.73109244 91.13830031
## attr(,"scale")
## [1] 11.0845083 0.4789061 0.4238324 0.3387221 0.4147376 0.4902380
## [7] 0.4988877 0.2321154 0.3641991 0.3312739 0.2943145 0.4154899
## [13] 0.4454137 17.9176033 14.4451920 7.3004751 5.0751008 220.4424148
## [19] 0.4910082 12.5276772 3.0834395 1.4104838 43.3604085 0.4433918
## [25] 43.1381575
## attr(,"nonsingular")
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
##
## attr(,"class")
## [1] "ncvsurv" "ncvreg"(MCP_Train_CoxPH_Model <- MCP_Train_CoxPH_Fit$model$beta)## 0.0137
## age 0.002448205
## betablok -0.379260896
## acei 0.038713889
## angioten.II 0.000000000
## anti.arrhy 0.000000000
## anti.coag 0.000000000
## aspirin -0.210588087
## vasodilator 0.000000000
## diuretic.loop 0.000000000
## diuretic.thiazide 0.000000000
## insulin 0.077586207
## surgery.pacemaker 0.000000000
## surgery.cabg 0.000000000
## resting.systolic.bp -0.001705225
## resting.hr 0.002940415
## lvef.metabl -0.011928129
## peak.vo2 -0.063746539
## interval -0.001120881
## cad 0.102984692
## bun 0.017095396
## sodium 0.000000000
## hgb 0.000000000
## glucose 0.000000000
## male 0.521276288
## crcl 0.000000000(MCP_Train_CoxPH_Lambda <- MCP_Train_CoxPH_Fit$lambda)## [1] 0.01374311(MCP_Train_CoxPH_Gamma <- MCP_Train_CoxPH_Fit$gamma)## [1] 4##################################
# Conducting an internal validation
# of the MCP model
# using a 50-cycle bootstrap
##################################
MCP_Train_CoxPH_Model_InternalValidation <- validate(MCP_Train_Predictors,
MCP_Train_Time,
MCP_Train_Status,
model.type = "mcp",
alpha = 1,
lambda = MCP_Train_CoxPH_Fit$lambda,
gamma = MCP_Train_CoxPH_Fit$gamma,
method = "bootstrap",
boot.times = 50,
tauc.type = "UNO",
tauc.time = seq(12, 120, 12),
seed = 88888888)## Start bootstrap sample 1
## Start bootstrap sample 2
## Start bootstrap sample 3
## Start bootstrap sample 4
## Start bootstrap sample 5
## Start bootstrap sample 6
## Start bootstrap sample 7
## Start bootstrap sample 8
## Start bootstrap sample 9
## Start bootstrap sample 10
## Start bootstrap sample 11
## Start bootstrap sample 12
## Start bootstrap sample 13
## Start bootstrap sample 14
## Start bootstrap sample 15
## Start bootstrap sample 16
## Start bootstrap sample 17
## Start bootstrap sample 18
## Start bootstrap sample 19
## Start bootstrap sample 20
## Start bootstrap sample 21
## Start bootstrap sample 22
## Start bootstrap sample 23
## Start bootstrap sample 24
## Start bootstrap sample 25
## Start bootstrap sample 26
## Start bootstrap sample 27
## Start bootstrap sample 28
## Start bootstrap sample 29
## Start bootstrap sample 30
## Start bootstrap sample 31
## Start bootstrap sample 32
## Start bootstrap sample 33
## Start bootstrap sample 34
## Start bootstrap sample 35
## Start bootstrap sample 36
## Start bootstrap sample 37
## Start bootstrap sample 38
## Start bootstrap sample 39
## Start bootstrap sample 40
## Start bootstrap sample 41
## Start bootstrap sample 42
## Start bootstrap sample 43
## Start bootstrap sample 44
## Start bootstrap sample 45
## Start bootstrap sample 46
## Start bootstrap sample 47
## Start bootstrap sample 48
## Start bootstrap sample 49
## Start bootstrap sample 50print(MCP_Train_CoxPH_Model_InternalValidation)## High-Dimensional Cox Model Validation Object
## Random seed: 88888888
## Validation method: bootstrap
## Bootstrap samples: 50
## Model type: mcp
## ncvreg model gamma: 4
## ncvreg model alpha: 1
## ncvreg model lambda: 0.01374311
## Time-dependent AUC type: UNO
## Evaluation time points for tAUC: 12 24 36 48 60 72 84 96 108 120summary(MCP_Train_CoxPH_Model_InternalValidation)## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7657023 0.7778923 0.6847150 0.6786775 0.6608072 0.6753051 0.6873961
## Min 0.7554392 0.7654295 0.6673734 0.6497727 0.6337684 0.6463523 0.6483589
## 0.25 Qt. 0.7623745 0.7745780 0.6767897 0.6742883 0.6550052 0.6683064 0.6752485
## Median 0.7661746 0.7781832 0.6853087 0.6799203 0.6614552 0.6763311 0.6875798
## 0.75 Qt. 0.7681476 0.7817208 0.6911974 0.6834742 0.6674232 0.6823387 0.7025691
## Max 0.7744849 0.7866981 0.7076284 0.6954846 0.6785003 0.6981830 0.7289946
## 96 108 120
## Mean 0.6137137 0.6277414 0.26004737
## Min 0.5637704 0.5240417 0.08551219
## 0.25 Qt. 0.5963127 0.5893859 0.21089858
## Median 0.6151269 0.6257171 0.26660433
## 0.75 Qt. 0.6305660 0.6675990 0.31391346
## Max 0.6625237 0.7337565 0.45570755MCP_Train_IV_Summary <- data.frame(matrix(ncol=4, nrow=10))
names(MCP_Train_IV_Summary) <- c("Model","Validation","MedianTDAUROC","Time")
for (i in 1:10){
MCP_Train_IV_Summary[i,1] <- "MCP"
MCP_Train_IV_Summary[i,2] <- "Cross-Validation"
MCP_Train_IV_Summary[i,3] <- summary(MCP_Train_CoxPH_Model_InternalValidation)[[4,i]]
MCP_Train_IV_Summary[i,4] <- i*12
}## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7657023 0.7778923 0.6847150 0.6786775 0.6608072 0.6753051 0.6873961
## Min 0.7554392 0.7654295 0.6673734 0.6497727 0.6337684 0.6463523 0.6483589
## 0.25 Qt. 0.7623745 0.7745780 0.6767897 0.6742883 0.6550052 0.6683064 0.6752485
## Median 0.7661746 0.7781832 0.6853087 0.6799203 0.6614552 0.6763311 0.6875798
## 0.75 Qt. 0.7681476 0.7817208 0.6911974 0.6834742 0.6674232 0.6823387 0.7025691
## Max 0.7744849 0.7866981 0.7076284 0.6954846 0.6785003 0.6981830 0.7289946
## 96 108 120
## Mean 0.6137137 0.6277414 0.26004737
## Min 0.5637704 0.5240417 0.08551219
## 0.25 Qt. 0.5963127 0.5893859 0.21089858
## Median 0.6151269 0.6257171 0.26660433
## 0.75 Qt. 0.6305660 0.6675990 0.31391346
## Max 0.6625237 0.7337565 0.45570755
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7657023 0.7778923 0.6847150 0.6786775 0.6608072 0.6753051 0.6873961
## Min 0.7554392 0.7654295 0.6673734 0.6497727 0.6337684 0.6463523 0.6483589
## 0.25 Qt. 0.7623745 0.7745780 0.6767897 0.6742883 0.6550052 0.6683064 0.6752485
## Median 0.7661746 0.7781832 0.6853087 0.6799203 0.6614552 0.6763311 0.6875798
## 0.75 Qt. 0.7681476 0.7817208 0.6911974 0.6834742 0.6674232 0.6823387 0.7025691
## Max 0.7744849 0.7866981 0.7076284 0.6954846 0.6785003 0.6981830 0.7289946
## 96 108 120
## Mean 0.6137137 0.6277414 0.26004737
## Min 0.5637704 0.5240417 0.08551219
## 0.25 Qt. 0.5963127 0.5893859 0.21089858
## Median 0.6151269 0.6257171 0.26660433
## 0.75 Qt. 0.6305660 0.6675990 0.31391346
## Max 0.6625237 0.7337565 0.45570755
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7657023 0.7778923 0.6847150 0.6786775 0.6608072 0.6753051 0.6873961
## Min 0.7554392 0.7654295 0.6673734 0.6497727 0.6337684 0.6463523 0.6483589
## 0.25 Qt. 0.7623745 0.7745780 0.6767897 0.6742883 0.6550052 0.6683064 0.6752485
## Median 0.7661746 0.7781832 0.6853087 0.6799203 0.6614552 0.6763311 0.6875798
## 0.75 Qt. 0.7681476 0.7817208 0.6911974 0.6834742 0.6674232 0.6823387 0.7025691
## Max 0.7744849 0.7866981 0.7076284 0.6954846 0.6785003 0.6981830 0.7289946
## 96 108 120
## Mean 0.6137137 0.6277414 0.26004737
## Min 0.5637704 0.5240417 0.08551219
## 0.25 Qt. 0.5963127 0.5893859 0.21089858
## Median 0.6151269 0.6257171 0.26660433
## 0.75 Qt. 0.6305660 0.6675990 0.31391346
## Max 0.6625237 0.7337565 0.45570755
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7657023 0.7778923 0.6847150 0.6786775 0.6608072 0.6753051 0.6873961
## Min 0.7554392 0.7654295 0.6673734 0.6497727 0.6337684 0.6463523 0.6483589
## 0.25 Qt. 0.7623745 0.7745780 0.6767897 0.6742883 0.6550052 0.6683064 0.6752485
## Median 0.7661746 0.7781832 0.6853087 0.6799203 0.6614552 0.6763311 0.6875798
## 0.75 Qt. 0.7681476 0.7817208 0.6911974 0.6834742 0.6674232 0.6823387 0.7025691
## Max 0.7744849 0.7866981 0.7076284 0.6954846 0.6785003 0.6981830 0.7289946
## 96 108 120
## Mean 0.6137137 0.6277414 0.26004737
## Min 0.5637704 0.5240417 0.08551219
## 0.25 Qt. 0.5963127 0.5893859 0.21089858
## Median 0.6151269 0.6257171 0.26660433
## 0.75 Qt. 0.6305660 0.6675990 0.31391346
## Max 0.6625237 0.7337565 0.45570755
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7657023 0.7778923 0.6847150 0.6786775 0.6608072 0.6753051 0.6873961
## Min 0.7554392 0.7654295 0.6673734 0.6497727 0.6337684 0.6463523 0.6483589
## 0.25 Qt. 0.7623745 0.7745780 0.6767897 0.6742883 0.6550052 0.6683064 0.6752485
## Median 0.7661746 0.7781832 0.6853087 0.6799203 0.6614552 0.6763311 0.6875798
## 0.75 Qt. 0.7681476 0.7817208 0.6911974 0.6834742 0.6674232 0.6823387 0.7025691
## Max 0.7744849 0.7866981 0.7076284 0.6954846 0.6785003 0.6981830 0.7289946
## 96 108 120
## Mean 0.6137137 0.6277414 0.26004737
## Min 0.5637704 0.5240417 0.08551219
## 0.25 Qt. 0.5963127 0.5893859 0.21089858
## Median 0.6151269 0.6257171 0.26660433
## 0.75 Qt. 0.6305660 0.6675990 0.31391346
## Max 0.6625237 0.7337565 0.45570755
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7657023 0.7778923 0.6847150 0.6786775 0.6608072 0.6753051 0.6873961
## Min 0.7554392 0.7654295 0.6673734 0.6497727 0.6337684 0.6463523 0.6483589
## 0.25 Qt. 0.7623745 0.7745780 0.6767897 0.6742883 0.6550052 0.6683064 0.6752485
## Median 0.7661746 0.7781832 0.6853087 0.6799203 0.6614552 0.6763311 0.6875798
## 0.75 Qt. 0.7681476 0.7817208 0.6911974 0.6834742 0.6674232 0.6823387 0.7025691
## Max 0.7744849 0.7866981 0.7076284 0.6954846 0.6785003 0.6981830 0.7289946
## 96 108 120
## Mean 0.6137137 0.6277414 0.26004737
## Min 0.5637704 0.5240417 0.08551219
## 0.25 Qt. 0.5963127 0.5893859 0.21089858
## Median 0.6151269 0.6257171 0.26660433
## 0.75 Qt. 0.6305660 0.6675990 0.31391346
## Max 0.6625237 0.7337565 0.45570755
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7657023 0.7778923 0.6847150 0.6786775 0.6608072 0.6753051 0.6873961
## Min 0.7554392 0.7654295 0.6673734 0.6497727 0.6337684 0.6463523 0.6483589
## 0.25 Qt. 0.7623745 0.7745780 0.6767897 0.6742883 0.6550052 0.6683064 0.6752485
## Median 0.7661746 0.7781832 0.6853087 0.6799203 0.6614552 0.6763311 0.6875798
## 0.75 Qt. 0.7681476 0.7817208 0.6911974 0.6834742 0.6674232 0.6823387 0.7025691
## Max 0.7744849 0.7866981 0.7076284 0.6954846 0.6785003 0.6981830 0.7289946
## 96 108 120
## Mean 0.6137137 0.6277414 0.26004737
## Min 0.5637704 0.5240417 0.08551219
## 0.25 Qt. 0.5963127 0.5893859 0.21089858
## Median 0.6151269 0.6257171 0.26660433
## 0.75 Qt. 0.6305660 0.6675990 0.31391346
## Max 0.6625237 0.7337565 0.45570755
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7657023 0.7778923 0.6847150 0.6786775 0.6608072 0.6753051 0.6873961
## Min 0.7554392 0.7654295 0.6673734 0.6497727 0.6337684 0.6463523 0.6483589
## 0.25 Qt. 0.7623745 0.7745780 0.6767897 0.6742883 0.6550052 0.6683064 0.6752485
## Median 0.7661746 0.7781832 0.6853087 0.6799203 0.6614552 0.6763311 0.6875798
## 0.75 Qt. 0.7681476 0.7817208 0.6911974 0.6834742 0.6674232 0.6823387 0.7025691
## Max 0.7744849 0.7866981 0.7076284 0.6954846 0.6785003 0.6981830 0.7289946
## 96 108 120
## Mean 0.6137137 0.6277414 0.26004737
## Min 0.5637704 0.5240417 0.08551219
## 0.25 Qt. 0.5963127 0.5893859 0.21089858
## Median 0.6151269 0.6257171 0.26660433
## 0.75 Qt. 0.6305660 0.6675990 0.31391346
## Max 0.6625237 0.7337565 0.45570755
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7657023 0.7778923 0.6847150 0.6786775 0.6608072 0.6753051 0.6873961
## Min 0.7554392 0.7654295 0.6673734 0.6497727 0.6337684 0.6463523 0.6483589
## 0.25 Qt. 0.7623745 0.7745780 0.6767897 0.6742883 0.6550052 0.6683064 0.6752485
## Median 0.7661746 0.7781832 0.6853087 0.6799203 0.6614552 0.6763311 0.6875798
## 0.75 Qt. 0.7681476 0.7817208 0.6911974 0.6834742 0.6674232 0.6823387 0.7025691
## Max 0.7744849 0.7866981 0.7076284 0.6954846 0.6785003 0.6981830 0.7289946
## 96 108 120
## Mean 0.6137137 0.6277414 0.26004737
## Min 0.5637704 0.5240417 0.08551219
## 0.25 Qt. 0.5963127 0.5893859 0.21089858
## Median 0.6151269 0.6257171 0.26660433
## 0.75 Qt. 0.6305660 0.6675990 0.31391346
## Max 0.6625237 0.7337565 0.45570755
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7657023 0.7778923 0.6847150 0.6786775 0.6608072 0.6753051 0.6873961
## Min 0.7554392 0.7654295 0.6673734 0.6497727 0.6337684 0.6463523 0.6483589
## 0.25 Qt. 0.7623745 0.7745780 0.6767897 0.6742883 0.6550052 0.6683064 0.6752485
## Median 0.7661746 0.7781832 0.6853087 0.6799203 0.6614552 0.6763311 0.6875798
## 0.75 Qt. 0.7681476 0.7817208 0.6911974 0.6834742 0.6674232 0.6823387 0.7025691
## Max 0.7744849 0.7866981 0.7076284 0.6954846 0.6785003 0.6981830 0.7289946
## 96 108 120
## Mean 0.6137137 0.6277414 0.26004737
## Min 0.5637704 0.5240417 0.08551219
## 0.25 Qt. 0.5963127 0.5893859 0.21089858
## Median 0.6151269 0.6257171 0.26660433
## 0.75 Qt. 0.6305660 0.6675990 0.31391346
## Max 0.6625237 0.7337565 0.45570755MCP_Train_IV_Summary## Model Validation MedianTDAUROC Time
## 1 MCP Cross-Validation 0.7661746 12
## 2 MCP Cross-Validation 0.7781832 24
## 3 MCP Cross-Validation 0.6853087 36
## 4 MCP Cross-Validation 0.6799203 48
## 5 MCP Cross-Validation 0.6614552 60
## 6 MCP Cross-Validation 0.6763311 72
## 7 MCP Cross-Validation 0.6875798 84
## 8 MCP Cross-Validation 0.6151269 96
## 9 MCP Cross-Validation 0.6257171 108
## 10 MCP Cross-Validation 0.2666043 120plot(MCP_Train_CoxPH_Model_InternalValidation,
ylim = c(0, 1))## 12 24 36 48 60 72 84
## Mean 0.7657023 0.7778923 0.6847150 0.6786775 0.6608072 0.6753051 0.6873961
## Min 0.7554392 0.7654295 0.6673734 0.6497727 0.6337684 0.6463523 0.6483589
## 0.25 Qt. 0.7623745 0.7745780 0.6767897 0.6742883 0.6550052 0.6683064 0.6752485
## Median 0.7661746 0.7781832 0.6853087 0.6799203 0.6614552 0.6763311 0.6875798
## 0.75 Qt. 0.7681476 0.7817208 0.6911974 0.6834742 0.6674232 0.6823387 0.7025691
## Max 0.7744849 0.7866981 0.7076284 0.6954846 0.6785003 0.6981830 0.7289946
## 96 108 120
## Mean 0.6137137 0.6277414 0.26004737
## Min 0.5637704 0.5240417 0.08551219
## 0.25 Qt. 0.5963127 0.5893859 0.21089858
## Median 0.6151269 0.6257171 0.26660433
## 0.75 Qt. 0.6305660 0.6675990 0.31391346
## Max 0.6625237 0.7337565 0.45570755
##################################
# Conducting an external validation
# of the MCP model
# using the test data
##################################
MCP_Test_Predictors <- as.matrix(
PMA_PreModelling_MCP_Test[,!names(PMA_PreModelling_MCP_Test)
%in% c("ttodead","died")])
MCP_Test_Time <- PMA_PreModelling_MCP_Test$ttodead
MCP_Test_Status <- PMA_PreModelling_MCP_Test$died
MCP_Test_Response <- survival::Surv(MCP_Test_Time, MCP_Test_Status)
MCP_Train_CoxPH_Model_ExternalValidation <- validate_external(MCP_Train_CoxPH_Fit,
MCP_Train_Predictors,
MCP_Train_Time,
MCP_Train_Status,
MCP_Test_Predictors,
MCP_Test_Time,
MCP_Test_Status,
tauc.type = "UNO",
tauc.time = seq(12, 120, 12))
print(MCP_Train_CoxPH_Model_ExternalValidation)## High-Dimensional Cox Model External Validation Object
## Model type: mcp
## Time-dependent AUC type: UNO
## Evaluation time points for tAUC: 12 24 36 48 60 72 84 96 108 120summary(MCP_Train_CoxPH_Model_ExternalValidation)## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8036043 0.746329 0.6444782 0.6019245 0.6482782 0.6010785 0.5660976
## 96 108 120
## AUC 0.6525144 0.4698016 0.275MCP_Train_EV_Summary <- data.frame(matrix(ncol=4, nrow=10))
names(MCP_Train_EV_Summary) <- c("Model","Validation","MedianTDAUROC","Time")
for (i in 1:10){
MCP_Train_EV_Summary[i,1] <- "MCP"
MCP_Train_EV_Summary[i,2] <- "Test"
MCP_Train_EV_Summary[i,3] <- summary(MCP_Train_CoxPH_Model_ExternalValidation)[[1,i]]
MCP_Train_EV_Summary[i,4] <- i*12
}## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8036043 0.746329 0.6444782 0.6019245 0.6482782 0.6010785 0.5660976
## 96 108 120
## AUC 0.6525144 0.4698016 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8036043 0.746329 0.6444782 0.6019245 0.6482782 0.6010785 0.5660976
## 96 108 120
## AUC 0.6525144 0.4698016 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8036043 0.746329 0.6444782 0.6019245 0.6482782 0.6010785 0.5660976
## 96 108 120
## AUC 0.6525144 0.4698016 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8036043 0.746329 0.6444782 0.6019245 0.6482782 0.6010785 0.5660976
## 96 108 120
## AUC 0.6525144 0.4698016 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8036043 0.746329 0.6444782 0.6019245 0.6482782 0.6010785 0.5660976
## 96 108 120
## AUC 0.6525144 0.4698016 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8036043 0.746329 0.6444782 0.6019245 0.6482782 0.6010785 0.5660976
## 96 108 120
## AUC 0.6525144 0.4698016 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8036043 0.746329 0.6444782 0.6019245 0.6482782 0.6010785 0.5660976
## 96 108 120
## AUC 0.6525144 0.4698016 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8036043 0.746329 0.6444782 0.6019245 0.6482782 0.6010785 0.5660976
## 96 108 120
## AUC 0.6525144 0.4698016 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8036043 0.746329 0.6444782 0.6019245 0.6482782 0.6010785 0.5660976
## 96 108 120
## AUC 0.6525144 0.4698016 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8036043 0.746329 0.6444782 0.6019245 0.6482782 0.6010785 0.5660976
## 96 108 120
## AUC 0.6525144 0.4698016 0.275MCP_Train_EV_Summary## Model Validation MedianTDAUROC Time
## 1 MCP Test 0.8036043 12
## 2 MCP Test 0.7463290 24
## 3 MCP Test 0.6444782 36
## 4 MCP Test 0.6019245 48
## 5 MCP Test 0.6482782 60
## 6 MCP Test 0.6010785 72
## 7 MCP Test 0.5660976 84
## 8 MCP Test 0.6525144 96
## 9 MCP Test 0.4698016 108
## 10 MCP Test 0.2750000 120plot(MCP_Train_CoxPH_Model_ExternalValidation,
ylim = c(0, 1))## 12 24 36 48 60 72 84
## AUC 0.8036043 0.746329 0.6444782 0.6019245 0.6482782 0.6010785 0.5660976
## 96 108 120
## AUC 0.6525144 0.4698016 0.275
##################################
# Conducting an internal calibration
# of the MCP model
# using a 50-cycle bootstrap
# for Year 5 (Time = 60)
##################################
MCP_Train_CoxPH_Model_InternalCalibration <- calibrate(MCP_Train_Predictors,
MCP_Train_Time,
MCP_Train_Status,
model.type = "mcp",
alpha = 1,
lambda = MCP_Train_CoxPH_Fit$lambda,
gamma = MCP_Train_CoxPH_Fit$gamma,
method = "bootstrap",
boot.times = 50,
pred.at = 60,
ngroup = 3,
seed = 88888888)## Start bootstrap sample 1
## Start bootstrap sample 2
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## Start bootstrap sample 49
## Start bootstrap sample 50print(MCP_Train_CoxPH_Model_InternalCalibration)## High-Dimensional Cox Model Calibration Object
## Random seed: 88888888
## Calibration method: bootstrap
## Bootstrap samples: 50
## Model type: mcp
## ncvreg model gamma: 4
## ncvreg model alpha: 1
## ncvreg model lambda: 0.01374311
## Calibration time point: 60
## Number of groups formed for calibration: 3summary(MCP_Train_CoxPH_Model_InternalCalibration)## Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.5660995 0.5178624 0.4769313 0.5623063
## 2 0.7574651 0.7662772 0.7303450 0.8039771
## 3 0.8816024 0.8940749 0.8677195 0.9212308plot(MCP_Train_CoxPH_Model_InternalCalibration)
##################################
# Estimating survival curves
# of the MCP model
# for the risk groups
# using the train data
##################################
MCP_Train_CoxPH_Model_KMPlot <- kmplot( MCP_Train_CoxPH_Model_InternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
##################################
# Conducting an internal calibration
# of the MCP model
# using a 50-cycle bootstrap
# for Year 10 (Time = 120)
##################################
MCP_Train_CoxPH_Model_InternalCalibration <- calibrate(MCP_Train_Predictors,
MCP_Train_Time,
MCP_Train_Status,
model.type = "mcp",
alpha = 1,
lambda = MCP_Train_CoxPH_Fit$lambda,
gamma = MCP_Train_CoxPH_Fit$gamma,
method = "bootstrap",
boot.times = 50,
pred.at = 120,
ngroup = 3,
seed = 88888888)## Start bootstrap sample 1
## Start bootstrap sample 2
## Start bootstrap sample 3
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## Start bootstrap sample 48
## Start bootstrap sample 49
## Start bootstrap sample 50print(MCP_Train_CoxPH_Model_InternalCalibration)## High-Dimensional Cox Model Calibration Object
## Random seed: 88888888
## Calibration method: bootstrap
## Bootstrap samples: 50
## Model type: mcp
## ncvreg model gamma: 4
## ncvreg model alpha: 1
## ncvreg model lambda: 0.01374311
## Calibration time point: 120
## Number of groups formed for calibration: 3summary(MCP_Train_CoxPH_Model_InternalCalibration)## Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.3524395 0.3578888 0.3134475 0.4086311
## 2 0.5988716 0.5464913 0.4903677 0.6090383
## 3 0.7921454 0.8016492 0.7530573 0.8533765plot(MCP_Train_CoxPH_Model_InternalCalibration)
##################################
# Estimating survival curves
# of the MCP model
# for the risk groups
# using the train data
##################################
MCP_Train_CoxPH_Model_KMPlot <- kmplot( MCP_Train_CoxPH_Model_InternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
##################################
# Conducting an external calibration
# of the MCP model
# using the test data
# for Year 5 (Time = 60)
##################################
MCP_Train_CoxPH_Model_ExternalCalibration <- calibrate_external(MCP_Train_CoxPH_Fit,
MCP_Train_Predictors,
MCP_Train_Time,
MCP_Train_Status,
MCP_Test_Predictors,
MCP_Test_Time,
MCP_Test_Status,
pred.at = 60,
ngroup = 3)
print(MCP_Train_CoxPH_Model_ExternalCalibration)## High-Dimensional Cox Model External Calibration Object
## Model type: mcp
## Calibration time point: 60
## Number of groups formed for calibration: 3summary(MCP_Train_CoxPH_Model_ExternalCalibration)## External Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.5750264 0.5616862 0.4828158 0.6534404
## 2 0.7610645 0.7062219 0.6330216 0.7878868
## 3 0.8862846 0.8551396 0.7965605 0.9180266plot(MCP_Train_CoxPH_Model_ExternalCalibration)
##################################
# Estimating survival curves
# of the MCP model
# for the risk groups
# using the test data
##################################
MCP_Test_CoxPH_Model_KMPlot <- kmplot( MCP_Train_CoxPH_Model_ExternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
##################################
# Conducting an external calibration
# of the MCP model
# using the test data
# for Year 10 (Time = 120)
##################################
MCP_Train_CoxPH_Model_ExternalCalibration <- calibrate_external(MCP_Train_CoxPH_Fit,
MCP_Train_Predictors,
MCP_Train_Time,
MCP_Train_Status,
MCP_Test_Predictors,
MCP_Test_Time,
MCP_Test_Status,
pred.at = 120,
ngroup = 3)
print(MCP_Train_CoxPH_Model_ExternalCalibration)## High-Dimensional Cox Model External Calibration Object
## Model type: mcp
## Calibration time point: 120
## Number of groups formed for calibration: 3summary(MCP_Train_CoxPH_Model_ExternalCalibration)## External Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.3556267 0.4671022 0.3847193 0.5671264
## 2 0.6004040 0.5864181 0.4984792 0.6898708
## 3 0.7980752 0.7409942 0.6307451 0.8705139plot(MCP_Train_CoxPH_Model_ExternalCalibration)
##################################
# Estimating survival curves
# of the MCP model
# for the risk groups
# using the test data
##################################
MCP_Test_CoxPH_Model_KMPlot <- kmplot( MCP_Train_CoxPH_Model_ExternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
1.5.4 Smoothly Clipped Absolute Deviation (SCAD)
Smoothly Clipped Absolute Deviation is a regularization technique aimed at encouraging sparsity in the model by imposing folded concave penalty functions on the regression coefficients. The algorithm uses the tuning parameter lambda which controls the amount of penalty applied, and a shape parameter gamma which determines the concavity of the penalty function. The method formulates an optimization problem that minimizes the negative log-partial likelihood of the Cox proportional hazards model subject to the penalty terms. The optimization problem is solved using coordinate descent, by iteratively updating regression coefficients, while incorporating the SCAD penalty. The SCAD penalty retains the penalization rate (and bias) of the LASSO for small coefficients, but continuously relaxes the rate of penalization by remaining flat for a while before decreasing as the absolute value of the coefficient increases. This approach can be useful for feature selection, allowing for the identification of a parsimonious set of predictors that significantly impact survival outcomes, while balancing the trade-off between model complexity and prediction.
[A] The multivariate Cox proportional hazards regression model from the survival package was implemented with the SCAD penalty function applied using the hdnom, penalized and ncvreg packages.
[B] The method contains 2 hyperparameters:
[B.1] nfolds = fold numbers of cross-validation fixed at a value = 5
[B.2] gammas = MCP penalty made to vary across a range of values equal to 2.10 to 5.00
[C] The method selected 15 out of the 25 predictors for the final Cox Proportional Hazards model as follows:
[C.1] age variable (numeric)
[C.2] betablok variable (factor)
[C.3] acei variable (factor)
[C.4] aspirin variable (factor)
[C.5] diuretic.thiazide variable (factor)
[C.6] insulin variable (factor)
[C.7] resting.systolic.bp variable (numeric)
[C.8] resting.hr variable (numeric)
[C.9] lvef.metabl variable (numeric)
[C.10] peak.vo2 variable (numeric)
[C.11] interval variable (numeric)
[C.12] cad variable (factor)
[C.13] bun variable (numeric)
[C.14] sodium variable (numeric)
[C.15] male variable (factor)
[D] Optimal model parameters were determined as:
[D.1] Lambda = 0.011
[D.2] Gamma = 5.000
[E] Internally validated Time-Dependent AUROC metrics using bootstrap resampling of the train data showed good performance overall except for the last few time periods and are summarized as follows:
[E.1] 12 months = 0.76617
[E.2] 24 months = 0.77818
[E.3] 36 months = 0.68530
[E.4] 48 months = 0.67992
[E.5] 60 months = 0.66145
[E.6] 72 months = 0.67633
[E.7] 84 months = 0.68757
[E.8] 96 months = 0.61512
[E.9] 108 months = 0.62571
[E.10] 120 months = 0.26660
[F] Externally validated Time-Dependent AUROC metrics using the test data showed good performance overall except for the last few time periods and are summarized as follows:
[F.1] 12 months = 0.80297
[F.2] 24 months = 0.74514
[F.3] 36 months = 0.64428
[F.4] 48 months = 0.60077
[F.5] 60 months = 0.64891
[F.6] 72 months = 0.60476
[F.7] 84 months = 0.56636
[F.8] 96 months = 0.65165
[F.9] 108 months = 0.45666
[F.10] 120 months = 0.27500
[G] Model predictions at time = 60 months were reasonably calibrated for both the train and test data. Kaplan-Meier survival curves for the three risk groups from the calibration data were differentially profiled with p-value < 0.05.
[H] Model predictions at time = 120 months were reasonably calibrated for both the train and test data. Kaplan-Meier survival curves for the three risk groups from the calibration data were differentially profiled with p-value < 0.05.
Code Chunk | Output
##################################
# Loading the preprocessed data
##################################
PMA_PreModelling_SCAD <- peakVO2.Source[,!names(peakVO2.Source) %in% rownames(UF_CoxPHModelSummary_NonSig)]
##################################
# Setting the appropriate variable types
##################################
PMA_PreModelling_SCAD$died <- as.factor(PMA_PreModelling_SCAD$died)
##################################
# Creating a local object
# for the train and test sets
##################################
set.seed(88888888)
TrainIndex <- createDataPartition(PMA_PreModelling_SCAD$died,
p = 0.8,
list = FALSE,
times = 1)
PMA_PreModelling_SCAD_Train <- PMA_PreModelling_SCAD[ TrainIndex,]
PMA_PreModelling_SCAD_Test <- PMA_PreModelling_SCAD[-TrainIndex,]
PMA_PreModelling_SCAD_Train$died <- as.numeric(PMA_PreModelling_SCAD_Train$died)
PMA_PreModelling_SCAD_Test$died <- as.numeric(PMA_PreModelling_SCAD_Test$died)
##################################
# Formulating the model components
# for the train data
##################################
SCAD_Train_Predictors <- as.matrix(
PMA_PreModelling_SCAD_Train[,!names(PMA_PreModelling_SCAD_Train)
%in% c("ttodead","died")])
SCAD_Train_Time <- PMA_PreModelling_SCAD_Train$ttodead
SCAD_Train_Status <- PMA_PreModelling_SCAD_Train$died
SCAD_Train_Response <- survival::Surv(SCAD_Train_Time, SCAD_Train_Status)
##################################
# Fitting a SCAD model
##################################
SCAD_Train_CoxPH_Fit <- fit_scad(SCAD_Train_Predictors,
SCAD_Train_Response,
nfolds = 5,
gammas = c(2.1, 3, 4, 5),
seed = 88888888,
trace = FALSE)
##################################
# Extracting the model objects and
# optimal hyperparameters
# from the SCAD model
##################################
(SCAD_Train_CoxPH_Model <- SCAD_Train_CoxPH_Fit$model)## $beta
## 0.0111
## age 0.0027219110
## betablok -0.3787249214
## acei 0.0479447613
## angioten.II 0.0000000000
## anti.arrhy 0.0000000000
## anti.coag 0.0000000000
## aspirin -0.2074697706
## vasodilator 0.0000000000
## diuretic.loop 0.0000000000
## diuretic.thiazide 0.0202248333
## insulin 0.0814959210
## surgery.pacemaker 0.0000000000
## surgery.cabg 0.0000000000
## resting.systolic.bp -0.0016822946
## resting.hr 0.0028380170
## lvef.metabl -0.0118827416
## peak.vo2 -0.0642100265
## interval -0.0011147473
## cad 0.0953210387
## bun 0.0169187797
## sodium -0.0002547134
## hgb 0.0000000000
## glucose 0.0000000000
## male 0.5225160066
## crcl 0.0000000000
##
## $iter
## [1] 42
##
## $lambda
## [1] 0.01114746
##
## $penalty
## [1] "SCAD"
##
## $gamma
## [1] 5
##
## $alpha
## [1] 1
##
## $convex.min
## NULL
##
## $loss
## [1] 3933.919
##
## $penalty.factor
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##
## $n
## [1] 1785
##
## $time
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2
## [19] 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3
## [37] 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4
## [55] 4 4 4 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6
## [73] 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 8 8
## [91] 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9
## [109] 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 11 11 11
## [127] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [145] 11 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
## [163] 12 12 12 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
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## [199] 14 14 14 14 14 14 14 14 14 14 15 15 15 15 15 15 15 15
## [217] 15 15 15 15 16 16 16 16 16 16 16 16 16 16 16 16 16 16
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## [253] 17 17 17 18 18 18 18 18 18 18 18 18 18 18 18 19 19 19
## [271] 19 19 19 19 19 19 19 19 19 19 19 19 19 20 20 20 20 20
## [289] 20 20 20 20 20 20 21 21 21 21 21 21 21 21 21 21 21 21
## [307] 21 21 21 21 21 21 21 21 21 22 22 22 22 22 22 22 22 22
## [325] 22 22 22 22 22 23 23 23 23 23 23 23 23 23 23 23 23 23
## [343] 23 23 23 23 23 23 24 24 24 24 24 24 24 24 25 25 25 25
## [361] 25 25 25 25 25 25 25 25 25 25 25 26 26 26 26 26 26 26
## [379] 26 26 26 26 26 26 26 26 26 26 26 26 27 27 27 27 27 27
## [397] 27 27 27 27 27 27 28 28 28 28 28 28 28 28 28 28 28 28
## [415] 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29
## [433] 29 29 29 29 29 29 30 30 30 30 30 30 30 30 30 30 30 30
## [451] 30 30 30 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31
## [469] 31 31 31 31 31 31 32 32 32 32 32 32 32 32 32 32 32 32
## [487] 32 32 32 32 32 32 33 33 33 33 33 33 33 33 33 33 33 34
## [505] 34 34 34 34 34 34 34 34 34 35 35 35 35 35 35 35 35 35
## [523] 35 35 35 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36
## [541] 36 36 36 36 37 37 37 37 37 37 37 37 37 37 37 37 37 37
## [559] 37 38 38 38 38 38 38 38 38 38 38 39 39 39 39 39 39 39
## [577] 39 39 39 39 39 39 39 39 39 39 39 40 40 40 40 40 40 40
## [595] 40 40 40 40 40 40 40 40 40 40 40 41 41 41 41 41 41 41
## [613] 41 41 41 41 41 42 42 42 42 42 42 42 42 42 42 42 42 42
## [631] 42 42 42 43 43 43 43 43 43 43 43 43 43 43 43 43 43 44
## [649] 44 44 44 44 44 44 44 45 45 45 45 45 45 45 45 45 45 45
## [667] 45 46 46 46 46 46 46 46 46 46 46 46 46 46 46 47 47 47
## [685] 47 47 47 47 47 47 48 48 48 48 48 48 48 48 48 48 48 48
## [703] 49 49 49 49 49 49 49 49 49 49 49 49 49 50 50 50 50 50
## [721] 50 50 50 50 50 50 50 51 51 51 51 51 51 51 51 51 51 51
## [739] 51 51 51 52 52 52 52 52 52 52 52 52 52 52 52 52 52 53
## [757] 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 54 54
## [775] 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 54 55 55
## [793] 55 55 55 55 55 55 55 55 55 55 55 55 55 55 56 56 56 56
## [811] 56 56 56 56 56 56 56 56 56 57 57 57 57 57 57 57 57 57
## [829] 57 57 57 57 57 57 57 57 58 58 58 58 58 58 58 58 58 58
## [847] 58 58 58 58 58 58 58 59 59 59 59 59 59 59 59 59 59 59
## [865] 59 59 59 59 59 59 59 59 59 59 59 60 60 60 60 60 60 60
## [883] 60 60 60 60 61 61 61 61 61 61 61 61 61 61 61 62 62 62
## [901] 62 62 62 62 62 62 62 62 62 62 62 62 63 63 63 63 63 63
## [919] 63 63 63 63 63 63 63 63 63 63 64 64 64 64 64 64 64 64
## [937] 64 64 64 64 64 64 65 65 65 65 65 65 65 65 65 65 65 65
## [955] 65 65 65 65 65 65 65 66 66 66 66 66 66 66 66 66 66 67
## [973] 67 67 67 67 67 67 67 67 68 68 68 68 68 68 68 68 68 68
## [991] 68 68 68 68 68 69 69 69 69 69 69 69 69 69 69 69 70 70
## [1009] 70 70 70 70 70 71 71 71 71 71 71 71 71 71 71 72 72 72
## [1027] 72 72 72 72 72 72 72 72 72 73 73 73 73 73 73 73 73 74
## [1045] 74 74 74 74 74 74 74 74 74 74 75 75 75 75 75 75 75 75
## [1063] 75 75 75 75 75 76 76 76 76 76 76 76 76 76 76 76 76 76
## [1081] 76 76 76 76 76 77 77 77 77 77 77 77 77 77 77 77 77 77
## [1099] 77 77 78 78 78 78 78 78 78 78 78 78 78 78 79 79 79 79
## [1117] 79 79 79 79 79 79 79 79 79 80 80 80 80 80 80 80 80 80
## [1135] 81 81 81 81 81 81 81 81 81 81 81 82 82 82 82 82 82 82
## [1153] 82 82 82 82 82 82 82 82 83 83 83 83 83 83 83 83 83 83
## [1171] 83 83 83 83 83 84 84 84 84 84 84 84 84 84 84 84 84 84
## [1189] 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 86 86 86
## [1207] 86 86 86 86 86 86 86 86 86 86 86 86 87 87 87 87 87 87
## [1225] 87 87 87 87 87 87 87 88 88 88 88 88 88 88 88 88 88 88
## [1243] 88 88 88 88 89 89 89 89 89 89 89 89 89 89 89 89 89 89
## [1261] 89 89 89 89 89 90 90 90 90 90 90 90 90 90 90 90 90 90
## [1279] 90 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91
## [1297] 92 92 92 92 92 92 92 92 92 92 92 92 92 92 92 92 92 93
## [1315] 93 93 93 93 93 93 93 93 93 94 94 94 94 94 94 94 94 94
## [1333] 94 94 94 94 94 94 94 94 94 95 95 95 95 95 95 95 95 95
## [1351] 95 95 95 95 96 96 96 96 96 96 96 96 96 96 96 96 96 96
## [1369] 96 96 96 96 96 96 96 96 97 97 97 97 97 97 97 97 97 97
## [1387] 97 97 97 97 97 97 97 97 97 98 98 98 98 98 98 98 98 98
## [1405] 98 98 98 98 98 98 98 98 98 98 98 99 99 99 99 99 99 100
## [1423] 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
## [1441] 100 101 101 101 101 101 101 101 101 101 101 101 101 101 101 102 102 102
## [1459] 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 103
## [1477] 103 103 103 103 103 103 103 103 104 104 104 104 104 104 104 104 104 104
## [1495] 104 104 104 104 104 104 105 105 105 105 105 105 105 105 105 105 106 106
## [1513] 106 106 106 106 106 106 106 106 106 106 106 106 106 107 107 107 107 107
## [1531] 107 107 107 107 107 107 107 108 108 108 108 108 108 108 108 108 108 108
## [1549] 108 108 108 109 109 109 109 109 109 109 109 109 109 109 109 109 109 110
## [1567] 110 110 110 110 110 110 110 110 110 110 111 111 111 111 111 111 111 111
## [1585] 112 112 112 112 112 112 112 113 113 113 113 113 113 113 113 113 113 113
## [1603] 113 113 113 113 113 114 114 114 114 114 114 114 114 114 114 114 114 114
## [1621] 114 115 115 115 115 115 115 115 115 115 115 115 115 115 115 115 115 115
## [1639] 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116 116
## [1657] 117 117 117 117 117 117 117 117 117 117 117 117 117 117 118 118 118 118
## [1675] 119 119 119 119 119 119 119 119 119 119 119 119 119 119 120 120 120 120
## [1693] 120 120 120 120 120 120 120 120 120 120 120 120 120 120 120 121 121 121
## [1711] 121 121 121 121 121 121 121 121 122 122 122 122 122 122 122 122 122 122
## [1729] 122 122 122 123 123 123 123 123 123 123 123 124 124 124 124 124 124 124
## [1747] 124 124 125 125 125 125 125 125 125 125 125 125 125 125 125 126 126 126
## [1765] 126 126 126 126 126 126 126 126 126 126 126 127 127 127 127 127 127 127
## [1783] 127 127 127
##
## $fail
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 0 0
## [149] 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0
## [186] 0 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 1 0 0 1 0
## [223] 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1
## [260] 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1
## [297] 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 0 0 0 0 1 0 1 1
## [334] 1 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 1 1 1 1 0 0 1 0 0 0 0
## [371] 0 0 1 0 1 1 1 1 1 1 0 1 1 0 1 0 0 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 1 0 0 1 1
## [408] 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1
## [445] 1 1 1 1 0 0 0 0 0 0 1 0 1 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1
## [482] 1 1 0 1 0 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 1 0 0 0 1 1 0 1 1
## [519] 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 0 0 0
## [556] 0 0 0 0 1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 0 1 1 1 0 0 1 0 0 0 0 0 1 0 1 1 1
## [593] 1 1 1 1 0 0 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 0 0 1 0 1 1 1 0
## [630] 0 0 0 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 1 0 0 0 0 0 0 0
## [667] 1 1 0 0 1 1 1 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 1 1 1 0 1 1 1 0 0 0 0 0
## [704] 1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 1 1 1 0 0 1 0 1 0 0 0 0 0
## [741] 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
## [778] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 1 1 1 0 0 0 0 0 0 1 0 1 1 0 0 0 0
## [815] 0 0 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 0 1 0 0 0 0
## [852] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 1 1 0 0 0 1 1
## [889] 0 1 1 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 0 0 0
## [926] 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 1 1 1 0 0 0 0 0 0 0 1
## [963] 1 0 1 1 0 1 1 0 0 1 0 0 1 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
## [1000] 1 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0
## [1037] 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0
## [1074] 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 0
## [1111] 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## [1148] 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0
## [1185] 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1
## [1222] 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0
## [1259] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0
## [1296] 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0
## [1333] 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 0
## [1370] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## [1407] 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0
## [1444] 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1481] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1518] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1555] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## [1592] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1629] 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1666] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1703] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1740] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1777] 0 0 0 0 0 0 0 0 0
##
## $order
## [1] 198 260 499 699 873 1130 1248 1278 1370 1416 1478 1550 1657 89
## [15] 93 267 387 571 580 585 635 708 965 1258 1308 1433 1695 1702
## [29] 3 148 262 430 500 562 639 861 991 1056 1342 1378 1388 1681
## [43] 1724 1743 1757 357 614 634 716 718 744 926 931 1148 1332 1603
## [57] 1626 147 302 629 763 808 888 1010 1144 1593 70 94 183 411
## [71] 464 513 842 964 984 1139 1158 1162 1456 1742 158 369 393 406
## [85] 503 695 701 1769 123 133 259 320 392 441 515 694 1266 1597
## [99] 1630 9 190 291 332 581 772 773 907 908 946 947 1352 1650
## [113] 1763 30 318 355 424 512 572 902 1075 1282 1420 7 177 182
## [127] 256 276 283 368 456 610 625 626 702 819 850 915 1008 1093
## [141] 1243 1526 1748 1754 1759 57 193 281 354 378 381 419 561 1045
## [155] 1218 1303 1393 1634 1648 1744 1746 1750 1751 1752 1753 6 49 132
## [169] 249 266 432 485 502 642 715 1089 1146 1214 1287 1740 1741 1745
## [183] 1747 1749 137 202 233 394 426 480 486 525 769 849 1153 1226
## [197] 1249 1343 1527 1615 1689 1704 1736 1738 1739 1758 1760 1761 1 37
## [211] 83 175 188 413 505 1222 1383 1502 1717 1718 135 217 218 238
## [225] 277 557 591 730 1040 1076 1564 1582 1684 1726 1731 1737 1755 189
## [239] 293 327 637 1481 1503 1611 1613 1640 1661 1706 1728 1729 1730 1732
## [253] 1734 1735 1756 126 142 296 544 791 866 878 1576 1708 1723 1725
## [267] 1733 178 541 768 954 1208 1224 1225 1242 1254 1614 1674 1696 1719
## [281] 1721 1727 1785 90 139 174 308 543 1023 1298 1429 1715 1720 1722
## [295] 78 79 167 203 269 288 326 1150 1371 1399 1435 1693 1701 1707
## [309] 1709 1710 1711 1713 1714 1716 1774 50 209 384 389 477 656 1119
## [323] 1179 1321 1505 1699 1705 1712 1768 124 364 367 382 395 749 922
## [337] 972 1020 1046 1256 1330 1419 1688 1691 1697 1698 1700 1703 36 141
## [351] 250 1052 1497 1662 1692 1694 248 270 412 472 721 820 997 1364
## [365] 1612 1667 1683 1685 1686 1687 1690 31 75 187 289 360 492 745
## [379] 770 823 851 895 993 1609 1649 1658 1677 1679 1682 1762 152 313
## [393] 579 1019 1079 1181 1636 1672 1676 1678 1680 1764 55 244 830 834
## [407] 999 1255 1384 1596 1605 1668 1673 1675 66 221 298 334 380 511
## [421] 531 1110 1216 1395 1543 1580 1583 1617 1637 1651 1659 1660 1663 1665
## [435] 1666 1669 1670 1671 58 86 329 344 423 666 846 932 942 1482
## [449] 1624 1652 1653 1656 1664 60 212 258 351 422 600 870 904 1136
## [463] 1295 1440 1577 1589 1610 1639 1641 1642 1643 1654 1655 1770 29 51
## [477] 80 88 136 165 652 913 917 1299 1447 1483 1551 1635 1644 1645
## [491] 1646 1775 241 272 300 436 692 777 970 1044 1206 1585 1647 309
## [505] 346 670 685 733 1142 1197 1470 1627 1638 299 303 304 407 576
## [519] 654 1316 1369 1404 1491 1518 1601 23 27 72 255 366 374 689
## [533] 725 807 893 1051 1300 1409 1619 1620 1628 1629 1633 1781 127 421
## [547] 463 474 475 644 1060 1192 1588 1621 1625 1631 1632 1778 1783 484
## [561] 554 727 795 950 975 1514 1595 1622 1623 77 159 275 567 1097
## [575] 1159 1189 1201 1337 1353 1366 1368 1423 1590 1616 1618 1772 1776 106
## [589] 129 376 496 821 1095 1215 1304 1360 1363 1367 1376 1488 1555 1572
## [603] 1773 1782 1784 204 648 728 865 949 1121 1194 1220 1608 1777 1779
## [617] 1780 19 85 115 179 222 287 343 440 767 797 1116 1462 1600
## [631] 1602 1607 1771 87 116 144 330 410 551 811 1098 1340 1546 1574
## [645] 1598 1604 1606 2 112 125 546 613 675 956 1477 5 44 280
## [659] 680 1579 1584 1586 1587 1591 1592 1599 1766 196 252 447 448 939
## [673] 952 1024 1171 1240 1348 1438 1498 1578 1581 102 569 664 1397 1487
## [687] 1567 1568 1573 1575 46 153 987 1014 1281 1284 1403 1467 1556 1569
## [701] 1570 1571 337 1127 1227 1320 1544 1554 1557 1558 1560 1561 1562 1565
## [715] 1566 160 396 587 617 852 1236 1537 1541 1552 1553 1559 1563 18
## [729] 105 264 286 307 1261 1412 1425 1523 1539 1540 1545 1547 1549 311
## [743] 437 859 1151 1273 1292 1525 1528 1534 1535 1536 1538 1542 1548 59
## [757] 145 400 443 457 620 762 1275 1436 1490 1499 1506 1520 1530 1531
## [771] 1532 1533 92 207 263 653 1101 1301 1317 1354 1492 1504 1510 1513
## [785] 1515 1519 1521 1522 1524 1529 65 166 268 359 361 478 597 650
## [799] 671 856 948 1461 1471 1512 1516 1517 22 169 528 709 934 1170
## [813] 1228 1362 1489 1507 1508 1509 1511 76 254 349 473 490 615 804
## [827] 862 1391 1427 1453 1465 1494 1495 1496 1500 1501 64 206 253 279
## [841] 372 470 536 668 1154 1289 1297 1414 1464 1474 1485 1486 1493 39
## [855] 41 42 199 282 319 687 784 953 1133 1441 1463 1466 1468 1469
## [869] 1472 1473 1476 1479 1480 1484 1767 162 297 565 737 978 1183 1195
## [883] 1267 1455 1459 1475 38 52 323 647 906 1269 1446 1448 1457 1458
## [897] 1460 143 227 251 684 806 1069 1408 1434 1443 1449 1450 1451 1452
## [911] 1454 1594 101 128 130 211 216 618 778 1229 1341 1413 1431 1437
## [925] 1439 1442 1444 1445 34 228 352 455 847 887 1204 1379 1380 1417
## [939] 1422 1428 1430 1432 24 181 292 491 560 627 1081 1112 1115 1141
## [953] 1163 1335 1381 1401 1411 1421 1424 1426 1765 62 242 339 350 693
## [967] 855 986 1143 1415 1418 53 173 314 461 750 871 1109 1389 1405
## [981] 54 229 290 483 1328 1359 1377 1382 1385 1394 1400 1402 1406 1407
## [995] 1410 69 95 416 628 1108 1213 1351 1390 1392 1396 1398 197 465
## [1009] 814 928 1016 1386 1387 96 121 317 1346 1356 1358 1372 1373 1374
## [1023] 1375 138 476 578 645 864 1085 1099 1221 1355 1357 1361 1365 186
## [1037] 1259 1322 1336 1338 1347 1349 1350 549 624 738 828 1032 1291 1329
## [1051] 1331 1339 1344 1345 305 732 748 822 1080 1279 1306 1312 1319 1326
## [1065] 1327 1333 1334 161 184 208 239 246 428 690 766 780 1065 1210
## [1079] 1251 1309 1314 1318 1323 1324 1325 16 220 225 235 449 683 1211
## [1093] 1286 1290 1305 1307 1310 1311 1313 1315 35 100 200 548 812 898
## [1107] 990 1113 1191 1294 1296 1302 171 438 458 459 921 985 1096 1120
## [1121] 1253 1262 1285 1288 1293 1031 1084 1233 1264 1268 1276 1277 1280 1283
## [1135] 113 114 163 1132 1217 1257 1265 1270 1271 1272 1274 14 316 383
## [1149] 429 454 537 633 741 891 955 1114 1219 1250 1260 1263 71 156
## [1163] 224 348 391 857 1176 1234 1235 1237 1239 1245 1246 1247 1252 98
## [1177] 417 471 482 514 612 623 677 1184 1202 1238 1241 1244 20 340
## [1191] 353 506 539 1137 1177 1190 1196 1203 1212 1223 1230 1231 1232 21
## [1205] 110 333 445 712 1072 1094 1104 1168 1169 1180 1199 1205 1207 1209
## [1219] 56 325 586 810 973 1106 1107 1186 1187 1188 1193 1198 1200 45
## [1233] 214 322 342 388 839 1165 1166 1167 1173 1174 1175 1178 1182 1185
## [1247] 13 149 237 510 540 883 892 1105 1118 1128 1149 1152 1155 1156
## [1261] 1157 1160 1161 1164 1172 867 875 992 1007 1013 1037 1100 1102 1134
## [1275] 1135 1138 1140 1145 1147 26 74 104 582 824 1035 1091 1092 1103
## [1289] 1117 1122 1123 1124 1125 1126 1129 1131 63 168 379 901 903 920
## [1303] 1009 1041 1064 1071 1077 1078 1083 1087 1088 1090 1111 774 1018 1048
## [1317] 1059 1067 1068 1070 1073 1082 1086 150 210 274 450 698 829 854
## [1331] 858 941 977 1030 1049 1050 1055 1058 1061 1066 1074 245 945 981
## [1345] 1004 1029 1042 1043 1047 1053 1054 1057 1062 1063 48 120 134 306
## [1359] 592 598 646 775 869 909 937 1005 1021 1022 1025 1027 1028 1033
## [1373] 1034 1036 1038 1039 285 321 754 788 943 969 982 994 996 1000
## [1387] 1001 1002 1003 1006 1011 1012 1015 1017 1026 192 427 550 756 800
## [1401] 899 957 960 961 966 968 974 976 979 980 983 988 989 995
## [1415] 998 232 345 446 959 963 967 119 271 371 386 415 495 498
## [1429] 573 608 676 740 885 916 927 935 936 944 951 958 962 109
## [1443] 172 377 466 467 641 838 848 900 912 930 933 938 940 122
## [1457] 131 261 705 760 836 860 880 889 894 896 910 911 914 918
## [1471] 919 923 924 925 929 43 191 201 460 827 882 884 897 905
## [1485] 151 240 278 295 328 802 843 872 874 876 877 879 881 886
## [1499] 890 971 25 294 468 764 789 825 837 845 863 868 284 315
## [1513] 661 710 759 782 813 826 831 832 833 835 840 841 844 68
## [1527] 118 385 402 425 493 752 755 785 816 817 818 338 401 451
## [1541] 479 696 783 786 792 798 799 803 805 809 815 347 373 375
## [1555] 604 621 765 771 781 787 790 793 794 796 801 47 155 213
## [1569] 336 439 469 530 711 761 776 779 108 236 662 743 751 753
## [1583] 757 758 370 552 735 739 742 746 747 170 195 226 442 713
## [1597] 714 720 722 723 724 726 729 731 734 736 853 146 194 231
## [1611] 301 444 589 640 657 703 704 706 707 717 719 12 40 61
## [1625] 180 215 257 420 453 529 566 583 609 672 688 691 697 700
## [1639] 17 84 107 403 622 638 655 660 663 667 669 673 674 678
## [1653] 679 681 682 686 15 33 73 81 103 157 234 324 588 616
## [1667] 643 658 659 665 331 631 649 651 32 82 230 265 312 335
## [1681] 418 431 602 603 611 630 632 636 28 140 185 219 310 362
## [1695] 452 524 574 590 593 594 595 596 599 601 605 606 607 91
## [1709] 99 111 117 223 243 435 489 533 575 584 8 10 358 390
## [1723] 519 521 527 547 555 564 568 570 577 67 247 408 553 556
## [1737] 558 559 563 11 341 356 397 405 433 520 534 545 154 176
## [1751] 399 404 414 504 518 523 526 532 535 538 542 164 205 363
## [1765] 365 409 434 497 501 507 508 509 516 517 522 4 97 273
## [1779] 398 462 481 487 488 494 619
##
## $Eta
## [,1]
## [1,] -0.7383269824
## [2,] 0.7778661246
## [3,] -1.5398452693
## [4,] -0.7506561916
## [5,] -0.0387182219
## [6,] -0.5326969119
## [7,] -0.1675550123
## [8,] -1.7168580357
## [9,] 0.3851141028
## [10,] -1.7945931306
## [11,] 0.3310392698
## [12,] -1.6309603565
## [13,] -0.6221370597
## [14,] 0.2129138377
## [15,] -0.8853394812
## [16,] 0.4237297868
## [17,] 0.0174843540
## [18,] -1.2596265350
## [19,] -0.2583079250
## [20,] 0.3207605072
## [21,] 0.4657696326
## [22,] -0.4470403979
## [23,] 1.3444519575
## [24,] -0.1743690929
## [25,] -0.5108628279
## [26,] -0.0877969093
## [27,] -0.4036965778
## [28,] -0.6029483119
## [29,] -0.3350395118
## [30,] -1.1695767183
## [31,] -0.3394171080
## [32,] -0.5464675933
## [33,] 0.0605595210
## [34,] -0.0108369637
## [35,] -0.0263106300
## [36,] -0.0355303820
## [37,] 1.1936646409
## [38,] 0.1481667057
## [39,] 0.4590754851
## [40,] 0.3339773443
## [41,] -0.3148148770
## [42,] -0.3236854373
## [43,] -1.4850863499
## [44,] -0.1546652382
## [45,] 0.4609764011
## [46,] -0.4753330740
## [47,] -0.2359287361
## [48,] -0.9898866042
## [49,] -1.4366994088
## [50,] -0.5364317228
## [51,] -0.7223396289
## [52,] -1.2643450731
## [53,] 0.1303924959
## [54,] 0.6234483727
## [55,] -0.9920516006
## [56,] -0.4707823533
## [57,] -0.1561348020
## [58,] -0.4638974457
## [59,] -0.8820582362
## [60,] 0.2462564705
## [61,] 0.2028533608
## [62,] -0.0351106427
## [63,] 0.8617327030
## [64,] -0.2354985699
## [65,] -1.0795845897
## [66,] -1.0327918756
## [67,] -0.6791194627
## [68,] 0.5425935525
## [69,] -0.0904496979
## [70,] -0.7538602107
## [71,] 0.8505566791
## [72,] -0.7154346899
## [73,] -0.0578406397
## [74,] -0.7329443051
## [75,] -0.5157530724
## [76,] -0.3385943681
## [77,] -0.8443440488
## [78,] -0.4452990079
## [79,] -1.2749797621
## [80,] 0.3289985267
## [81,] -0.0201017860
## [82,] -0.1951874222
## [83,] -0.7142702586
## [84,] -0.0974752045
## [85,] -1.7071893507
## [86,] 0.1172048909
## [87,] -0.8838427363
## [88,] -0.0484273153
## [89,] 0.9432252286
## [90,] 0.0029525901
## [91,] 0.1832549101
## [92,] -0.7168688316
## [93,] -2.9674661991
## [94,] 0.3343292992
## [95,] 0.2450029539
## [96,] 0.0159080070
## [97,] -1.3031881741
## [98,] -0.9120502199
## [99,] -0.3880108720
## [100,] -0.8837681217
## [101,] -0.0106895068
## [102,] 0.2386803809
## [103,] -0.2176914595
## [104,] 0.0267012767
## [105,] 0.2340056264
## [106,] 0.0984704945
## [107,] -1.3058353079
## [108,] -0.9357788527
## [109,] -0.4301310740
## [110,] -0.4248804167
## [111,] -1.9909577290
## [112,] -0.4118794022
## [113,] -0.2029500733
## [114,] -2.1587343611
## [115,] -1.1729665052
## [116,] -0.0427570023
## [117,] -0.8657823510
## [118,] 0.4885323081
## [119,] -0.8528389440
## [120,] -0.7167947326
## [121,] -1.6952392088
## [122,] -1.7231966790
## [123,] -0.9589999251
## [124,] -0.7297348599
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## [1783,] -1.4073915791
## [1784,] -0.9244666353
## [1785,] -2.0242529125
##
## $X
## age betablok acei angioten.II anti.arrhy anti.coag
## 249 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 318 -0.02486626 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 631 0.69686174 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 883 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1095 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1405 1.32837374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1554 0.24578174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1589 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1708 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1773 -1.73897025 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1850 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1935 -1.91940225 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2077 0.78707774 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 110 0.60664574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 116 -3.27264225 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 328 0.15556574 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 482 0.42621374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 723 -1.10745826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 732 0.51642974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 737 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 802 0.24578174 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 894 1.23815774 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1204 1.23815774 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1564 1.32837374 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1632 0.51642974 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1794 0.60664574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2120 -2.73134625 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2127 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 3 2.23053373 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 188 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 320 0.33599774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 539 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 632 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 712 0.60664574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 808 1.41858974 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1081 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1232 -0.65637826 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1313 -0.20529826 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1674 0.42621374 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1717 -1.10745826 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1727 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2104 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2154 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2181 0.60664574 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2197 0.87729374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 445 -2.19005025 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 777 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 800 2.32074973 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 904 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 906 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 933 1.41858974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1155 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1162 2.50118173 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1424 0.96750974 -1.3440996 0.5538366 2.5619444 -0.5322119 -0.8193585
## 1664 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2000 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2035 1.77945374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 186 0.24578174 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 375 0.51642974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 792 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 957 1.50880574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1014 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1112 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1253 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1420 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1988 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 88 0.69686174 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 117 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 229 -2.55091425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 512 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 583 -0.29551426 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 648 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1058 1.23815774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1203 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1225 0.60664574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1415 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1438 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1442 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1824 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2178 0.24578174 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 200 0.69686174 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 460 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 490 -0.38573026 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 505 0.78707774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 636 1.32837374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 877 0.96750974 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 885 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2214 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 157 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 170 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 317 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 399 -0.65637826 -1.3440996 -1.8055867 2.5619444 1.8789510 1.2204670
## 489 1.50880574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 552 1.59902174 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 652 -0.20529826 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 876 -0.47594626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1573 1.14794174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1993 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2040 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 10 -0.83681026 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 238 0.42621374 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 360 0.69686174 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 416 1.86966973 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 733 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 967 -0.29551426 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 968 1.32837374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1134 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1135 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1183 -1.37810625 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1184 -0.29551426 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1686 -1.91940225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2069 0.78707774 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 2204 -1.82918625 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 38 0.33599774 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 392 -0.56616226 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 443 0.51642974 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 532 -0.92702626 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 647 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 724 -1.91940225 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1128 0.87729374 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1337 0.06534974 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1593 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1777 0.24578174 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 8 1.32837374 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 222 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 228 -1.55853825 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 313 1.41858974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 342 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 350 1.14794174 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 459 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 573 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 771 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 788 0.87729374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 789 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 886 -1.28789025 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1029 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1067 0.87729374 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1143 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1251 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1360 -0.29551426 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1547 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1906 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2187 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2193 1.14794174 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2199 1.14794174 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 74 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 241 -1.55853825 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 348 0.15556574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 442 1.50880574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 470 0.78707774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 474 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 524 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 711 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1299 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1515 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1624 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1736 0.78707774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2044 0.33599774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2067 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2183 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2185 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2189 1.23815774 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2190 1.68923774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2191 -1.10745826 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 2192 -1.37810625 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 7 0.15556574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 62 1.05772574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 169 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 305 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 327 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 542 -1.82918625 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 613 1.77945374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 635 0.60664574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 812 0.24578174 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 903 -0.29551426 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1355 0.60664574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1422 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1509 -1.73897025 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1601 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2175 0.15556574 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 2177 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2184 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2186 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2188 0.96750974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 174 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 253 1.77945374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 286 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 492 0.69686174 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 534 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 607 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 614 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 663 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 964 0.78707774 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1066 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1433 -1.01724226 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1524 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1555 0.78707774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1675 0.87729374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1907 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2021 0.60664574 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 2113 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2129 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2170 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2172 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2173 -0.38573026 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2198 0.24578174 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2200 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2201 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1 1.77945374 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 46 0.96750974 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 103 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 220 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 236 0.06534974 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 514 1.32837374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 638 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1520 0.60664574 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1722 -0.02486626 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1877 0.06534974 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 2145 0.96750974 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 2146 0.69686174 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 172 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 269 0.51642974 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 270 0.42621374 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 291 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 343 -1.82918625 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 707 -1.10745826 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 745 0.60664574 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 919 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1293 0.42621374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1338 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1952 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1975 -1.19767425 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2108 -0.38573026 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 2156 -1.73897025 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2164 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2171 0.96750974 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2195 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 237 1.86966973 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 363 0.60664574 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 411 0.51642974 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 806 0.15556574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1854 0.06534974 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1879 -0.11508226 0.7439925 0.5538366 2.5619444 -0.5322119 -0.8193585
## 2015 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2018 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2056 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2081 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2132 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2158 0.42621374 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2159 0.96750974 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 2163 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2165 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2168 -1.10745826 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2169 2.05010173 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2196 0.42621374 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 161 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 180 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 367 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 685 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 990 1.59902174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1086 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1100 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1969 -0.11508226 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 2135 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2153 0.33599774 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 2155 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2166 0.42621374 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 223 0.15556574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 681 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 963 0.15556574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1192 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1500 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1522 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1523 0.78707774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1545 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1560 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2019 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2095 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2121 -0.83681026 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 2147 0.60664574 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 2151 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2157 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2231 0.96750974 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 112 0.24578174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 176 1.68923774 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 219 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 382 0.87729374 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 684 1.50880574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1268 0.06534974 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1617 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1786 1.05772574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2143 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2148 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2152 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 98 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 99 -0.65637826 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 209 0.69686174 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 254 -0.29551426 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 331 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 357 -0.02486626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 410 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1426 1.23815774 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1709 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1746 1.50880574 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 1796 -0.29551426 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2118 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2126 0.60664574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2134 0.60664574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2136 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2137 -0.29551426 0.7439925 0.5538366 2.5619444 1.8789510 -0.8193585
## 2138 0.69686174 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 2141 0.78707774 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2142 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2144 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2220 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 64 0.69686174 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 261 1.77945374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 478 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 485 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 604 0.42621374 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 828 -0.56616226 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1392 0.87729374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1463 1.05772574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1647 -1.73897025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1881 1.32837374 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 2124 0.42621374 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2131 -1.28789025 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2140 -0.38573026 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2213 -0.74659426 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 159 0.42621374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 454 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 458 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 475 0.87729374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 493 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 939 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1150 -0.02486626 0.7439925 0.5538366 2.5619444 -0.5322119 -0.8193585
## 1213 -0.11508226 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1264 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1300 0.51642974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1562 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1661 1.59902174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1776 0.24578174 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2112 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2115 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2122 0.42621374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2123 -1.10745826 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2125 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2128 -0.83681026 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 45 1.41858974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 179 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 306 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1307 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1872 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2082 -0.29551426 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 2117 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2119 2.50118173 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 304 1.32837374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 334 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 513 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 598 -0.02486626 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 909 0.15556574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1030 1.14794174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1238 0.60664574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1700 0.78707774 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2017 0.78707774 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 2087 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2106 0.87729374 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 2109 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2110 -1.19767425 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2111 -0.83681026 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2114 -1.19767425 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 40 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 95 1.41858974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 234 0.78707774 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 358 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 450 0.51642974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 621 -0.83681026 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 934 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 965 1.14794174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1033 1.41858974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1068 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1120 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1234 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2011 -1.01724226 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2068 1.41858974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2078 -0.11508226 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 2100 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2102 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2105 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2203 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 193 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 387 1.95988573 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 731 1.05772574 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 1262 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1342 -3.00199425 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1465 0.42621374 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2048 -3.18242625 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2093 -0.11508226 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2099 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2101 0.51642974 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 2103 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2205 -1.37810625 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 72 2.32074973 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 299 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1043 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1049 0.24578174 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1240 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1561 -0.29551426 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1723 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1992 -2.00961825 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2005 -0.38573026 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 2088 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2094 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2097 -2.37048225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 84 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 273 0.69686174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 370 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 418 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 473 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 646 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 670 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1381 0.15556574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1512 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1738 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1928 -2.00961825 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1973 1.68923774 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1976 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2024 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2049 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2070 -0.74659426 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2079 -1.55853825 0.7439925 0.5538366 2.5619444 -0.5322119 1.2204670
## 2080 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2083 -2.19005025 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2085 2.14031773 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2086 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2090 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2091 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2092 -0.38573026 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 75 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 106 1.32837374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 413 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 432 2.41096573 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 531 -0.47594626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 839 -0.02486626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1062 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1163 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1177 -2.09983425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1855 -2.19005025 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2033 -2.46069825 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2071 1.77945374 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 2073 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2076 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2084 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 78 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 264 0.69686174 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 315 -0.56616226 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 439 -0.65637826 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 529 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 758 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1092 0.06534974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1130 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1412 -0.29551426 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1614 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1802 -0.74659426 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1970 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1983 0.42621374 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2012 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2053 -1.55853825 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2057 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2058 -2.00961825 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2059 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2074 -0.56616226 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2075 0.33599774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2215 -1.91940225 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 37 1.95988573 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 65 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 100 0.78707774 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 108 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 173 0.87729374 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 207 -0.65637826 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 824 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1141 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1145 -2.28026625 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1618 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1812 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1856 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1937 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2047 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2061 0.60664574 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2062 0.96750974 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2063 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2221 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 295 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 337 -0.02486626 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 372 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 546 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 874 1.23815774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 972 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1210 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1298 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1498 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1979 -0.47594626 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2066 -0.47594626 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 383 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 434 1.23815774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 843 1.41858974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 865 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 922 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1418 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1485 -3.27264225 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1842 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2037 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2052 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 371 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 376 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 377 1.05772574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 506 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 728 0.51642974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 826 -0.92702626 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1642 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1707 -1.28789025 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1751 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1864 1.32837374 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1895 0.96750974 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1998 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 31 1.05772574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 35 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 90 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 312 1.05772574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 457 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 466 2.05010173 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 870 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 913 0.78707774 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1013 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1117 -2.82156225 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1306 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1619 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1756 -0.38573026 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 2026 -0.65637826 0.7439925 0.5538366 2.5619444 -0.5322119 -0.8193585
## 2028 1.41858974 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2038 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2039 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2043 0.06534974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2227 1.05772574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 162 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 528 -0.11508226 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 581 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 600 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 601 1.86966973 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 814 0.42621374 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1318 -1.10745826 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1477 -0.65637826 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1982 1.05772574 -1.3440996 -1.8055867 2.5619444 1.8789510 1.2204670
## 2029 0.15556574 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2034 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2041 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2042 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2224 -0.29551426 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 2229 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 612 1.32837374 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 703 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 916 0.60664574 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 994 0.78707774 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1188 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1216 1.14794174 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1890 -0.02486626 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1991 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2031 1.50880574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2032 -0.65637826 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 97 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 201 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 341 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 717 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1364 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1439 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1474 -2.09983425 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1489 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1669 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1687 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1704 0.06534974 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 1706 -2.55091425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1780 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1984 -0.02486626 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 2022 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2025 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2217 -0.38573026 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2222 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 135 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 166 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 468 -0.02486626 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 626 1.23815774 -1.3440996 0.5538366 2.5619444 -0.5322119 -0.8193585
## 1031 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1362 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1510 0.42621374 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1625 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1696 -2.28026625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1699 -0.56616226 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1705 -1.46832225 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1715 -2.73134625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1861 -0.65637826 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1941 -0.56616226 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1964 -0.92702626 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 2218 0.15556574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2228 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2230 -3.00199425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 255 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 819 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 917 1.05772574 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1085 0.06534974 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1187 -0.47594626 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1394 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1479 1.59902174 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1517 -0.38573026 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 2009 0.87729374 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 2223 -0.56616226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2225 -1.55853825 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2226 -1.64875425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 27 2.50118173 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 105 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 147 0.78707774 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 224 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 274 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 356 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 431 0.78707774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 551 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 962 0.78707774 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 997 0.96750974 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1389 0.96750974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1831 1.77945374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1996 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1999 1.68923774 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2008 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2216 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 107 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 148 -0.11508226 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 182 1.41858974 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 414 2.05010173 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 511 -0.02486626 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 699 -0.02486626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1017 1.50880574 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1366 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1672 -0.02486626 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1931 -0.92702626 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1966 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1994 -1.01724226 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 2002 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2007 -1.82918625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 2 2.05010173 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 143 1.14794174 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 160 -0.83681026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 688 1.95988573 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 776 -0.29551426 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 850 1.32837374 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1194 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1849 -0.02486626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 5 2.23053373 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 55 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 347 1.77945374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 856 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1972 -0.20529826 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1977 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1980 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1981 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1985 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1987 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1995 0.33599774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 2207 -1.28789025 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 244 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 308 0.24578174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 560 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 561 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1173 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1190 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1269 0.24578174 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1453 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1543 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1681 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1800 0.69686174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1873 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1971 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1974 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 130 0.33599774 -1.3440996 0.5538366 2.5619444 -0.5322119 -0.8193585
## 721 1.32837374 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 837 1.23815774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1741 -1.46832225 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1860 1.50880574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1957 -0.02486626 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1960 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1965 0.51642974 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1968 0.42621374 -1.3440996 -1.8055867 2.5619444 1.8789510 1.2204670
## 58 0.42621374 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 195 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1228 -1.64875425 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1257 -0.29551426 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1592 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1596 -1.01724226 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1750 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1837 -2.28026625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1942 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1961 -1.19767425 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1962 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1963 0.60664574 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 422 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1401 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1525 0.06534974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1646 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1929 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1940 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1945 -1.64875425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1946 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1948 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1949 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1950 -0.38573026 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1953 -2.09983425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1955 -1.64875425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 202 -0.02486626 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 494 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 740 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 780 0.69686174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1069 -1.91940225 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1537 -1.37810625 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1920 -1.73897025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1926 -2.37048225 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1938 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1939 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1947 -2.73134625 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1951 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 25 -1.91940225 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 134 0.60664574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 323 0.15556574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 354 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 381 -1.64875425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1568 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1766 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1782 1.68923774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1903 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1923 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1925 1.14794174 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 1930 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1932 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1934 -3.09221025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 385 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 548 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1078 0.78707774 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1429 -1.10745826 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1581 -0.92702626 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1609 -1.82918625 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1905 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1908 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1916 0.60664574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1918 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1919 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1921 1.14794174 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1927 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1933 1.23815774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 77 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 184 -1.28789025 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 498 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 555 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 574 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 783 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 956 -0.47594626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1584 2.14031773 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1797 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1863 -1.73897025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1874 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1882 -2.28026625 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1898 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1911 1.50880574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1913 0.33599774 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1914 0.51642974 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1915 -1.91940225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 115 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 259 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 321 1.32837374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 825 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1370 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1621 0.69686174 0.7439925 0.5538366 2.5619444 -0.5322119 1.2204670
## 1643 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1688 -1.82918625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1865 -0.29551426 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1880 0.42621374 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1886 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1889 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1892 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1897 -1.73897025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1899 -2.09983425 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1900 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1904 1.23815774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1909 -1.28789025 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 83 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 208 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 329 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 449 -2.64113025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 451 -0.56616226 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 605 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 754 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 822 1.32837374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 844 -2.28026625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1074 0.60664574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1185 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1829 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1843 2.41096573 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1888 -1.64875425 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1893 1.77945374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1894 0.51642974 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 30 -1.73897025 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 211 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 666 -0.02486626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 895 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1165 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1452 -2.28026625 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1526 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1698 -1.46832225 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1862 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1883 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1884 1.41858974 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1885 1.05772574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1887 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 96 -0.56616226 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 311 1.68923774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 437 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 599 -1.28789025 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 619 -1.28789025 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 778 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1008 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1082 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1732 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1784 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1820 0.15556574 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1834 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1869 1.14794174 0.7439925 0.5538366 2.5619444 -0.5322119 1.2204670
## 1870 -0.47594626 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1871 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1875 -0.38573026 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 1876 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 82 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 258 0.33599774 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 310 1.05772574 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 345 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 464 -1.28789025 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 595 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 676 1.86966973 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 841 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1434 -1.37810625 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1605 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1616 0.87729374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1769 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1833 -2.64113025 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1846 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1858 -0.02486626 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1859 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1866 0.24578174 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 50 0.60664574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 52 1.50880574 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 53 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 250 1.95988573 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 349 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 396 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 868 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 979 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1191 1.32837374 0.7439925 0.5538366 2.5619444 -0.5322119 -0.8193585
## 1409 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1803 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1832 -1.19767425 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1836 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1839 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1841 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1844 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1845 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1848 -0.29551426 -1.3440996 0.5538366 2.5619444 -0.5322119 -0.8193585
## 1851 1.41858974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1852 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1857 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 2208 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 204 0.51642974 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 369 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 715 1.14794174 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 926 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1219 0.15556574 -1.3440996 -1.8055867 2.5619444 1.8789510 -0.8193585
## 1468 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1480 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1574 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1823 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1827 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1847 -1.64875425 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 47 0.60664574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 66 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 405 0.24578174 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 818 1.23815774 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1133 0.60664574 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1576 -2.00961825 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1811 0.69686174 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1813 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1825 1.50880574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1826 1.41858974 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1828 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 181 1.14794174 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 279 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 307 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 863 -1.55853825 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1011 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1330 0.06534974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1755 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1795 -1.82918625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1806 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1814 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1815 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1816 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1818 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1821 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1990 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 126 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 165 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 167 1.68923774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 263 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 268 -0.47594626 0.7439925 0.5538366 2.5619444 -0.5322119 -0.8193585
## 781 -1.28789025 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 973 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1528 -1.82918625 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1673 1.59902174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1768 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1788 -0.47594626 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1798 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1801 1.23815774 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1804 0.87729374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1807 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1809 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 43 1.23815774 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 280 -1.19767425 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 440 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 572 -0.74659426 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1063 0.69686174 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1111 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1496 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1718 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1719 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1774 0.78707774 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1779 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1785 -2.91177825 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1787 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1789 -0.02486626 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 32 -0.20529826 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 226 -0.38573026 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 361 1.14794174 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 620 -2.46069825 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 710 0.33599774 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 790 1.23815774 0.7439925 0.5538366 2.5619444 -0.5322119 1.2204670
## 1344 0.51642974 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1383 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1388 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1417 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1443 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1667 -1.10745826 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1720 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1748 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1765 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1778 0.42621374 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1781 -0.56616226 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1783 0.69686174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 2206 0.51642974 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 80 1.05772574 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 297 0.60664574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 424 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 438 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 875 1.68923774 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1073 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1227 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1419 -1.28789025 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1772 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1775 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 69 1.50880574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 217 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 388 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 579 -0.29551426 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 942 -0.20529826 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1093 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1380 -0.56616226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1729 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1752 1.77945374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 71 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 281 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 359 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 611 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1658 0.69686174 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1694 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1716 -1.46832225 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1721 -0.02486626 -1.3440996 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 1724 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1737 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1747 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1749 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1753 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1754 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1757 -0.02486626 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 87 -0.56616226 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 118 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 519 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 791 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1379 0.60664574 -1.3440996 -1.8055867 2.5619444 1.8789510 1.2204670
## 1508 0.87729374 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1685 -0.20529826 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1730 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1734 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1740 -0.56616226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1745 0.06534974 -1.3440996 0.5538366 2.5619444 1.8789510 -0.8193585
## 248 1.23815774 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 585 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1021 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1159 -0.38573026 -1.3440996 -1.8055867 2.5619444 1.8789510 -0.8193585
## 1259 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1725 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1726 0.42621374 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 119 0.87729374 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 153 1.32837374 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 391 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1679 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1690 -2.09983425 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1693 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1710 0.24578174 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1711 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1712 -0.56616226 0.7439925 0.5538366 2.5619444 -0.5322119 1.2204670
## 1713 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 175 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 603 -1.28789025 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 730 -0.47594626 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 815 0.96750974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1084 0.60664574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1349 -1.19767425 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1368 1.50880574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1519 0.87729374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1689 -1.46832225 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1691 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1697 0.15556574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1701 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 233 -2.19005025 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1565 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1649 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1668 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1670 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
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## 1590 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 537 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
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## 1644 1.68923774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
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## 562 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 862 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1505 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1607 -1.19767425 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1634 -0.20529826 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
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## 1641 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 44 0.24578174 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
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## 251 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 693 -0.20529826 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1019 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1123 0.51642974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
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## 1476 -1.64875425 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
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## 1615 -2.09983425 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 549 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
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## 1393 0.42621374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1559 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1569 0.42621374 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
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## 1602 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1278 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1347 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1591 -2.46069825 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1408 -2.73134625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
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## 1572 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1577 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1579 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 390 -1.55853825 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 677 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 930 0.42621374 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
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## 1193 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1387 1.59902174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1516 1.05772574 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
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## 1570 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 198 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
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## 1542 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1549 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1550 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
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## 1558 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 522 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
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## 609 1.14794174 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
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## 852 -1.55853825 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
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## 1544 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 679 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1494 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1529 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1530 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1531 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 29 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 140 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 417 -1.82918625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
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## 899 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
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## 1450 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1464 -0.02486626 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1487 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1497 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 1501 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 73 -1.19767425 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 408 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 738 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1016 -1.73897025 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
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## 1378 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1471 -1.91940225 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1472 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1473 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1478 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1486 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1488 -0.02486626 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 57 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 266 -0.02486626 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 404 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 429 -0.02486626 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 484 1.77945374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1055 0.24578174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1445 -1.82918625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1447 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1448 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1456 -0.38573026 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1457 0.78707774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1458 2.05010173 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1462 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1466 0.06534974 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1470 -0.74659426 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 17 1.50880574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 189 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 290 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 645 -0.20529826 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 680 -0.11508226 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1105 1.68923774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1116 -0.47594626 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1376 -0.38573026 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1391 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1402 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1425 1.86966973 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1430 -0.38573026 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1435 1.59902174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1436 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1437 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1440 1.41858974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1441 1.41858974 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1444 0.87729374 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1454 1.41858974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1087 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1097 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1233 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1250 0.78707774 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1256 -0.02486626 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1290 -2.55091425 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1369 0.69686174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1371 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1410 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1411 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1414 0.15556574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1416 0.96750974 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1421 -1.46832225 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1423 1.68923774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 34 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 94 1.50880574 -1.3440996 -1.8055867 2.5619444 1.8789510 -0.8193585
## 133 -2.55091425 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 734 1.68923774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1034 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1287 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1357 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1358 0.06534974 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1372 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1390 0.42621374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1395 0.60664574 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1396 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1397 -0.74659426 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1399 -1.55853825 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1400 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1404 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1406 -2.37048225 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 81 1.50880574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 210 -0.02486626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 471 1.68923774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1126 -0.02486626 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1129 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1148 1.32837374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1252 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1294 1.05772574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1322 -0.56616226 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1333 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1339 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1341 0.24578174 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1346 0.06534974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1352 -0.92702626 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1353 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1356 -1.91940225 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1382 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 969 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1261 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1303 1.05772574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1316 0.96750974 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 1328 1.41858974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1329 -2.37048225 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1332 0.60664574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1335 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1345 -0.38573026 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1350 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 190 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 262 0.69686174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 339 -0.38573026 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 563 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 882 -0.02486626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1042 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1071 -0.56616226 -1.3440996 0.5538366 2.5619444 -0.5322119 1.2204670
## 1076 0.87729374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1176 0.33599774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1218 1.77945374 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1277 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1304 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1305 0.60664574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1311 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1315 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1319 -0.02486626 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1327 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1336 0.96750974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 300 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1182 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1222 -1.10745826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1246 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1276 -2.82156225 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1296 0.24578174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1297 -0.20529826 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1301 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1309 1.05772574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1310 -0.11508226 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1314 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1320 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1321 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 61 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 152 1.32837374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 171 0.60664574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 380 0.60664574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 746 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 755 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 817 0.15556574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 970 -1.73897025 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1091 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1136 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1170 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1247 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1265 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1266 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1270 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1272 0.60664574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1273 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1283 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1285 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1289 -0.38573026 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1291 0.51642974 0.7439925 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1292 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 352 0.78707774 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 403 0.42621374 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 947 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 987 -2.73134625 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1178 1.68923774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1209 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1223 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1235 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1237 0.33599774 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1242 -0.20529826 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1243 -2.91177825 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1244 -0.56616226 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1245 1.41858974 -1.3440996 -1.8055867 2.5619444 1.8789510 1.2204670
## 1248 -0.29551426 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1254 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1255 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1258 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1260 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1271 -0.65637826 -1.3440996 -1.8055867 2.5619444 1.8789510 1.2204670
## 240 -0.92702626 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 536 -0.29551426 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 698 1.50880574 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 949 0.15556574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1001 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1124 -2.28026625 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1195 -1.55853825 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1198 -0.38573026 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1199 -0.92702626 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1205 -0.11508226 -1.3440996 0.5538366 2.5619444 -0.5322119 1.2204670
## 1208 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1215 0.42621374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1217 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1220 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1221 -0.47594626 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1224 -0.38573026 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1229 -0.83681026 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1230 -2.37048225 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1236 1.23815774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1239 1.32837374 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 285 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 433 0.15556574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 559 -1.55853825 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1197 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1202 -0.47594626 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1206 0.06534974 0.7439925 0.5538366 2.5619444 -0.5322119 1.2204670
## 151 0.60664574 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 335 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 463 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 480 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 518 0.42621374 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 624 1.23815774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 630 1.41858974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 725 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 769 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 851 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 929 -0.38573026 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1108 1.32837374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1144 0.24578174 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1157 0.06534974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1166 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1169 -0.38573026 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1181 -1.10745826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1189 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1196 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1201 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 139 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 215 0.24578174 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 469 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 586 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 587 -1.19767425 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 811 0.69686174 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 1054 0.51642974 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 1064 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1125 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1140 1.32837374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1161 -1.55853825 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1164 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1172 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1174 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 154 -0.02486626 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 168 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 319 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 890 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 953 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1051 0.69686174 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 1080 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1102 -1.64875425 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
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## 1119 -1.10745826 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
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## 1137 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1139 0.42621374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1142 0.15556574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1146 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1147 1.05772574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1151 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1153 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1154 -1.82918625 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
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## 54 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 239 0.69686174 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 252 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 577 -1.28789025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1039 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1104 0.69686174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1106 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1122 0.60664574 0.7439925 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1131 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
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## 293 1.32837374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 344 -0.38573026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 365 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 412 -0.11508226 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1005 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1059 -1.82918625 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1094 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1096 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1098 -0.02486626 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1099 -1.64875425 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1101 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1103 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1110 1.68923774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1114 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1211 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 33 0.60664574 0.7439925 -1.8055867 2.5619444 1.8789510 1.2204670
## 364 -0.11508226 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 590 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 958 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 988 0.42621374 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 1035 1.23815774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1053 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1061 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1083 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1089 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 351 -0.56616226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 389 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 834 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 896 -2.73134625 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 952 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 977 -1.73897025 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1020 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1037 -1.28789025 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1044 -0.02486626 0.7439925 -1.8055867 2.5619444 -0.5322119 1.2204670
## 1045 -1.73897025 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1048 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1050 -0.56616226 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 1056 -2.19005025 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1057 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1060 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 86 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 150 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 479 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 500 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 533 -0.65637826 -1.3440996 -1.8055867 2.5619444 1.8789510 1.2204670
## 622 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 944 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 948 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 982 -0.38573026 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 1024 -0.29551426 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 1027 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1028 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 423 -0.02486626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 499 0.15556574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 564 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 606 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 878 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 978 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 983 0.06534974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 991 -2.09983425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 998 0.87729374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1000 -0.38573026 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1007 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1009 0.51642974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 1015 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 1023 -1.37810625 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 435 -0.11508226 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 465 0.78707774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 467 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 763 -2.46069825 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 784 1.50880574 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 960 -2.55091425 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 966 -0.11508226 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 976 -1.46832225 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 986 0.15556574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 989 -1.55853825 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 992 -1.01724226 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 993 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 996 -1.46832225 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 1002 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 60 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 197 0.06534974 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 265 0.33599774 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 421 -1.10745826 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 550 0.78707774 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 593 0.15556574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 668 0.24578174 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 897 0.06534974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 955 0.69686174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 971 1.14794174 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 974 0.78707774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 138 0.15556574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 289 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 835 0.69686174 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 932 -1.19767425 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 943 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 946 1.23815774 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 950 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 951 0.33599774 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 462 0.78707774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 701 1.59902174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 924 -1.10745826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 928 0.15556574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 931 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 935 -1.55853825 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 936 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 212 -0.38573026 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 243 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 278 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 553 -0.65637826 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 901 -0.83681026 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 902 -0.29551426 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 908 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 910 -0.65637826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 911 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 912 -1.55853825 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 915 0.15556574 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 918 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 920 1.14794174 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 923 0.42621374 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 925 -0.02486626 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 1070 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 185 0.06534974 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 242 -1.28789025 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 284 -2.09983425 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 373 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 557 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 742 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 810 -1.46832225 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 830 1.32837374 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 887 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 889 -0.20529826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 891 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 892 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 905 0.24578174 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 907 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 16 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 51 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 79 1.05772574 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 225 0.69686174 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 267 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 314 0.51642974 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 526 0.69686174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 570 1.59902174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 667 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 716 -2.82156225 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 735 0.78707774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 770 1.23815774 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 847 -0.92702626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 869 0.15556574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 873 0.06534974 0.7439925 -1.8055867 -0.3903285 1.8789510 -0.8193585
## 879 -2.55091425 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 884 0.51642974 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 23 -1.46832225 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 104 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 137 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 502 1.41858974 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 785 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 807 -2.37048225 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 827 0.06534974 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 833 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 836 0.60664574 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 840 -1.01724226 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 842 -1.64875425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 848 -0.02486626 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 849 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 853 1.23815774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 855 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 857 0.06534974 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 860 -0.83681026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 866 -0.02486626 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 20 -0.20529826 -1.3440996 -1.8055867 2.5619444 -0.5322119 1.2204670
## 42 1.59902174 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 91 0.33599774 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 101 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 131 -0.56616226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 199 1.68923774 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 287 -0.47594626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 406 0.24578174 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 741 0.06534974 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 779 -0.56616226 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 813 0.78707774 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 831 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 832 -1.82918625 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 838 0.51642974 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 415 1.50880574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 794 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 821 -1.73897025 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 823 0.96750974 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 41 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 102 0.87729374 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 282 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 326 -0.56616226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 386 -0.11508226 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 419 -1.28789025 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 523 -0.38573026 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 541 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 760 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 761 -0.47594626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 772 -2.09983425 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 793 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 798 0.87729374 -1.3440996 -1.8055867 -0.3903285 -0.5322119 1.2204670
## 804 -0.47594626 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 36 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 177 1.50880574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 231 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 271 -0.74659426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 384 -2.55091425 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 452 -1.37810625 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 565 0.69686174 -1.3440996 -1.8055867 -0.3903285 1.8789510 1.2204670
## 662 -1.64875425 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 726 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 744 -0.11508226 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 748 -0.02486626 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 749 -1.10745826 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 751 -2.91177825 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 753 -2.55091425 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 756 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 759 -0.11508226 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 764 0.51642974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 766 -0.11508226 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 768 -1.64875425 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 113 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 124 0.78707774 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 142 -0.29551426 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 149 -3.00199425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 275 -1.01724226 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 298 -1.55853825 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 545 0.33599774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 618 -1.55853825 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 672 -0.47594626 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 727 -0.02486626 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 736 0.24578174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 9 -1.19767425 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 11 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 448 0.60664574 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 486 0.60664574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 657 -0.20529826 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 659 0.15556574 0.7439925 -1.8055867 2.5619444 1.8789510 -0.8193585
## 665 0.06534974 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 690 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 704 -1.55853825 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 714 0.15556574 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 719 -0.29551426 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 722 -1.01724226 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 729 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 85 -0.65637826 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 303 -0.74659426 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 508 -1.01724226 0.7439925 0.5538366 -0.3903285 1.8789510 1.2204670
## 702 1.50880574 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 705 0.06534974 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 708 0.33599774 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 709 -0.29551426 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 713 -1.19767425 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 13 -1.28789025 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 427 -1.37810625 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 444 -0.56616226 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 495 -0.83681026 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 504 0.42621374 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 543 -1.82918625 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 658 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 673 0.42621374 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 686 -0.38573026 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 196 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 221 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 497 -0.38573026 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 503 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 516 0.96750974 0.7439925 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 637 0.33599774 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 656 -0.92702626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 661 1.14794174 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 664 1.05772574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 671 0.06534974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 674 -0.74659426 0.7439925 0.5538366 -0.3903285 -0.5322119 1.2204670
## 678 -1.10745826 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 683 0.60664574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 206 0.24578174 0.7439925 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 256 0.06534974 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 453 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 456 -0.29551426 -1.3440996 -1.8055867 2.5619444 -0.5322119 -0.8193585
## 509 1.23815774 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 544 0.69686174 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 627 0.15556574 -1.3440996 0.5538366 -0.3903285 1.8789510 1.2204670
## 634 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 641 -2.28026625 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 642 -0.02486626 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## 643 -0.65637826 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 653 -0.02486626 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 654 -0.29551426 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 660 -0.56616226 0.7439925 0.5538366 -0.3903285 1.8789510 -0.8193585
## 4 1.14794174 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 121 0.24578174 0.7439925 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 338 -0.47594626 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 496 -1.82918625 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 580 -1.28789025 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 608 -0.47594626 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 615 -0.11508226 -1.3440996 0.5538366 -0.3903285 -0.5322119 -0.8193585
## 616 1.41858974 -1.3440996 0.5538366 -0.3903285 -0.5322119 1.2204670
## 623 0.51642974 -1.3440996 0.5538366 -0.3903285 1.8789510 -0.8193585
## 782 -1.10745826 -1.3440996 -1.8055867 -0.3903285 -0.5322119 -0.8193585
## aspirin vasodilator diuretic.loop diuretic.thiazide insulin
## 249 1.0690450 4.062019 -2.3135067 -0.378811 -0.3254965
## 318 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 631 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 883 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1095 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1405 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1554 1.0690450 4.062019 0.4322443 -0.378811 3.0722299
## 1589 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1708 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1773 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1850 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1935 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2077 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 110 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 116 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 328 -0.9354143 4.062019 0.4322443 2.639839 3.0722299
## 482 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 723 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 732 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 737 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 802 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 894 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1204 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1564 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1632 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1794 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2120 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 2127 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 3 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 188 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 320 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 539 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 632 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 712 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 808 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 1081 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1232 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1313 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1674 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1717 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1727 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2104 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2154 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2181 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2197 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 445 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 777 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 800 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 904 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 906 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 933 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1155 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1162 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1424 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1664 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2000 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2035 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 186 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 375 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 792 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 957 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1014 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1112 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1253 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1420 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1988 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 88 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 117 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 229 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 512 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 583 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 648 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1058 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 1203 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1225 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1415 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1438 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1442 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1824 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2178 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 200 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 460 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 490 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 505 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 636 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 877 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 885 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2214 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 157 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 170 -0.9354143 4.062019 0.4322443 -0.378811 3.0722299
## 317 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 399 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 489 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 552 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 652 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 876 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1573 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1993 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2040 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 10 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 238 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 360 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 416 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 733 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 967 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 968 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1134 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 1135 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1183 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1184 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1686 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2069 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2204 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 38 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 392 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 443 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 532 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 647 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 724 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1128 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1337 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 1593 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1777 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 8 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 222 1.0690450 -0.246183 0.4322443 2.639839 3.0722299
## 228 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 313 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 342 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 350 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 459 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 573 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 771 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 788 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 789 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 886 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1029 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1067 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1143 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1251 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1360 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1547 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1906 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2187 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2193 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2199 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 74 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 241 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 348 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 442 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 470 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 474 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 524 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 711 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1299 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1515 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1624 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1736 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 2044 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2067 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2183 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2185 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2189 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2190 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 2191 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2192 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 7 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 62 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 169 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 305 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 327 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 542 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 613 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 635 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 812 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 903 -0.9354143 4.062019 -2.3135067 2.639839 -0.3254965
## 1355 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1422 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1509 -0.9354143 4.062019 0.4322443 -0.378811 3.0722299
## 1601 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2175 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2177 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2184 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2186 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2188 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 174 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 253 1.0690450 -0.246183 0.4322443 2.639839 3.0722299
## 286 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 492 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 534 1.0690450 4.062019 -2.3135067 -0.378811 -0.3254965
## 607 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 614 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 663 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 964 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1066 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1433 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1524 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1555 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1675 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1907 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2021 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2113 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2129 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2170 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2172 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2173 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2198 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2200 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2201 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 46 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 103 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 220 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 236 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 514 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 638 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1520 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1722 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1877 -0.9354143 -0.246183 -2.3135067 -0.378811 3.0722299
## 2145 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2146 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 172 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 269 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 270 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 291 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 343 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 707 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 745 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 919 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1293 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1338 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1952 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1975 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2108 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2156 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 2164 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2171 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 2195 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 237 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 363 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 411 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 806 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1854 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1879 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2015 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 2018 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2056 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2081 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2132 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2158 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2159 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2163 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2165 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2168 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2169 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2196 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 161 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 180 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 367 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 685 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 990 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1086 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1100 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1969 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2135 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2153 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2155 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2166 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 223 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 681 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 963 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 1192 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1500 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1522 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1523 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1545 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 1560 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2019 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2095 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2121 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2147 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2151 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2157 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2231 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 112 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 176 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 219 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 382 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 684 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1268 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1617 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1786 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2143 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2148 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 2152 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 98 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 99 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 209 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 254 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 331 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 357 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 410 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1426 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1709 1.0690450 -0.246183 -2.3135067 -0.378811 3.0722299
## 1746 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1796 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2118 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2126 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2134 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2136 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2137 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2138 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 2141 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2142 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2144 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2220 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 64 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 261 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 478 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 485 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 604 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 828 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1392 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1463 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1647 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1881 -0.9354143 4.062019 0.4322443 2.639839 -0.3254965
## 2124 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2131 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2140 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2213 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 159 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 454 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 458 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 475 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 493 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 939 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1150 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1213 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1264 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1300 -0.9354143 4.062019 0.4322443 2.639839 3.0722299
## 1562 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1661 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1776 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2112 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 2115 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2122 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2123 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2125 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2128 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 45 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 179 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 306 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1307 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1872 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2082 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2117 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2119 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 304 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 334 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 513 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 598 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 909 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 1030 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1238 1.0690450 -0.246183 0.4322443 2.639839 3.0722299
## 1700 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2017 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 2087 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2106 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2109 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2110 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2111 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2114 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 40 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 95 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 234 1.0690450 4.062019 0.4322443 -0.378811 3.0722299
## 358 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 450 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 621 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 934 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 965 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1033 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1068 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1120 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1234 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2011 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2068 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2078 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2100 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2102 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2105 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 2203 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 193 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 387 -0.9354143 -0.246183 -2.3135067 -0.378811 3.0722299
## 731 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1262 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1342 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1465 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2048 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2093 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2099 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2101 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2103 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2205 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 72 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 299 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1043 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1049 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1240 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1561 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1723 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1992 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2005 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2088 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2094 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2097 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 84 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 273 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 370 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 418 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 473 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 646 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 670 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1381 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1512 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1738 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1928 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1973 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1976 -0.9354143 4.062019 0.4322443 -0.378811 3.0722299
## 2024 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2049 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2070 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2079 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2080 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 2083 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2085 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2086 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2090 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 2091 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 2092 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 75 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 106 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 413 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 432 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 531 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 839 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1062 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1163 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1177 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1855 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2033 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2071 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2073 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2076 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2084 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 78 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 264 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 315 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 439 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 529 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 758 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1092 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1130 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1412 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1614 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1802 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1970 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1983 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2012 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 2053 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2057 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2058 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2059 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2074 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2075 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2215 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 37 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 65 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 100 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 108 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 173 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 207 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 824 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1141 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1145 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1618 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1812 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1856 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1937 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2047 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2061 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2062 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2063 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 2221 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 295 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 337 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 372 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 546 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 874 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 972 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1210 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1298 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1498 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1979 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2066 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 383 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 434 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 843 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 865 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 922 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1418 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1485 -0.9354143 -0.246183 -2.3135067 2.639839 -0.3254965
## 1842 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2037 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2052 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 371 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 376 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 377 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 506 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 728 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 826 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1642 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1707 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1751 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1864 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1895 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 1998 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 31 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 35 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 90 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 312 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 457 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 466 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 870 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 913 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1013 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1117 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1306 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1619 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1756 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2026 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2028 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2038 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2039 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2043 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2227 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 162 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 528 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 581 1.0690450 -0.246183 -2.3135067 2.639839 3.0722299
## 600 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 601 1.0690450 -0.246183 0.4322443 2.639839 3.0722299
## 814 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1318 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1477 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1982 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2029 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2034 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2041 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2042 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2224 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2229 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 612 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 703 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 916 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 994 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1188 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1216 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1890 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 1991 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2031 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2032 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 97 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 201 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 341 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 717 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1364 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1439 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1474 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1489 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1669 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1687 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 1704 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1706 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1780 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1984 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2022 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2025 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2217 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2222 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 135 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 166 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 468 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 626 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1031 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1362 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1510 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1625 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1696 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1699 -0.9354143 4.062019 0.4322443 -0.378811 3.0722299
## 1705 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1715 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1861 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1941 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1964 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 2218 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2228 1.0690450 4.062019 0.4322443 -0.378811 3.0722299
## 2230 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 255 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 819 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 917 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1085 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 1187 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1394 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1479 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1517 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 2009 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2223 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 2225 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2226 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 27 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 105 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 147 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 224 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 274 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 356 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 431 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 551 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 962 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 997 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1389 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1831 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1996 1.0690450 -0.246183 -2.3135067 -0.378811 3.0722299
## 1999 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2008 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2216 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 107 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 148 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 182 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 414 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 511 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 699 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1017 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1366 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1672 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 1931 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 1966 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1994 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 2002 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 2007 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 2 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 143 -0.9354143 4.062019 0.4322443 2.639839 -0.3254965
## 160 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 688 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 776 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 850 -0.9354143 4.062019 0.4322443 2.639839 -0.3254965
## 1194 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1849 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 5 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 55 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 347 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 856 1.0690450 -0.246183 0.4322443 2.639839 3.0722299
## 1972 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1977 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1980 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1981 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1985 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1987 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1995 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 2207 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 244 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 308 -0.9354143 -0.246183 -2.3135067 2.639839 -0.3254965
## 560 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 561 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1173 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1190 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1269 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1453 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1543 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1681 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1800 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1873 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1971 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1974 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 130 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 721 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 837 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 1741 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1860 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1957 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1960 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1965 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1968 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 58 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 195 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1228 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1257 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1592 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1596 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1750 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1837 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1942 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1961 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1962 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1963 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 422 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1401 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 1525 -0.9354143 -0.246183 -2.3135067 2.639839 -0.3254965
## 1646 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1929 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1940 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1945 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 1946 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1948 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1949 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1950 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1953 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1955 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 202 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 494 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 740 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 780 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1069 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1537 -0.9354143 4.062019 -2.3135067 -0.378811 -0.3254965
## 1920 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1926 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1938 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1939 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1947 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1951 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 25 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 134 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 323 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 354 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 381 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1568 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1766 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1782 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1903 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1923 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 1925 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1930 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1932 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1934 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 385 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 548 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1078 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1429 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1581 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1609 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1905 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1908 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1916 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1918 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1919 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1921 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1927 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1933 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 77 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 184 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 498 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 555 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 574 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 783 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 956 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 1584 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1797 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1863 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1874 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1882 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1898 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1911 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1913 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1914 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1915 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 115 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 259 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 321 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 825 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1370 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1621 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 1643 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1688 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1865 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1880 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1886 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1889 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1892 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1897 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1899 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1900 -0.9354143 4.062019 0.4322443 2.639839 -0.3254965
## 1904 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1909 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 83 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 208 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 329 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 449 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 451 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 605 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 754 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 822 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 844 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1074 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1185 1.0690450 4.062019 0.4322443 -0.378811 -0.3254965
## 1829 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1843 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1888 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1893 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1894 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 30 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 211 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 666 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 895 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 1165 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1452 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1526 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1698 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1862 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1883 1.0690450 4.062019 0.4322443 2.639839 -0.3254965
## 1884 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1885 1.0690450 -0.246183 0.4322443 2.639839 -0.3254965
## 1887 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 96 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 311 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 437 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 599 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 619 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 778 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1008 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1082 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1732 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1784 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1820 1.0690450 -0.246183 0.4322443 2.639839 3.0722299
## 1834 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1869 1.0690450 4.062019 0.4322443 2.639839 3.0722299
## 1870 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 1871 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1875 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1876 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 82 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 258 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 310 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 345 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 464 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 595 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 676 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 841 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1434 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1605 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1616 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1769 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1833 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1846 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 1858 1.0690450 -0.246183 -2.3135067 2.639839 -0.3254965
## 1859 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 1866 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 50 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 52 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 53 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 250 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 349 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
## 396 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 868 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 979 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1191 1.0690450 -0.246183 -2.3135067 -0.378811 -0.3254965
## 1409 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 1841 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
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## 863 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
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## 1283 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
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## 725 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 769 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 851 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 929 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
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## 469 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 890 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 1023 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
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## 889 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
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## 51 -0.9354143 -0.246183 -2.3135067 2.639839 -0.3254965
## 79 -0.9354143 -0.246183 -2.3135067 2.639839 -0.3254965
## 225 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
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## 879 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 848 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 849 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
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## 855 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 91 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
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## 199 -0.9354143 -0.246183 -2.3135067 2.639839 -0.3254965
## 287 -0.9354143 4.062019 0.4322443 -0.378811 3.0722299
## 406 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 741 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 779 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 813 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 831 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 832 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 838 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 415 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 794 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 821 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
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## 41 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 102 1.0690450 -0.246183 0.4322443 -0.378811 3.0722299
## 282 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 326 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 386 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 523 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 541 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 760 -0.9354143 -0.246183 0.4322443 2.639839 -0.3254965
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## 772 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 793 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 798 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 804 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 36 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
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## 231 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 271 -0.9354143 -0.246183 0.4322443 2.639839 3.0722299
## 384 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 452 -0.9354143 -0.246183 -2.3135067 2.639839 -0.3254965
## 565 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 662 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 749 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 751 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 753 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 756 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 759 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 764 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 766 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 768 1.0690450 -0.246183 -2.3135067 -0.378811 3.0722299
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## 657 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 659 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
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## 303 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 508 -0.9354143 4.062019 0.4322443 -0.378811 -0.3254965
## 702 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 705 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 708 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
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## 504 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 543 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 658 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 503 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 637 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 656 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 661 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 664 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 671 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 674 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 678 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 683 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 206 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 256 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
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## 456 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 509 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 544 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 627 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 634 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 641 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 642 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 643 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## 653 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 654 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 660 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 4 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 121 1.0690450 -0.246183 0.4322443 2.639839 3.0722299
## 338 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 496 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 580 -0.9354143 -0.246183 0.4322443 -0.378811 3.0722299
## 608 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 615 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 616 -0.9354143 -0.246183 0.4322443 -0.378811 -0.3254965
## 623 1.0690450 -0.246183 0.4322443 -0.378811 -0.3254965
## 782 -0.9354143 -0.246183 -2.3135067 -0.378811 -0.3254965
## surgery.pacemaker surgery.cabg resting.systolic.bp resting.hr lvef.metabl
## 249 1.8728523 -0.6125297 -0.37910858 -0.64933938 -1.38824471
## 318 -0.5339449 1.6325740 -0.04424236 -1.41083843 -0.01847084
## 631 1.8728523 -0.6125297 0.51386800 -0.23397626 0.66641609
## 883 -0.5339449 -0.6125297 1.63008872 1.15056748 -0.01847084
## 1095 1.8728523 1.6325740 -1.71857344 -0.51088500 -1.38824471
## 1405 -0.5339449 -0.6125297 0.17900178 0.94288592 -0.70335777
## 1554 -0.5339449 -0.6125297 -0.49073065 1.77361216 -0.70335777
## 1589 -0.5339449 -0.6125297 0.17900178 1.28902185 0.66641609
## 1708 -0.5339449 1.6325740 -0.04424236 1.98129372 -0.70335777
## 1773 1.8728523 -0.6125297 -0.04424236 0.11215968 -1.38824471
## 1850 1.8728523 -0.6125297 1.63008872 -0.44165782 -0.70335777
## 1935 -0.5339449 -0.6125297 0.17900178 0.80443154 -0.01847084
## 2077 -0.5339449 -0.6125297 0.06737971 -0.85702094 2.72107688
## 110 1.8728523 1.6325740 -1.27208515 0.31984124 -1.38824471
## 116 -0.5339449 -0.6125297 -1.38370722 2.32742965 -0.01847084
## 328 -0.5339449 -0.6125297 -1.04884101 -0.23397626 -1.66219948
## 482 -0.5339449 -0.6125297 -0.15586443 -0.51088500 -1.79917686
## 723 -0.5339449 -0.6125297 -1.16046308 0.66597717 -0.01847084
## 732 -0.5339449 -0.6125297 -0.15586443 -0.37243063 -1.38824471
## 737 -0.5339449 1.6325740 -0.04424236 -0.85702094 -1.38824471
## 802 -0.5339449 -0.6125297 -0.60235272 -1.34161124 -0.01847084
## 894 -0.5339449 1.6325740 0.84873421 0.04293249 -0.01847084
## 1204 1.8728523 1.6325740 1.63008872 0.18138686 -0.01847084
## 1564 1.8728523 1.6325740 -0.04424236 -0.51088500 0.66641609
## 1632 -0.5339449 -0.6125297 -1.16046308 -0.30320344 -0.01847084
## 1794 1.8728523 1.6325740 -0.37910858 0.80443154 1.35130302
## 2120 1.8728523 -0.6125297 -1.16046308 -0.44165782 1.35130302
## 2127 -0.5339449 -0.6125297 -0.60235272 1.35824904 0.39246131
## 3 -0.5339449 -0.6125297 -0.15586443 1.91206653 2.72107688
## 188 -0.5339449 -0.6125297 1.63008872 0.11215968 0.66641609
## 320 -0.5339449 -0.6125297 0.06737971 -0.02629470 -0.70335777
## 539 -0.5339449 -0.6125297 0.06737971 -0.37243063 -0.70335777
## 632 -0.5339449 -0.6125297 -1.60695137 1.77361216 -0.01847084
## 712 -0.5339449 -0.6125297 -0.60235272 0.80443154 -1.38824471
## 808 -0.5339449 1.6325740 -0.15586443 1.70438497 -0.01847084
## 1081 -0.5339449 -0.6125297 1.63008872 1.01211310 -0.01847084
## 1232 1.8728523 -0.6125297 -1.71857344 -0.30320344 -0.01847084
## 1313 -0.5339449 1.6325740 0.51386800 0.11215968 -0.84033516
## 1674 1.8728523 1.6325740 -0.26748651 1.08134029 -1.38824471
## 1717 1.8728523 1.6325740 1.63008872 -0.71856656 -1.38824471
## 1727 1.8728523 1.6325740 -1.38370722 -0.71856656 -0.70335777
## 2104 1.8728523 1.6325740 -0.04424236 -0.92624812 -0.01847084
## 2154 -0.5339449 -0.6125297 -1.04884101 -0.44165782 1.35130302
## 2181 -0.5339449 1.6325740 -1.04884101 -1.13392968 0.66641609
## 2197 -0.5339449 -0.6125297 -1.38370722 1.28902185 -0.01847084
## 445 -0.5339449 -0.6125297 -0.49073065 -1.34161124 -0.01847084
## 777 -0.5339449 1.6325740 -0.71397479 0.52752280 -0.42940300
## 800 -0.5339449 -0.6125297 -0.15586443 0.04293249 2.72107688
## 904 -0.5339449 -0.6125297 2.63468736 -0.51088500 -0.70335777
## 906 -0.5339449 1.6325740 -0.82559686 -0.99547531 0.52943870
## 933 1.8728523 1.6325740 0.06737971 -0.44165782 -0.01847084
## 1155 -0.5339449 1.6325740 2.18819907 -0.51088500 2.03618995
## 1162 -0.5339449 -0.6125297 -0.04424236 0.25061405 -1.38824471
## 1424 1.8728523 1.6325740 -0.71397479 0.73520436 -0.01847084
## 1664 -0.5339449 1.6325740 1.63008872 -0.09552188 -1.38824471
## 2000 -0.5339449 1.6325740 -0.04424236 0.11215968 -0.01847084
## 2035 -0.5339449 1.6325740 -1.49532930 0.04293249 -0.01847084
## 186 -0.5339449 1.6325740 2.18819907 -1.06470250 -0.70335777
## 375 -0.5339449 1.6325740 -1.27208515 -0.51088500 -0.01847084
## 792 -0.5339449 -0.6125297 -0.93721894 2.18897528 -0.01847084
## 957 1.8728523 1.6325740 0.06737971 0.25061405 -0.01847084
## 1014 -0.5339449 1.6325740 -0.49073065 -0.23397626 -0.70335777
## 1112 -0.5339449 1.6325740 -0.60235272 0.04293249 -1.66219948
## 1253 -0.5339449 1.6325740 -0.04424236 1.08134029 -0.70335777
## 1420 -0.5339449 1.6325740 0.62549007 0.80443154 1.35130302
## 1988 -0.5339449 1.6325740 -0.04424236 -0.02629470 -1.38824471
## 88 -0.5339449 -0.6125297 0.17900178 -0.78779375 -2.07313164
## 117 1.8728523 -0.6125297 -0.93721894 0.04293249 -1.38824471
## 229 -0.5339449 -0.6125297 0.51386800 1.42747622 -1.38824471
## 512 -0.5339449 -0.6125297 1.18360043 2.05052090 0.66641609
## 583 1.8728523 -0.6125297 -0.49073065 -1.13392968 0.25548393
## 648 -0.5339449 -0.6125297 -0.60235272 -0.71856656 -0.70335777
## 1058 -0.5339449 -0.6125297 0.17900178 0.59674998 -0.70335777
## 1203 -0.5339449 -0.6125297 0.51386800 3.01970152 -1.38824471
## 1225 -0.5339449 -0.6125297 -1.49532930 0.94288592 -0.70335777
## 1415 1.8728523 1.6325740 -1.27208515 -1.20315687 -0.70335777
## 1438 -0.5339449 1.6325740 1.29522250 -1.06470250 1.35130302
## 1442 1.8728523 -0.6125297 -1.27208515 0.04293249 -0.70335777
## 1824 1.8728523 -0.6125297 -1.16046308 0.18138686 -0.01847084
## 2178 -0.5339449 1.6325740 -1.04884101 1.08134029 -0.70335777
## 200 1.8728523 -0.6125297 0.40224593 0.66597717 -0.70335777
## 460 1.8728523 -0.6125297 -1.16046308 1.35824904 -0.01847084
## 490 -0.5339449 -0.6125297 -0.93721894 -1.06470250 2.03618995
## 505 -0.5339449 -0.6125297 0.06737971 -1.13392968 -0.01847084
## 636 -0.5339449 -0.6125297 0.06737971 -0.85702094 -0.56638039
## 877 1.8728523 1.6325740 1.07197836 0.66597717 -1.38824471
## 885 -0.5339449 -0.6125297 -0.26748651 1.08134029 -0.84033516
## 2214 -0.5339449 -0.6125297 -0.37910858 0.04293249 -1.38824471
## 157 1.8728523 -0.6125297 -1.04884101 -0.16474907 -1.38824471
## 170 -0.5339449 -0.6125297 0.17900178 -0.30320344 -0.01847084
## 317 -0.5339449 1.6325740 -1.71857344 -0.71856656 0.39246131
## 399 -0.5339449 1.6325740 -1.49532930 -0.30320344 -0.01847084
## 489 -0.5339449 1.6325740 0.96035628 -2.03388311 0.66641609
## 552 -0.5339449 -0.6125297 -0.93721894 1.35824904 -1.38824471
## 652 -0.5339449 -0.6125297 -1.71857344 -0.92624812 -1.38824471
## 876 -0.5339449 -0.6125297 -1.16046308 1.63515778 -1.52522209
## 1573 -0.5339449 -0.6125297 -0.71397479 -0.92624812 -0.01847084
## 1993 1.8728523 -0.6125297 -0.93721894 0.11215968 -0.01847084
## 2040 -0.5339449 -0.6125297 -0.49073065 1.08134029 2.03618995
## 10 1.8728523 -0.6125297 -0.60235272 0.52752280 0.66641609
## 238 -0.5339449 -0.6125297 0.40224593 -0.09552188 -0.01847084
## 360 -0.5339449 -0.6125297 -1.60695137 -0.09552188 -0.70335777
## 416 -0.5339449 -0.6125297 -0.37910858 0.11215968 -0.56638039
## 733 -0.5339449 -0.6125297 -0.71397479 1.08134029 -1.38824471
## 967 -0.5339449 1.6325740 -0.82559686 -0.16474907 0.52943870
## 968 1.8728523 -0.6125297 -0.04424236 1.21979466 -0.70335777
## 1134 -0.5339449 -0.6125297 -0.60235272 1.63515778 -0.70335777
## 1135 -0.5339449 1.6325740 0.40224593 -1.13392968 -0.70335777
## 1183 -0.5339449 -0.6125297 -0.60235272 1.42747622 -1.93615425
## 1184 -0.5339449 -0.6125297 -1.16046308 2.11974809 -1.66219948
## 1686 -0.5339449 1.6325740 1.07197836 0.80443154 -0.01847084
## 2069 -0.5339449 1.6325740 0.17900178 -1.13392968 -0.70335777
## 2204 -0.5339449 -0.6125297 -1.16046308 2.25820246 -0.70335777
## 38 -0.5339449 -0.6125297 1.07197836 2.67356558 -0.70335777
## 392 1.8728523 -0.6125297 -1.04884101 -0.78779375 0.66641609
## 443 1.8728523 -0.6125297 1.63008872 -0.37243063 -1.66219948
## 532 -0.5339449 -0.6125297 -1.27208515 0.80443154 -0.70335777
## 647 1.8728523 1.6325740 -0.04424236 -0.09552188 -0.01847084
## 724 -0.5339449 -0.6125297 0.51386800 0.45829561 -0.97731255
## 1128 -0.5339449 1.6325740 -1.04884101 0.11215968 0.66641609
## 1337 -0.5339449 -0.6125297 0.84873421 0.59674998 0.66641609
## 1593 -0.5339449 1.6325740 -0.49073065 -1.48006562 2.31014472
## 1777 1.8728523 1.6325740 -0.60235272 -1.13392968 -0.01847084
## 8 -0.5339449 -0.6125297 -0.37910858 -0.30320344 -0.70335777
## 222 -0.5339449 1.6325740 0.17900178 -0.37243063 -0.70335777
## 228 -0.5339449 -0.6125297 0.51386800 -1.27238406 -1.38824471
## 313 1.8728523 -0.6125297 1.18360043 -0.78779375 -1.38824471
## 342 -0.5339449 1.6325740 0.29062385 -0.58011219 -0.01847084
## 350 -0.5339449 1.6325740 0.51386800 -1.13392968 -0.01847084
## 459 -0.5339449 -0.6125297 -0.15586443 -0.09552188 -0.01847084
## 573 -0.5339449 -0.6125297 -0.60235272 -0.30320344 -1.38824471
## 771 -0.5339449 -0.6125297 -0.49073065 1.08134029 -0.70335777
## 788 -0.5339449 1.6325740 -0.37910858 1.15056748 0.66641609
## 789 -0.5339449 -0.6125297 -0.04424236 1.49670341 -0.70335777
## 886 -0.5339449 -0.6125297 -0.93721894 -0.02629470 -0.01847084
## 1029 1.8728523 1.6325740 -1.16046308 0.66597717 -0.70335777
## 1067 1.8728523 -0.6125297 1.63008872 0.73520436 -1.38824471
## 1143 -0.5339449 -0.6125297 -0.82559686 -0.30320344 1.35130302
## 1251 -0.5339449 -0.6125297 -1.38370722 0.66597717 -0.01847084
## 1360 1.8728523 1.6325740 -1.49532930 1.01211310 -0.56638039
## 1547 -0.5339449 -0.6125297 -1.04884101 0.87365873 -0.01847084
## 1906 -0.5339449 -0.6125297 0.51386800 0.45829561 -0.01847084
## 2187 -0.5339449 1.6325740 1.18360043 -0.23397626 -0.70335777
## 2193 1.8728523 -0.6125297 -1.60695137 3.01970152 0.66641609
## 2199 1.8728523 -0.6125297 -0.37910858 -0.37243063 -0.29242562
## 74 1.8728523 -0.6125297 -0.15586443 0.94288592 -1.38824471
## 241 -0.5339449 -0.6125297 1.07197836 -1.20315687 -1.38824471
## 348 1.8728523 -0.6125297 0.17900178 -0.16474907 1.35130302
## 442 -0.5339449 1.6325740 -0.60235272 -1.34161124 -0.70335777
## 470 -0.5339449 -0.6125297 2.07657700 0.11215968 -0.70335777
## 474 -0.5339449 -0.6125297 0.17900178 -0.64933938 -0.01847084
## 524 -0.5339449 1.6325740 -1.71857344 -0.23397626 0.66641609
## 711 1.8728523 1.6325740 -0.93721894 0.66597717 0.52943870
## 1299 -0.5339449 -0.6125297 0.62549007 0.04293249 0.66641609
## 1515 1.8728523 -0.6125297 -0.04424236 -1.13392968 -0.01847084
## 1624 1.8728523 -0.6125297 -0.71397479 -1.41083843 -0.84033516
## 1736 -0.5339449 -0.6125297 -0.82559686 0.52752280 -0.70335777
## 2044 1.8728523 -0.6125297 4.08577430 -0.64933938 -0.70335777
## 2067 -0.5339449 1.6325740 1.51846664 -1.06470250 -0.01847084
## 2183 1.8728523 1.6325740 -0.04424236 -0.64933938 1.35130302
## 2185 -0.5339449 1.6325740 -0.04424236 -0.44165782 -0.01847084
## 2189 1.8728523 -0.6125297 -0.60235272 -0.44165782 -0.70335777
## 2190 1.8728523 -0.6125297 -0.60235272 -0.78779375 1.35130302
## 2191 -0.5339449 -0.6125297 -0.15586443 -0.44165782 -0.01847084
## 2192 -0.5339449 -0.6125297 -1.49532930 -0.09552188 -0.01847084
## 7 -0.5339449 -0.6125297 -0.60235272 0.18138686 -1.38824471
## 62 -0.5339449 1.6325740 -1.27208515 0.18138686 -0.01847084
## 169 1.8728523 1.6325740 2.29982115 -0.37243063 0.66641609
## 305 -0.5339449 -0.6125297 -1.16046308 -1.61851999 -0.01847084
## 327 -0.5339449 1.6325740 -1.71857344 -0.37243063 -0.70335777
## 542 -0.5339449 -0.6125297 -0.15586443 0.04293249 0.66641609
## 613 -0.5339449 -0.6125297 -0.60235272 -0.02629470 1.62525779
## 635 -0.5339449 -0.6125297 0.62549007 -0.99547531 1.62525779
## 812 1.8728523 -0.6125297 -1.16046308 0.38906842 -1.66219948
## 903 1.8728523 1.6325740 -0.93721894 -0.09552188 1.76223518
## 1355 -0.5339449 -0.6125297 2.85793151 -0.02629470 0.66641609
## 1422 -0.5339449 1.6325740 2.63468736 -0.16474907 -0.70335777
## 1509 -0.5339449 -0.6125297 1.96495493 1.15056748 0.66641609
## 1601 1.8728523 -0.6125297 0.84873421 -0.09552188 -0.01847084
## 2175 1.8728523 -0.6125297 0.96035628 0.59674998 0.80339347
## 2177 -0.5339449 -0.6125297 0.62549007 -0.02629470 2.03618995
## 2184 -0.5339449 1.6325740 -0.49073065 0.94288592 -0.70335777
## 2186 -0.5339449 1.6325740 -0.15586443 0.66597717 -0.01847084
## 2188 -0.5339449 1.6325740 0.73711214 -1.75697436 2.03618995
## 174 -0.5339449 1.6325740 0.51386800 0.18138686 -0.01847084
## 253 -0.5339449 -0.6125297 1.63008872 -0.51088500 -0.70335777
## 286 -0.5339449 1.6325740 -0.49073065 -0.58011219 -1.38824471
## 492 -0.5339449 -0.6125297 -0.60235272 0.87365873 1.35130302
## 534 -0.5339449 1.6325740 1.63008872 1.49670341 -0.84033516
## 607 -0.5339449 -0.6125297 1.63008872 0.18138686 0.66641609
## 614 -0.5339449 1.6325740 0.06737971 -0.51088500 -0.56638039
## 663 -0.5339449 1.6325740 0.06737971 -0.23397626 0.66641609
## 964 -0.5339449 1.6325740 -1.16046308 0.66597717 0.66641609
## 1066 1.8728523 1.6325740 0.51386800 -0.64933938 0.66641609
## 1433 -0.5339449 -0.6125297 0.40224593 -0.02629470 -0.01847084
## 1524 -0.5339449 -0.6125297 -1.16046308 1.15056748 -0.70335777
## 1555 -0.5339449 1.6325740 -0.60235272 -0.85702094 -1.38824471
## 1675 1.8728523 1.6325740 -0.71397479 -0.30320344 -0.70335777
## 1907 1.8728523 -0.6125297 -0.26748651 0.66597717 -0.01847084
## 2021 -0.5339449 -0.6125297 -0.04424236 0.18138686 -0.70335777
## 2113 -0.5339449 1.6325740 0.06737971 0.73520436 -0.01847084
## 2129 -0.5339449 -0.6125297 0.06737971 -0.23397626 -0.01847084
## 2170 -0.5339449 -0.6125297 -1.49532930 -0.30320344 -0.01847084
## 2172 -0.5339449 1.6325740 1.96495493 -0.09552188 -0.01847084
## 2173 1.8728523 -0.6125297 2.18819907 -0.92624812 2.72107688
## 2198 -0.5339449 1.6325740 -0.60235272 0.04293249 -0.01847084
## 2200 -0.5339449 -0.6125297 -0.49073065 0.80443154 -0.01847084
## 2201 -0.5339449 -0.6125297 -0.71397479 -0.64933938 -0.70335777
## 1 -0.5339449 -0.6125297 -1.16046308 -0.16474907 -0.70335777
## 46 -0.5339449 1.6325740 1.40684457 -0.02629470 0.66641609
## 103 -0.5339449 1.6325740 -1.04884101 -0.37243063 -0.01847084
## 220 -0.5339449 -0.6125297 1.74171079 1.77361216 -0.01847084
## 236 -0.5339449 -0.6125297 -1.60695137 0.59674998 1.35130302
## 514 -0.5339449 -0.6125297 -1.16046308 -0.02629470 -1.66219948
## 638 -0.5339449 1.6325740 -1.16046308 -0.44165782 -0.70335777
## 1520 1.8728523 1.6325740 -0.93721894 0.25061405 2.72107688
## 1722 -0.5339449 -0.6125297 -1.04884101 2.11974809 -1.38824471
## 1877 -0.5339449 -0.6125297 -1.16046308 -0.16474907 -0.70335777
## 2145 1.8728523 -0.6125297 1.18360043 -0.64933938 -0.70335777
## 2146 1.8728523 -0.6125297 -0.04424236 0.38906842 -0.70335777
## 172 -0.5339449 1.6325740 0.17900178 -0.58011219 -0.01847084
## 269 -0.5339449 1.6325740 2.18819907 -0.51088500 2.03618995
## 270 -0.5339449 1.6325740 0.73711214 -1.20315687 -1.11428993
## 291 1.8728523 1.6325740 -0.60235272 -0.44165782 -0.70335777
## 343 1.8728523 -0.6125297 -0.60235272 -0.16474907 -1.38824471
## 707 -0.5339449 -0.6125297 1.63008872 -0.78779375 -0.70335777
## 745 -0.5339449 -0.6125297 -1.10465204 -0.16474907 -0.01847084
## 919 -0.5339449 -0.6125297 -0.15586443 0.73520436 -0.70335777
## 1293 1.8728523 -0.6125297 -0.04424236 -0.44165782 -0.70335777
## 1338 -0.5339449 -0.6125297 0.40224593 -0.23397626 -0.01847084
## 1952 -0.5339449 1.6325740 -0.60235272 -2.24156467 1.35130302
## 1975 1.8728523 -0.6125297 -0.15586443 -1.13392968 1.35130302
## 2108 1.8728523 -0.6125297 -0.15586443 0.45829561 -0.01847084
## 2156 -0.5339449 -0.6125297 -0.04424236 -0.37243063 0.66641609
## 2164 -0.5339449 -0.6125297 -0.26748651 -0.64933938 0.66641609
## 2171 -0.5339449 1.6325740 -0.71397479 -0.37243063 -0.84033516
## 2195 -0.5339449 1.6325740 1.63008872 -0.51088500 -0.01847084
## 237 -0.5339449 -0.6125297 0.06737971 -1.06470250 0.66641609
## 363 -0.5339449 1.6325740 -0.37910858 -0.23397626 -0.70335777
## 411 -0.5339449 -0.6125297 -0.60235272 0.31984124 -0.01847084
## 806 -0.5339449 -0.6125297 -0.49073065 -1.41083843 -0.70335777
## 1854 -0.5339449 -0.6125297 0.40224593 -0.99547531 -1.38824471
## 1879 1.8728523 -0.6125297 0.73711214 -0.44165782 -0.01847084
## 2015 1.8728523 1.6325740 0.51386800 0.45829561 -0.70335777
## 2018 1.8728523 -0.6125297 -1.71857344 0.11215968 -0.29242562
## 2056 -0.5339449 -0.6125297 0.96035628 0.04293249 2.72107688
## 2081 1.8728523 -0.6125297 -0.60235272 0.59674998 -1.38824471
## 2132 -0.5339449 1.6325740 0.17900178 -0.23397626 -1.11428993
## 2158 -0.5339449 1.6325740 1.07197836 -0.44165782 2.72107688
## 2159 -0.5339449 -0.6125297 -1.04884101 0.11215968 -1.38824471
## 2163 -0.5339449 -0.6125297 0.17900178 1.77361216 -0.01847084
## 2165 1.8728523 -0.6125297 -0.37910858 0.59674998 2.03618995
## 2168 1.8728523 -0.6125297 -0.60235272 0.80443154 2.03618995
## 2169 -0.5339449 -0.6125297 0.40224593 -0.37243063 -0.01847084
## 2196 -0.5339449 1.6325740 -0.26748651 0.73520436 -0.01847084
## 161 -0.5339449 -0.6125297 -1.27208515 -0.51088500 -1.38824471
## 180 -0.5339449 1.6325740 0.06737971 1.35824904 -1.38824471
## 367 -0.5339449 -0.6125297 -0.49073065 1.84283934 -0.01847084
## 685 1.8728523 -0.6125297 -1.71857344 -0.44165782 0.66641609
## 990 -0.5339449 -0.6125297 -0.60235272 -0.64933938 -0.01847084
## 1086 -0.5339449 1.6325740 -1.04884101 -0.71856656 -0.01847084
## 1100 -0.5339449 -0.6125297 -1.16046308 0.45829561 -0.70335777
## 1969 -0.5339449 -0.6125297 -0.71397479 0.11215968 -0.01847084
## 2135 -0.5339449 -0.6125297 1.07197836 -0.37243063 -1.38824471
## 2153 -0.5339449 1.6325740 -0.04424236 -0.85702094 0.66641609
## 2155 -0.5339449 -0.6125297 -0.49073065 -0.09552188 0.66641609
## 2166 1.8728523 -0.6125297 -1.04884101 1.21979466 -0.01847084
## 223 -0.5339449 1.6325740 -1.04884101 -0.92624812 -0.01847084
## 681 -0.5339449 1.6325740 -1.27208515 1.35824904 -1.25126732
## 963 1.8728523 1.6325740 -0.04424236 -0.51088500 2.03618995
## 1192 -0.5339449 -0.6125297 -0.71397479 -0.30320344 -1.11428993
## 1500 -0.5339449 1.6325740 1.07197836 -1.48006562 0.66641609
## 1522 -0.5339449 1.6325740 2.18819907 1.49670341 2.72107688
## 1523 -0.5339449 -0.6125297 -0.60235272 2.60433840 -1.38824471
## 1545 -0.5339449 -0.6125297 -0.49073065 -1.27238406 -1.38824471
## 1560 -0.5339449 -0.6125297 -0.37910858 1.15056748 -0.70335777
## 2019 1.8728523 -0.6125297 -0.04424236 -0.51088500 -0.01847084
## 2095 1.8728523 -0.6125297 0.62549007 -0.51088500 -1.38824471
## 2121 -0.5339449 -0.6125297 0.17900178 1.01211310 2.72107688
## 2147 1.8728523 -0.6125297 -0.04424236 -0.09552188 0.66641609
## 2151 -0.5339449 -0.6125297 0.29062385 0.45829561 2.72107688
## 2157 -0.5339449 -0.6125297 0.96035628 0.25061405 -0.70335777
## 2231 -0.5339449 -0.6125297 1.40684457 -0.78779375 -0.01847084
## 112 1.8728523 -0.6125297 -0.49073065 1.15056748 1.35130302
## 176 1.8728523 1.6325740 -0.82559686 -0.85702094 0.25548393
## 219 -0.5339449 1.6325740 1.29522250 -0.30320344 -0.70335777
## 382 1.8728523 1.6325740 -1.71857344 -0.37243063 -0.70335777
## 684 -0.5339449 -0.6125297 -0.71397479 0.52752280 1.35130302
## 1268 -0.5339449 -0.6125297 -0.15586443 -0.99547531 0.66641609
## 1617 1.8728523 -0.6125297 -0.04424236 -0.71856656 -0.01847084
## 1786 -0.5339449 1.6325740 1.18360043 -0.09552188 -0.01847084
## 2143 -0.5339449 -0.6125297 -0.71397479 -0.64933938 -0.01847084
## 2148 -0.5339449 -0.6125297 -0.93721894 -0.09552188 -1.38824471
## 2152 -0.5339449 -0.6125297 -0.15586443 1.49670341 -0.70335777
## 98 -0.5339449 -0.6125297 0.06737971 -0.23397626 -0.70335777
## 99 -0.5339449 -0.6125297 1.07197836 -0.16474907 -0.70335777
## 209 -0.5339449 -0.6125297 1.07197836 -0.44165782 0.66641609
## 254 -0.5339449 -0.6125297 -0.71397479 0.94288592 0.66641609
## 331 1.8728523 -0.6125297 1.18360043 0.94288592 1.07734825
## 357 -0.5339449 -0.6125297 0.51386800 0.11215968 -0.70335777
## 410 -0.5339449 -0.6125297 1.07197836 -0.99547531 0.66641609
## 1426 -0.5339449 -0.6125297 1.63008872 -1.34161124 0.66641609
## 1709 -0.5339449 -0.6125297 -0.71397479 0.52752280 -0.01847084
## 1746 -0.5339449 1.6325740 0.73711214 -0.58011219 0.66641609
## 1796 1.8728523 1.6325740 0.17900178 0.87365873 -0.01847084
## 2118 -0.5339449 1.6325740 1.29522250 -1.89542874 1.35130302
## 2126 -0.5339449 1.6325740 -1.16046308 0.73520436 -0.01847084
## 2134 1.8728523 1.6325740 -0.15586443 0.73520436 -0.01847084
## 2136 -0.5339449 1.6325740 1.07197836 -1.34161124 0.66641609
## 2137 -0.5339449 -0.6125297 0.06737971 0.04293249 2.03618995
## 2138 1.8728523 -0.6125297 -1.16046308 0.31984124 0.66641609
## 2141 -0.5339449 -0.6125297 1.07197836 -1.06470250 2.03618995
## 2142 1.8728523 -0.6125297 1.63008872 -1.06470250 0.66641609
## 2144 1.8728523 -0.6125297 -0.82559686 -0.85702094 -1.38824471
## 2220 -0.5339449 1.6325740 -0.49073065 1.63515778 -0.01847084
## 64 -0.5339449 -0.6125297 -1.04884101 -0.09552188 0.66641609
## 261 -0.5339449 1.6325740 1.74171079 1.49670341 1.35130302
## 478 -0.5339449 1.6325740 -0.60235272 0.11215968 -0.01847084
## 485 1.8728523 -0.6125297 -1.04884101 0.25061405 -1.38824471
## 604 -0.5339449 1.6325740 -0.04424236 0.66597717 -0.01847084
## 828 -0.5339449 -0.6125297 0.51386800 1.08134029 1.35130302
## 1392 1.8728523 -0.6125297 -0.49073065 0.73520436 -0.01847084
## 1463 -0.5339449 -0.6125297 -0.60235272 0.04293249 -0.01847084
## 1647 -0.5339449 -0.6125297 -0.71397479 -0.92624812 -0.70335777
## 1881 -0.5339449 -0.6125297 1.07197836 1.91206653 -1.38824471
## 2124 -0.5339449 1.6325740 0.06737971 -0.92624812 1.35130302
## 2131 -0.5339449 -0.6125297 0.06737971 -0.78779375 0.66641609
## 2140 1.8728523 1.6325740 -0.60235272 -1.82620155 0.52943870
## 2213 -0.5339449 -0.6125297 -0.04424236 -1.41083843 -0.01847084
## 159 1.8728523 1.6325740 -1.04884101 -1.13392968 -0.70335777
## 454 -0.5339449 -0.6125297 0.62549007 0.38906842 -1.38824471
## 458 -0.5339449 -0.6125297 -1.16046308 2.05052090 -2.07313164
## 475 1.8728523 -0.6125297 -0.93721894 -0.44165782 -0.70335777
## 493 -0.5339449 1.6325740 0.84873421 0.25061405 -0.01847084
## 939 1.8728523 1.6325740 0.06737971 0.80443154 0.66641609
## 1150 -0.5339449 -0.6125297 -1.16046308 2.46588402 -0.01847084
## 1213 -0.5339449 1.6325740 -0.04424236 1.08134029 -1.38824471
## 1264 -0.5339449 1.6325740 -0.60235272 -0.99547531 -0.01847084
## 1300 -0.5339449 -0.6125297 1.51846664 -0.16474907 -0.01847084
## 1562 1.8728523 -0.6125297 -1.16046308 -0.44165782 -0.70335777
## 1661 -0.5339449 -0.6125297 -1.60695137 0.31984124 -0.70335777
## 1776 -0.5339449 1.6325740 0.29062385 -2.10311030 0.66641609
## 2112 1.8728523 1.6325740 -0.82559686 1.01211310 -0.01847084
## 2115 -0.5339449 1.6325740 0.06737971 -1.48006562 0.66641609
## 2122 -0.5339449 -0.6125297 -0.93721894 -0.85702094 -0.97731255
## 2123 1.8728523 -0.6125297 0.62549007 0.45829561 1.35130302
## 2125 -0.5339449 -0.6125297 0.96035628 0.04293249 -0.70335777
## 2128 -0.5339449 -0.6125297 -0.04424236 0.66597717 -0.01847084
## 45 1.8728523 1.6325740 0.62549007 -0.99547531 -1.38824471
## 179 -0.5339449 -0.6125297 -0.60235272 0.25061405 -0.56638039
## 306 1.8728523 -0.6125297 2.74630943 0.80443154 -0.01847084
## 1307 1.8728523 -0.6125297 -0.93721894 -1.20315687 -1.25126732
## 1872 -0.5339449 1.6325740 0.51386800 -0.30320344 -0.01847084
## 2082 -0.5339449 1.6325740 1.63008872 -1.54929280 1.35130302
## 2117 -0.5339449 -0.6125297 0.06737971 -0.85702094 -1.38824471
## 2119 1.8728523 1.6325740 0.62549007 0.11215968 2.03618995
## 304 -0.5339449 1.6325740 1.18360043 -0.02629470 -0.01847084
## 334 1.8728523 -0.6125297 -0.04424236 -1.13392968 -0.70335777
## 513 -0.5339449 1.6325740 0.62549007 -0.64933938 0.66641609
## 598 -0.5339449 -0.6125297 -0.60235272 -0.44165782 2.72107688
## 909 -0.5339449 1.6325740 -0.49073065 0.87365873 0.66641609
## 1030 1.8728523 -0.6125297 -1.04884101 -0.44165782 -0.01847084
## 1238 1.8728523 1.6325740 -1.27208515 0.31984124 -0.70335777
## 1700 1.8728523 -0.6125297 -0.04424236 -1.13392968 -1.38824471
## 2017 -0.5339449 -0.6125297 -0.15586443 2.32742965 -1.38824471
## 2087 1.8728523 1.6325740 0.17900178 0.87365873 -0.29242562
## 2106 -0.5339449 1.6325740 0.17900178 -0.23397626 -0.01847084
## 2109 -0.5339449 -0.6125297 2.07657700 -0.30320344 2.72107688
## 2110 -0.5339449 1.6325740 1.29522250 -0.71856656 2.03618995
## 2111 -0.5339449 -0.6125297 -0.60235272 -0.64933938 -0.01847084
## 2114 1.8728523 1.6325740 -0.04424236 0.18138686 1.07734825
## 40 -0.5339449 -0.6125297 -0.49073065 1.08134029 -0.70335777
## 95 -0.5339449 1.6325740 -0.49073065 -0.85702094 -0.01847084
## 234 1.8728523 1.6325740 -1.60695137 -1.34161124 -0.01847084
## 358 -0.5339449 -0.6125297 -0.37910858 -0.78779375 -1.38824471
## 450 -0.5339449 -0.6125297 0.40224593 0.38906842 0.66641609
## 621 -0.5339449 -0.6125297 0.29062385 1.49670341 -0.01847084
## 934 1.8728523 1.6325740 1.63008872 0.38906842 1.35130302
## 965 -0.5339449 1.6325740 1.07197836 -0.58011219 -0.70335777
## 1033 1.8728523 1.6325740 1.63008872 -0.16474907 -1.25126732
## 1068 1.8728523 -0.6125297 -0.49073065 0.87365873 -1.38824471
## 1120 -0.5339449 -0.6125297 -0.49073065 0.73520436 -0.70335777
## 1234 -0.5339449 -0.6125297 -1.16046308 1.35824904 -0.01847084
## 2011 1.8728523 -0.6125297 -0.26748651 -0.16474907 -0.70335777
## 2068 -0.5339449 -0.6125297 0.62549007 0.04293249 -0.01847084
## 2078 -0.5339449 1.6325740 -0.49073065 0.11215968 -1.38824471
## 2100 1.8728523 1.6325740 1.29522250 0.25061405 -0.70335777
## 2102 -0.5339449 -0.6125297 0.73711214 1.21979466 1.07734825
## 2105 -0.5339449 -0.6125297 -0.71397479 0.52752280 -0.70335777
## 2203 -0.5339449 -0.6125297 -0.71397479 0.38906842 -1.38824471
## 193 -0.5339449 1.6325740 -0.04424236 -0.44165782 -0.01847084
## 387 -0.5339449 -0.6125297 1.07197836 -0.71856656 0.66641609
## 731 -0.5339449 -0.6125297 0.62549007 -0.78779375 2.72107688
## 1262 -0.5339449 -0.6125297 0.40224593 -1.48006562 -0.01847084
## 1342 -0.5339449 -0.6125297 1.63008872 -0.99547531 -0.01847084
## 1465 1.8728523 -0.6125297 -1.27208515 -0.85702094 0.66641609
## 2048 1.8728523 -0.6125297 -0.49073065 0.66597717 -0.70335777
## 2093 -0.5339449 -0.6125297 1.07197836 -0.16474907 0.66641609
## 2099 -0.5339449 -0.6125297 -1.04884101 0.94288592 -0.01847084
## 2101 -0.5339449 -0.6125297 -0.15586443 -0.09552188 -1.38824471
## 2103 -0.5339449 -0.6125297 -0.04424236 -0.99547531 0.66641609
## 2205 -0.5339449 -0.6125297 -0.15586443 1.35824904 -0.70335777
## 72 -0.5339449 1.6325740 1.07197836 -0.23397626 2.03618995
## 299 1.8728523 1.6325740 0.51386800 -1.41083843 -0.70335777
## 1043 -0.5339449 -0.6125297 -0.49073065 -1.68774718 -0.01847084
## 1049 -0.5339449 -0.6125297 -0.26748651 -1.20315687 -0.70335777
## 1240 -0.5339449 -0.6125297 -0.93721894 -0.58011219 -0.01847084
## 1561 1.8728523 -0.6125297 -0.04424236 0.94288592 2.72107688
## 1723 -0.5339449 1.6325740 -0.60235272 1.98129372 -0.84033516
## 1992 -0.5339449 -0.6125297 0.40224593 0.59674998 -0.70335777
## 2005 -0.5339449 -0.6125297 -0.37910858 -0.44165782 -1.38824471
## 2088 -0.5339449 1.6325740 0.06737971 -1.20315687 -0.01847084
## 2094 -0.5339449 -0.6125297 -0.49073065 0.11215968 -0.01847084
## 2097 1.8728523 -0.6125297 -1.04884101 1.91206653 -0.01847084
## 84 -0.5339449 1.6325740 -1.16046308 2.18897528 -0.01847084
## 273 -0.5339449 1.6325740 0.62549007 -0.44165782 -0.70335777
## 370 -0.5339449 1.6325740 -0.15586443 -0.71856656 -0.01847084
## 418 -0.5339449 1.6325740 0.51386800 -0.44165782 -0.70335777
## 473 -0.5339449 1.6325740 -0.04424236 -0.92624812 -1.38824471
## 646 1.8728523 -0.6125297 1.29522250 -0.44165782 -1.38824471
## 670 -0.5339449 -0.6125297 0.17900178 0.45829561 -0.97731255
## 1381 -0.5339449 -0.6125297 -0.37910858 1.28902185 -0.01847084
## 1512 -0.5339449 1.6325740 -1.16046308 0.38906842 -0.70335777
## 1738 -0.5339449 -0.6125297 -0.26748651 1.56593060 0.66641609
## 1928 1.8728523 -0.6125297 -0.82559686 0.38906842 -0.70335777
## 1973 1.8728523 1.6325740 0.51386800 -0.30320344 -1.38824471
## 1976 1.8728523 -0.6125297 0.17900178 1.84283934 -0.01847084
## 2024 -0.5339449 -0.6125297 0.96035628 1.77361216 0.66641609
## 2049 -0.5339449 -0.6125297 -1.60695137 1.01211310 -0.01847084
## 2070 1.8728523 -0.6125297 0.06737971 -0.78779375 -0.56638039
## 2079 -0.5339449 -0.6125297 0.06737971 1.84283934 -0.70335777
## 2080 1.8728523 -0.6125297 1.18360043 0.31984124 0.66641609
## 2083 1.8728523 -0.6125297 1.63008872 1.56593060 -0.56638039
## 2085 -0.5339449 1.6325740 0.40224593 -0.78779375 1.62525779
## 2086 1.8728523 -0.6125297 0.62549007 -0.37243063 -0.01847084
## 2090 -0.5339449 1.6325740 -1.16046308 0.11215968 0.66641609
## 2091 -0.5339449 1.6325740 -0.60235272 -0.09552188 1.35130302
## 2092 -0.5339449 -0.6125297 -0.04424236 0.31984124 -1.38824471
## 75 1.8728523 -0.6125297 0.62549007 1.21979466 1.35130302
## 106 1.8728523 1.6325740 0.17900178 -1.13392968 -0.01847084
## 413 -0.5339449 -0.6125297 0.06737971 -0.92624812 0.66641609
## 432 -0.5339449 -0.6125297 1.40684457 -1.82620155 -1.38824471
## 531 -0.5339449 -0.6125297 -0.93721894 0.45829561 -0.01847084
## 839 -0.5339449 -0.6125297 0.06737971 -0.58011219 0.66641609
## 1062 -0.5339449 1.6325740 -0.60235272 0.04293249 1.35130302
## 1163 -0.5339449 -0.6125297 0.17900178 1.08134029 -0.70335777
## 1177 -0.5339449 -0.6125297 -0.04424236 -0.85702094 -1.38824471
## 1855 1.8728523 -0.6125297 -0.04424236 1.21979466 -0.70335777
## 2033 -0.5339449 -0.6125297 0.17900178 0.38906842 -0.70335777
## 2071 1.8728523 1.6325740 2.74630943 -0.85702094 -0.01847084
## 2073 -0.5339449 -0.6125297 0.62549007 0.31984124 2.03618995
## 2076 -0.5339449 1.6325740 -0.93721894 -0.64933938 1.35130302
## 2084 -0.5339449 -0.6125297 0.06737971 1.35824904 -1.38824471
## 78 -0.5339449 -0.6125297 2.07657700 -1.61851999 1.35130302
## 264 -0.5339449 1.6325740 1.85333286 -1.75697436 0.66641609
## 315 -0.5339449 1.6325740 -1.27208515 -0.44165782 -0.70335777
## 439 -0.5339449 -0.6125297 -1.16046308 0.11215968 1.35130302
## 529 -0.5339449 1.6325740 -0.82559686 0.31984124 -0.42940300
## 758 -0.5339449 -0.6125297 0.51386800 0.11215968 1.35130302
## 1092 -0.5339449 1.6325740 -1.04884101 1.70438497 -0.70335777
## 1130 -0.5339449 -0.6125297 -1.60695137 0.04293249 -1.38824471
## 1412 1.8728523 -0.6125297 -0.71397479 0.25061405 -0.70335777
## 1614 -0.5339449 -0.6125297 -1.16046308 0.25061405 -0.70335777
## 1802 1.8728523 1.6325740 -0.37910858 -0.71856656 2.72107688
## 1970 -0.5339449 1.6325740 -0.04424236 -0.64933938 0.66641609
## 1983 1.8728523 -0.6125297 1.96495493 -0.92624812 -0.01847084
## 2012 -0.5339449 -0.6125297 0.51386800 2.25820246 -0.01847084
## 2053 -0.5339449 -0.6125297 0.29062385 -0.78779375 -0.01847084
## 2057 -0.5339449 -0.6125297 -0.49073065 0.73520436 -0.70335777
## 2058 -0.5339449 -0.6125297 0.17900178 -0.92624812 -0.01847084
## 2059 1.8728523 -0.6125297 0.40224593 -0.09552188 0.66641609
## 2074 1.8728523 1.6325740 1.18360043 -0.71856656 -0.70335777
## 2075 -0.5339449 -0.6125297 1.07197836 -1.75697436 -0.42940300
## 2215 -0.5339449 -0.6125297 1.07197836 0.04293249 1.89921256
## 37 1.8728523 1.6325740 0.29062385 -1.13392968 1.35130302
## 65 -0.5339449 1.6325740 -0.71397479 0.94288592 1.07734825
## 100 -0.5339449 1.6325740 -0.04424236 -0.92624812 0.66641609
## 108 -0.5339449 -0.6125297 0.62549007 0.52752280 0.66641609
## 173 -0.5339449 -0.6125297 2.18819907 -0.44165782 -0.01847084
## 207 1.8728523 -0.6125297 -0.60235272 -0.23397626 0.66641609
## 824 -0.5339449 1.6325740 -0.37910858 -0.71856656 -0.70335777
## 1141 -0.5339449 -0.6125297 1.07197836 -0.16474907 -0.70335777
## 1145 -0.5339449 -0.6125297 -1.71857344 0.11215968 -0.97731255
## 1618 -0.5339449 -0.6125297 -0.15586443 0.52752280 -1.38824471
## 1812 1.8728523 -0.6125297 -0.93721894 0.18138686 -1.38824471
## 1856 -0.5339449 -0.6125297 0.84873421 1.98129372 -0.70335777
## 1937 -0.5339449 -0.6125297 -1.16046308 0.73520436 -0.01847084
## 2047 -0.5339449 -0.6125297 0.06737971 1.28902185 -0.01847084
## 2061 -0.5339449 1.6325740 0.62549007 -0.78779375 0.66641609
## 2062 -0.5339449 1.6325740 1.63008872 0.18138686 -0.70335777
## 2063 -0.5339449 1.6325740 -0.49073065 -0.58011219 -0.01847084
## 2221 -0.5339449 -0.6125297 0.62549007 -0.37243063 -0.01847084
## 295 1.8728523 -0.6125297 -1.60695137 0.11215968 -0.70335777
## 337 1.8728523 -0.6125297 -0.26748651 0.18138686 0.66641609
## 372 -0.5339449 -0.6125297 0.06737971 2.60433840 -1.38824471
## 546 -0.5339449 -0.6125297 -0.04424236 0.18138686 -0.01847084
## 874 -0.5339449 1.6325740 -0.04424236 -1.48006562 -0.01847084
## 972 1.8728523 1.6325740 -1.04884101 -0.64933938 -0.70335777
## 1210 -0.5339449 1.6325740 -0.04424236 0.31984124 -0.01847084
## 1298 -0.5339449 1.6325740 -1.71857344 -1.06470250 -0.01847084
## 1498 -0.5339449 -0.6125297 -1.16046308 0.73520436 -0.70335777
## 1979 1.8728523 1.6325740 -0.60235272 -0.44165782 -0.70335777
## 2066 -0.5339449 -0.6125297 -0.15586443 0.45829561 -0.01847084
## 383 -0.5339449 -0.6125297 0.29062385 -1.82620155 -0.01847084
## 434 1.8728523 -0.6125297 -0.37910858 0.73520436 -0.70335777
## 843 -0.5339449 1.6325740 -0.93721894 0.25061405 0.66641609
## 865 -0.5339449 -0.6125297 0.96035628 0.45829561 0.66641609
## 922 -0.5339449 -0.6125297 -1.60695137 -0.92624812 -1.38824471
## 1418 -0.5339449 -0.6125297 2.29982115 -0.78779375 -0.01847084
## 1485 -0.5339449 -0.6125297 0.51386800 1.35824904 -1.38824471
## 1842 -0.5339449 -0.6125297 1.63008872 -0.02629470 2.03618995
## 2037 -0.5339449 1.6325740 0.62549007 0.18138686 -0.01847084
## 2052 -0.5339449 -0.6125297 -0.60235272 0.73520436 -0.01847084
## 371 -0.5339449 1.6325740 0.62549007 0.52752280 -0.01847084
## 376 -0.5339449 -0.6125297 -0.60235272 1.91206653 -0.70335777
## 377 1.8728523 1.6325740 1.29522250 -0.99547531 -1.38824471
## 506 -0.5339449 -0.6125297 -1.60695137 -0.64933938 1.48828040
## 728 1.8728523 -0.6125297 -0.26748651 0.31984124 -1.38824471
## 826 -0.5339449 -0.6125297 -0.04424236 -0.02629470 -0.01847084
## 1642 -0.5339449 -0.6125297 -0.26748651 1.70438497 -0.70335777
## 1707 -0.5339449 -0.6125297 -1.16046308 1.08134029 0.66641609
## 1751 -0.5339449 -0.6125297 -0.60235272 -1.27238406 -0.01847084
## 1864 -0.5339449 -0.6125297 -0.49073065 1.84283934 -0.70335777
## 1895 1.8728523 1.6325740 0.29062385 -0.44165782 -0.01847084
## 1998 1.8728523 1.6325740 0.40224593 -0.58011219 -0.01847084
## 31 1.8728523 1.6325740 -1.16046308 -0.71856656 -0.01847084
## 35 1.8728523 -0.6125297 -0.37910858 -0.44165782 -0.70335777
## 90 -0.5339449 -0.6125297 -1.71857344 -0.51088500 0.66641609
## 312 -0.5339449 1.6325740 0.40224593 -0.37243063 -0.70335777
## 457 -0.5339449 -0.6125297 -0.04424236 -1.41083843 -0.01847084
## 466 -0.5339449 -0.6125297 0.17900178 2.18897528 -0.70335777
## 870 1.8728523 -0.6125297 1.63008872 -1.13392968 -0.70335777
## 913 1.8728523 -0.6125297 -0.60235272 1.28902185 -0.70335777
## 1013 -0.5339449 -0.6125297 -0.04424236 1.35824904 -0.70335777
## 1117 -0.5339449 -0.6125297 -1.16046308 2.39665684 0.66641609
## 1306 -0.5339449 -0.6125297 -1.60695137 0.80443154 -0.70335777
## 1619 1.8728523 -0.6125297 -1.16046308 1.56593060 -1.11428993
## 1756 1.8728523 -0.6125297 0.51386800 -0.30320344 2.72107688
## 2026 1.8728523 -0.6125297 0.73711214 -0.78779375 -0.70335777
## 2028 -0.5339449 1.6325740 -0.04424236 0.11215968 0.66641609
## 2038 -0.5339449 -0.6125297 -0.37910858 -2.38001904 -0.01847084
## 2039 -0.5339449 -0.6125297 -1.04884101 0.25061405 -0.70335777
## 2043 -0.5339449 1.6325740 0.17900178 -0.37243063 -0.01847084
## 2227 1.8728523 1.6325740 0.40224593 2.39665684 0.66641609
## 162 -0.5339449 -0.6125297 1.96495493 0.31984124 -1.38824471
## 528 -0.5339449 -0.6125297 -0.60235272 0.87365873 -0.01847084
## 581 -0.5339449 -0.6125297 1.07197836 0.11215968 -0.70335777
## 600 -0.5339449 -0.6125297 2.74630943 -0.51088500 1.35130302
## 601 -0.5339449 1.6325740 0.73711214 0.04293249 0.66641609
## 814 -0.5339449 -0.6125297 0.29062385 -0.85702094 -0.15544823
## 1318 -0.5339449 -0.6125297 -1.16046308 -0.64933938 -0.70335777
## 1477 -0.5339449 -0.6125297 -0.26748651 -1.61851999 2.03618995
## 1982 -0.5339449 -0.6125297 0.51386800 -0.02629470 0.66641609
## 2029 1.8728523 1.6325740 0.62549007 0.18138686 -0.01847084
## 2034 -0.5339449 -0.6125297 0.17900178 -0.44165782 -0.01847084
## 2041 -0.5339449 -0.6125297 -0.93721894 -0.51088500 -0.70335777
## 2042 -0.5339449 -0.6125297 0.40224593 -0.99547531 0.66641609
## 2224 -0.5339449 -0.6125297 1.07197836 -0.44165782 -0.70335777
## 2229 -0.5339449 -0.6125297 -1.04884101 0.25061405 0.66641609
## 612 -0.5339449 1.6325740 -0.15586443 0.11215968 -0.56638039
## 703 1.8728523 -0.6125297 0.73711214 -0.51088500 0.66641609
## 916 -0.5339449 1.6325740 0.62549007 -0.02629470 -0.70335777
## 994 -0.5339449 -0.6125297 1.18360043 1.35824904 -0.01847084
## 1188 -0.5339449 -0.6125297 1.07197836 -0.78779375 1.35130302
## 1216 -0.5339449 1.6325740 -0.04424236 -0.64933938 0.66641609
## 1890 -0.5339449 -0.6125297 -0.37910858 1.15056748 -2.07313164
## 1991 -0.5339449 -0.6125297 -0.04424236 1.28902185 0.66641609
## 2031 -0.5339449 -0.6125297 0.17900178 -1.41083843 1.35130302
## 2032 -0.5339449 -0.6125297 -1.04884101 1.21979466 2.03618995
## 97 -0.5339449 1.6325740 2.74630943 1.21979466 1.89921256
## 201 -0.5339449 -0.6125297 0.96035628 0.52752280 -0.70335777
## 341 1.8728523 1.6325740 -0.93721894 -0.37243063 -0.70335777
## 717 -0.5339449 -0.6125297 -0.49073065 -0.85702094 -0.70335777
## 1364 -0.5339449 1.6325740 -1.71857344 0.11215968 0.94037086
## 1439 1.8728523 -0.6125297 -0.71397479 0.52752280 -0.84033516
## 1474 1.8728523 -0.6125297 -0.60235272 1.01211310 -0.01847084
## 1489 -0.5339449 -0.6125297 -1.60695137 1.49670341 0.66641609
## 1669 -0.5339449 -0.6125297 -1.16046308 -0.23397626 -0.01847084
## 1687 -0.5339449 -0.6125297 -1.04884101 -1.34161124 -1.38824471
## 1704 -0.5339449 1.6325740 -1.04884101 -0.09552188 -0.70335777
## 1706 -0.5339449 -0.6125297 -1.16046308 -0.85702094 -0.01847084
## 1780 -0.5339449 -0.6125297 -0.71397479 -0.02629470 -0.70335777
## 1984 -0.5339449 -0.6125297 -0.37910858 0.59674998 1.35130302
## 2022 -0.5339449 1.6325740 -0.04424236 0.87365873 -0.70335777
## 2025 -0.5339449 -0.6125297 0.96035628 0.25061405 0.66641609
## 2217 1.8728523 -0.6125297 0.40224593 -0.71856656 -0.01847084
## 2222 -0.5339449 -0.6125297 -1.04884101 -0.37243063 -0.70335777
## 135 1.8728523 -0.6125297 -0.15586443 0.66597717 -0.70335777
## 166 1.8728523 -0.6125297 0.62549007 -0.71856656 0.66641609
## 468 -0.5339449 -0.6125297 0.51386800 -0.16474907 -0.70335777
## 626 -0.5339449 -0.6125297 1.29522250 -0.37243063 -0.01847084
## 1031 -0.5339449 1.6325740 -0.49073065 -0.02629470 -0.01847084
## 1362 1.8728523 -0.6125297 -0.04424236 1.77361216 -1.38824471
## 1510 1.8728523 -0.6125297 -1.27208515 0.04293249 -1.38824471
## 1625 -0.5339449 -0.6125297 -0.60235272 0.38906842 -1.25126732
## 1696 -0.5339449 -0.6125297 -0.26748651 0.18138686 0.11850654
## 1699 -0.5339449 -0.6125297 0.06737971 0.94288592 1.35130302
## 1705 -0.5339449 -0.6125297 1.63008872 -1.41083843 -0.42940300
## 1715 -0.5339449 -0.6125297 0.51386800 -0.16474907 -1.11428993
## 1861 -0.5339449 1.6325740 -1.16046308 0.18138686 -0.01847084
## 1941 1.8728523 -0.6125297 -0.04424236 1.42747622 -1.38824471
## 1964 -0.5339449 -0.6125297 1.07197836 -0.99547531 -0.70335777
## 2218 -0.5339449 -0.6125297 -0.04424236 -1.41083843 -0.01847084
## 2228 -0.5339449 1.6325740 -0.60235272 -0.51088500 -0.70335777
## 2230 -0.5339449 -0.6125297 0.29062385 -1.54929280 -1.38824471
## 255 1.8728523 1.6325740 -0.37910858 -1.13392968 0.66641609
## 819 1.8728523 -0.6125297 -0.49073065 1.70438497 -0.42940300
## 917 -0.5339449 1.6325740 -0.26748651 1.91206653 -0.70335777
## 1085 -0.5339449 -0.6125297 0.51386800 0.73520436 -0.42940300
## 1187 -0.5339449 -0.6125297 -0.60235272 1.77361216 -1.25126732
## 1394 -0.5339449 -0.6125297 -0.49073065 0.31984124 -0.70335777
## 1479 -0.5339449 -0.6125297 -1.60695137 0.73520436 -0.70335777
## 1517 1.8728523 -0.6125297 -0.37910858 -1.48006562 -0.70335777
## 2009 -0.5339449 1.6325740 -0.82559686 1.01211310 1.35130302
## 2223 -0.5339449 -0.6125297 -0.15586443 1.42747622 -1.38824471
## 2225 -0.5339449 -0.6125297 -0.37910858 -0.44165782 2.72107688
## 2226 -0.5339449 -0.6125297 -0.93721894 1.15056748 -1.38824471
## 27 -0.5339449 1.6325740 0.96035628 0.25061405 -0.01847084
## 105 -0.5339449 -0.6125297 -1.16046308 -0.09552188 2.72107688
## 147 1.8728523 -0.6125297 -0.04424236 -0.30320344 -0.01847084
## 224 -0.5339449 -0.6125297 0.96035628 -0.85702094 -0.42940300
## 274 1.8728523 -0.6125297 0.06737971 -1.48006562 -0.70335777
## 356 1.8728523 1.6325740 -0.82559686 0.73520436 2.72107688
## 431 -0.5339449 -0.6125297 0.62549007 0.94288592 -0.01847084
## 551 -0.5339449 -0.6125297 0.06737971 0.04293249 -0.70335777
## 962 -0.5339449 1.6325740 -1.16046308 1.21979466 -0.70335777
## 997 -0.5339449 -0.6125297 3.41604186 0.87365873 -0.01847084
## 1389 1.8728523 1.6325740 -1.71857344 1.21979466 -0.01847084
## 1831 1.8728523 1.6325740 -0.60235272 -0.09552188 -0.01847084
## 1996 1.8728523 1.6325740 0.96035628 -1.20315687 2.03618995
## 1999 -0.5339449 -0.6125297 1.63008872 -2.24156467 1.35130302
## 2008 -0.5339449 -0.6125297 -0.60235272 -1.27238406 0.25548393
## 2216 1.8728523 -0.6125297 -0.93721894 0.25061405 -0.70335777
## 107 -0.5339449 1.6325740 0.29062385 -0.51088500 0.66641609
## 148 -0.5339449 -0.6125297 -1.27208515 -1.06470250 -0.01847084
## 182 -0.5339449 -0.6125297 -0.04424236 -0.99547531 1.35130302
## 414 -0.5339449 1.6325740 0.73711214 -1.34161124 -0.70335777
## 511 -0.5339449 1.6325740 -1.04884101 0.18138686 -1.38824471
## 699 -0.5339449 -0.6125297 -0.71397479 0.38906842 -0.01847084
## 1017 -0.5339449 1.6325740 -0.26748651 -0.85702094 -0.01847084
## 1366 -0.5339449 -0.6125297 -1.71857344 -0.78779375 2.72107688
## 1672 -0.5339449 1.6325740 -0.82559686 0.25061405 -0.70335777
## 1931 -0.5339449 -0.6125297 0.84873421 -0.51088500 1.35130302
## 1966 -0.5339449 -0.6125297 -0.49073065 -0.64933938 -0.70335777
## 1994 -0.5339449 -0.6125297 0.51386800 -1.41083843 2.03618995
## 2002 -0.5339449 -0.6125297 1.07197836 -0.92624812 -0.70335777
## 2007 -0.5339449 -0.6125297 2.18819907 1.21979466 -0.70335777
## 2 -0.5339449 -0.6125297 1.29522250 -1.61851999 -0.01847084
## 143 1.8728523 -0.6125297 -0.71397479 -0.64933938 -0.01847084
## 160 -0.5339449 -0.6125297 1.74171079 -1.96465592 -0.01847084
## 688 -0.5339449 1.6325740 0.73711214 -0.51088500 0.66641609
## 776 -0.5339449 -0.6125297 0.06737971 1.84283934 -0.01847084
## 850 -0.5339449 -0.6125297 -0.04424236 -1.89542874 1.35130302
## 1194 -0.5339449 1.6325740 -0.04424236 1.49670341 -0.70335777
## 1849 -0.5339449 -0.6125297 -0.49073065 -1.34161124 -0.70335777
## 5 -0.5339449 1.6325740 1.07197836 1.21979466 -0.01847084
## 55 1.8728523 -0.6125297 1.07197836 -0.58011219 -0.01847084
## 347 -0.5339449 -0.6125297 1.63008872 -1.96465592 -0.01847084
## 856 -0.5339449 -0.6125297 -0.60235272 -0.85702094 -0.01847084
## 1972 -0.5339449 -0.6125297 -0.04424236 -1.68774718 1.35130302
## 1977 -0.5339449 -0.6125297 0.29062385 -1.48006562 -1.38824471
## 1980 -0.5339449 -0.6125297 -1.38370722 -0.23397626 -1.38824471
## 1981 1.8728523 -0.6125297 1.07197836 -0.02629470 -0.70335777
## 1985 1.8728523 1.6325740 -1.16046308 0.25061405 -1.38824471
## 1987 -0.5339449 1.6325740 0.73711214 -0.16474907 0.66641609
## 1995 1.8728523 -0.6125297 -1.04884101 -0.44165782 -1.38824471
## 2207 -0.5339449 -0.6125297 -0.82559686 -1.13392968 -0.70335777
## 244 -0.5339449 1.6325740 -0.60235272 -0.09552188 -0.01847084
## 308 -0.5339449 -0.6125297 1.51846664 1.63515778 0.66641609
## 560 -0.5339449 -0.6125297 -0.04424236 0.59674998 -0.70335777
## 561 -0.5339449 -0.6125297 -1.27208515 0.94288592 1.35130302
## 1173 -0.5339449 -0.6125297 -1.04884101 0.04293249 0.66641609
## 1190 1.8728523 -0.6125297 -0.82559686 -2.03388311 -0.70335777
## 1269 1.8728523 -0.6125297 0.51386800 0.87365873 -0.01847084
## 1453 -0.5339449 -0.6125297 1.96495493 0.31984124 0.66641609
## 1543 -0.5339449 -0.6125297 -0.37910858 0.66597717 -1.38824471
## 1681 -0.5339449 -0.6125297 -1.27208515 -1.13392968 -0.70335777
## 1800 1.8728523 1.6325740 -1.27208515 -0.92624812 0.66641609
## 1873 -0.5339449 -0.6125297 -0.82559686 -1.61851999 0.66641609
## 1971 -0.5339449 -0.6125297 -1.16046308 0.31984124 1.21432563
## 1974 1.8728523 -0.6125297 1.74171079 -0.16474907 -1.38824471
## 130 -0.5339449 -0.6125297 -0.15586443 0.73520436 -0.01847084
## 721 1.8728523 -0.6125297 -0.60235272 0.38906842 -1.38824471
## 837 -0.5339449 -0.6125297 2.74630943 -0.09552188 -0.70335777
## 1741 -0.5339449 -0.6125297 0.06737971 0.66597717 -0.70335777
## 1860 1.8728523 1.6325740 -1.16046308 0.25061405 -1.38824471
## 1957 -0.5339449 -0.6125297 -0.37910858 1.15056748 -0.70335777
## 1960 -0.5339449 -0.6125297 -0.60235272 1.49670341 -0.70335777
## 1965 1.8728523 -0.6125297 -1.38370722 -0.44165782 -1.38824471
## 1968 1.8728523 -0.6125297 0.06737971 0.80443154 -0.01847084
## 58 1.8728523 1.6325740 -1.04884101 -1.13392968 0.66641609
## 195 1.8728523 -0.6125297 -0.04424236 1.15056748 -0.01847084
## 1228 -0.5339449 -0.6125297 -0.60235272 3.01970152 -0.70335777
## 1257 -0.5339449 -0.6125297 -0.04424236 0.18138686 -0.01847084
## 1592 -0.5339449 1.6325740 -0.76978583 1.49670341 -0.70335777
## 1596 -0.5339449 -0.6125297 -1.16046308 1.70438497 -0.70335777
## 1750 -0.5339449 -0.6125297 -1.49532930 -2.24156467 -0.70335777
## 1837 -0.5339449 -0.6125297 0.73711214 2.18897528 0.66641609
## 1942 1.8728523 -0.6125297 -1.27208515 -0.30320344 1.35130302
## 1961 -0.5339449 -0.6125297 -0.93721894 -0.92624812 1.35130302
## 1962 1.8728523 1.6325740 -1.27208515 -0.58011219 -0.70335777
## 1963 -0.5339449 1.6325740 0.51386800 -1.06470250 -0.01847084
## 422 -0.5339449 -0.6125297 -0.15586443 -1.48006562 0.66641609
## 1401 1.8728523 -0.6125297 0.51386800 0.52752280 -1.38824471
## 1525 -0.5339449 -0.6125297 0.40224593 -0.44165782 0.66641609
## 1646 -0.5339449 -0.6125297 -0.82559686 1.49670341 -0.70335777
## 1929 -0.5339449 -0.6125297 -0.04424236 -0.30320344 0.66641609
## 1940 -0.5339449 -0.6125297 0.51386800 1.56593060 -0.70335777
## 1945 -0.5339449 -0.6125297 -0.49073065 1.56593060 -1.38824471
## 1946 -0.5339449 -0.6125297 -0.60235272 -0.58011219 -0.70335777
## 1948 -0.5339449 -0.6125297 -0.26748651 -0.51088500 -1.38824471
## 1949 -0.5339449 -0.6125297 -0.37910858 0.11215968 -0.70335777
## 1950 -0.5339449 1.6325740 -0.26748651 -0.23397626 -0.01847084
## 1953 -0.5339449 -0.6125297 0.17900178 0.31984124 -0.01847084
## 1955 -0.5339449 -0.6125297 -0.04424236 2.11974809 -0.70335777
## 202 -0.5339449 -0.6125297 -1.04884101 -0.71856656 -2.07313164
## 494 -0.5339449 -0.6125297 0.62549007 0.18138686 -0.01847084
## 740 -0.5339449 1.6325740 -0.60235272 0.18138686 -0.70335777
## 780 -0.5339449 1.6325740 0.40224593 0.73520436 1.35130302
## 1069 -0.5339449 -0.6125297 -1.04884101 -1.20315687 2.03618995
## 1537 -0.5339449 -0.6125297 -0.60235272 -1.13392968 0.66641609
## 1920 -0.5339449 -0.6125297 -0.37910858 1.01211310 -0.70335777
## 1926 -0.5339449 -0.6125297 -0.26748651 0.25061405 -0.70335777
## 1938 -0.5339449 -0.6125297 0.84873421 2.18897528 0.66641609
## 1939 -0.5339449 -0.6125297 -1.04884101 -0.16474907 0.66641609
## 1947 -0.5339449 -0.6125297 -0.60235272 -0.64933938 0.66641609
## 1951 1.8728523 -0.6125297 -1.16046308 0.38906842 -1.38824471
## 25 -0.5339449 -0.6125297 0.06737971 1.70438497 -1.38824471
## 134 -0.5339449 1.6325740 0.96035628 0.45829561 -0.01847084
## 323 -0.5339449 1.6325740 0.17900178 0.45829561 -0.01847084
## 354 -0.5339449 -0.6125297 1.07197836 -0.99547531 -0.01847084
## 381 -0.5339449 -0.6125297 -1.16046308 0.31984124 -0.01847084
## 1568 -0.5339449 -0.6125297 1.18360043 1.63515778 1.35130302
## 1766 1.8728523 -0.6125297 -1.16046308 -1.13392968 -0.01847084
## 1782 1.8728523 -0.6125297 0.06737971 -0.37243063 -0.01847084
## 1903 -0.5339449 -0.6125297 -0.49073065 -0.99547531 -0.01847084
## 1923 -0.5339449 -0.6125297 1.85333286 -0.16474907 -0.01847084
## 1925 1.8728523 1.6325740 1.74171079 -0.51088500 -0.01847084
## 1930 -0.5339449 -0.6125297 -1.38370722 0.31984124 -0.70335777
## 1932 -0.5339449 -0.6125297 0.06737971 -0.09552188 -0.70335777
## 1934 -0.5339449 -0.6125297 0.84873421 0.73520436 1.35130302
## 385 -0.5339449 1.6325740 -0.04424236 0.18138686 -0.15544823
## 548 -0.5339449 -0.6125297 -1.16046308 -0.02629470 -0.70335777
## 1078 -0.5339449 1.6325740 0.06737971 -1.06470250 0.66641609
## 1429 -0.5339449 -0.6125297 -0.60235272 1.84283934 0.66641609
## 1581 -0.5339449 -0.6125297 -1.38370722 0.87365873 -0.29242562
## 1609 1.8728523 -0.6125297 -0.60235272 0.73520436 -1.11428993
## 1905 -0.5339449 -0.6125297 -0.04424236 -1.61851999 2.72107688
## 1908 -0.5339449 1.6325740 -1.49532930 1.15056748 -0.01847084
## 1916 1.8728523 -0.6125297 0.73711214 0.59674998 0.66641609
## 1918 -0.5339449 -0.6125297 3.30441979 2.11974809 -0.01847084
## 1919 -0.5339449 -0.6125297 -0.04424236 0.38906842 0.66641609
## 1921 1.8728523 -0.6125297 -0.15586443 0.38906842 -0.70335777
## 1927 -0.5339449 1.6325740 -1.04884101 0.38906842 -0.01847084
## 1933 1.8728523 1.6325740 0.29062385 0.25061405 -1.38824471
## 77 1.8728523 1.6325740 0.40224593 -0.37243063 0.66641609
## 184 -0.5339449 -0.6125297 0.62549007 0.45829561 -0.70335777
## 498 -0.5339449 -0.6125297 0.29062385 -0.09552188 -0.70335777
## 555 -0.5339449 -0.6125297 1.40684457 -1.34161124 -0.01847084
## 574 -0.5339449 -0.6125297 1.07197836 -2.03388311 2.72107688
## 783 -0.5339449 -0.6125297 -0.71397479 -0.58011219 0.66641609
## 956 -0.5339449 -0.6125297 -0.49073065 0.73520436 -0.01847084
## 1584 1.8728523 -0.6125297 -0.37910858 -0.78779375 -0.01847084
## 1797 1.8728523 -0.6125297 0.62549007 -0.23397626 1.35130302
## 1863 -0.5339449 -0.6125297 0.40224593 0.66597717 -0.70335777
## 1874 1.8728523 -0.6125297 -0.71397479 -1.41083843 1.35130302
## 1882 -0.5339449 -0.6125297 -0.04424236 0.80443154 -1.38824471
## 1898 -0.5339449 1.6325740 -0.60235272 -0.58011219 2.03618995
## 1911 1.8728523 -0.6125297 1.07197836 -0.64933938 -0.70335777
## 1913 1.8728523 -0.6125297 -1.04884101 -0.23397626 -0.70335777
## 1914 -0.5339449 -0.6125297 1.85333286 -0.44165782 -0.70335777
## 1915 1.8728523 -0.6125297 -1.16046308 -0.51088500 0.25548393
## 115 -0.5339449 -0.6125297 -0.71397479 -0.02629470 -0.01847084
## 259 1.8728523 -0.6125297 -0.04424236 -0.23397626 1.35130302
## 321 1.8728523 -0.6125297 -1.16046308 -0.92624812 -1.38824471
## 825 -0.5339449 1.6325740 3.41604186 1.35824904 2.72107688
## 1370 -0.5339449 -0.6125297 0.62549007 1.15056748 -0.70335777
## 1621 -0.5339449 -0.6125297 -1.49532930 -0.16474907 -0.01847084
## 1643 -0.5339449 -0.6125297 1.74171079 -0.44165782 -1.38824471
## 1688 -0.5339449 -0.6125297 -0.60235272 -0.85702094 1.35130302
## 1865 -0.5339449 -0.6125297 -0.60235272 -0.16474907 0.66641609
## 1880 -0.5339449 1.6325740 -0.04424236 -1.13392968 -1.11428993
## 1886 -0.5339449 -0.6125297 0.62549007 1.49670341 0.66641609
## 1889 1.8728523 1.6325740 -0.60235272 -0.09552188 -1.38824471
## 1892 -0.5339449 -0.6125297 -0.71397479 -1.41083843 -0.01847084
## 1897 -0.5339449 -0.6125297 -0.93721894 -0.09552188 -0.01847084
## 1899 -0.5339449 -0.6125297 -0.60235272 -1.61851999 2.03618995
## 1900 -0.5339449 -0.6125297 0.51386800 -0.37243063 0.66641609
## 1904 1.8728523 -0.6125297 -0.26748651 -1.34161124 -0.01847084
## 1909 -0.5339449 -0.6125297 -0.60235272 -0.02629470 0.66641609
## 83 -0.5339449 -0.6125297 -0.26748651 1.08134029 -0.70335777
## 208 -0.5339449 -0.6125297 -0.15586443 1.56593060 -0.70335777
## 329 -0.5339449 -0.6125297 -1.27208515 -0.99547531 -0.15544823
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## 1829 -0.5339449 -0.6125297 1.07197836 0.45829561 -0.01847084
## 1843 1.8728523 1.6325740 -0.15586443 -1.13392968 2.03618995
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## 1526 -0.5339449 -0.6125297 -1.16046308 0.80443154 -0.70335777
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## 619 -0.5339449 -0.6125297 0.40224593 0.52752280 -0.70335777
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## 1008 -0.5339449 -0.6125297 -1.04884101 -0.44165782 -0.70335777
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## 1732 -0.5339449 -0.6125297 0.62549007 -1.61851999 -1.38824471
## 1784 -0.5339449 -0.6125297 0.06737971 -0.64933938 1.35130302
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## 258 1.8728523 -0.6125297 0.73711214 0.66597717 -0.70335777
## 310 1.8728523 1.6325740 -1.60695137 -0.23397626 -1.38824471
## 345 -0.5339449 -0.6125297 0.40224593 1.42747622 -0.01847084
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## 676 -0.5339449 1.6325740 1.85333286 1.01211310 2.72107688
## 841 -0.5339449 -0.6125297 1.63008872 -0.64933938 -0.01847084
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## 1858 -0.5339449 -0.6125297 0.29062385 -0.51088500 0.66641609
## 1859 -0.5339449 -0.6125297 1.85333286 -1.61851999 1.35130302
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## 250 -0.5339449 -0.6125297 0.96035628 -0.44165782 -0.01847084
## 349 1.8728523 -0.6125297 -1.38370722 0.80443154 1.35130302
## 396 1.8728523 -0.6125297 -0.60235272 -0.02629470 -0.01847084
## 868 -0.5339449 -0.6125297 1.85333286 -1.34161124 1.35130302
## 979 -0.5339449 -0.6125297 1.40684457 0.31984124 -0.70335777
## 1191 -0.5339449 -0.6125297 0.40224593 -1.13392968 1.35130302
## 1409 1.8728523 -0.6125297 -0.49073065 0.87365873 -0.70335777
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## 1832 -0.5339449 -0.6125297 0.73711214 0.80443154 -0.01847084
## 1836 1.8728523 1.6325740 -0.04424236 -0.51088500 -0.70335777
## 1839 -0.5339449 -0.6125297 0.29062385 -0.92624812 1.35130302
## 1841 -0.5339449 -0.6125297 0.73711214 1.91206653 -0.70335777
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## 1845 -0.5339449 -0.6125297 0.84873421 -1.13392968 -0.70335777
## 1848 -0.5339449 -0.6125297 1.74171079 1.08134029 -0.70335777
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## 1852 -0.5339449 -0.6125297 -0.04424236 -0.92624812 0.66641609
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## 2208 -0.5339449 -0.6125297 1.63008872 0.59674998 -0.70335777
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## 1574 -0.5339449 1.6325740 -0.15586443 -1.41083843 0.66641609
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## 1789 -0.5339449 -0.6125297 -0.82559686 0.52752280 0.66641609
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## 1730 -0.5339449 -0.6125297 -1.60695137 -0.30320344 -0.70335777
## 1734 1.8728523 1.6325740 -1.16046308 0.80443154 -0.70335777
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## 1745 -0.5339449 -0.6125297 -1.16046308 -0.37243063 -0.01847084
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## 1726 -0.5339449 -0.6125297 0.96035628 1.77361216 1.35130302
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## 391 1.8728523 1.6325740 1.85333286 -0.30320344 0.66641609
## 1679 -0.5339449 -0.6125297 1.74171079 -0.44165782 0.66641609
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## 1711 -0.5339449 -0.6125297 -0.60235272 -0.23397626 -0.70335777
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## 1713 -0.5339449 -0.6125297 -0.93721894 -0.85702094 -0.01847084
## 175 -0.5339449 1.6325740 -0.37910858 0.52752280 -1.38824471
## 603 1.8728523 -0.6125297 1.63008872 -0.23397626 0.66641609
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## 709 -0.5339449 1.6325740 -0.04424236 -0.30320344 -0.01847084
## 713 -0.5339449 -0.6125297 1.74171079 -2.10311030 -0.70335777
## 13 -0.5339449 -0.6125297 -0.37910858 -0.51088500 -0.01847084
## 427 -0.5339449 -0.6125297 -1.38370722 2.32742965 -0.56638039
## 444 -0.5339449 -0.6125297 -0.60235272 -0.16474907 1.35130302
## 495 -0.5339449 1.6325740 -1.04884101 -0.58011219 1.35130302
## 504 -0.5339449 -0.6125297 0.06737971 -0.44165782 0.66641609
## 543 -0.5339449 -0.6125297 -0.49073065 -0.16474907 -0.70335777
## 658 -0.5339449 -0.6125297 -0.60235272 -0.71856656 -0.01847084
## 673 -0.5339449 1.6325740 -0.71397479 -0.02629470 -1.38824471
## 686 -0.5339449 -0.6125297 0.96035628 0.59674998 -0.01847084
## 196 -0.5339449 -0.6125297 -0.49073065 -0.71856656 -0.01847084
## 221 -0.5339449 -0.6125297 0.06737971 1.08134029 -0.70335777
## 497 -0.5339449 -0.6125297 0.62549007 0.59674998 -0.01847084
## 503 -0.5339449 1.6325740 -0.93721894 -0.23397626 -0.29242562
## 516 -0.5339449 1.6325740 -0.37910858 -0.09552188 -0.70335777
## 637 -0.5339449 -0.6125297 0.17900178 -0.64933938 -0.01847084
## 656 -0.5339449 -0.6125297 -0.04424236 0.04293249 0.80339347
## 661 -0.5339449 -0.6125297 -1.04884101 -0.51088500 -0.01847084
## 664 -0.5339449 -0.6125297 1.74171079 -1.20315687 -0.01847084
## 671 -0.5339449 1.6325740 -0.26748651 1.15056748 -0.01847084
## 674 -0.5339449 1.6325740 -0.49073065 0.11215968 -1.38824471
## 678 -0.5339449 -0.6125297 0.06737971 0.87365873 1.35130302
## 683 -0.5339449 -0.6125297 -0.04424236 -0.44165782 2.03618995
## 206 -0.5339449 -0.6125297 -0.60235272 -0.37243063 -0.01847084
## 256 1.8728523 -0.6125297 0.29062385 -0.44165782 -0.29242562
## 453 -0.5339449 1.6325740 0.73711214 1.56593060 0.66641609
## 456 -0.5339449 -0.6125297 -0.37910858 0.52752280 0.66641609
## 509 1.8728523 -0.6125297 0.84873421 -1.13392968 2.03618995
## 544 -0.5339449 1.6325740 -0.26748651 -0.71856656 -0.15544823
## 627 -0.5339449 -0.6125297 0.84873421 -0.71856656 0.66641609
## 634 -0.5339449 1.6325740 -0.93721894 1.21979466 -0.01847084
## 641 -0.5339449 -0.6125297 0.62549007 1.08134029 -0.29242562
## 642 -0.5339449 -0.6125297 0.40224593 1.15056748 -0.84033516
## 643 -0.5339449 -0.6125297 2.18819907 -1.48006562 0.66641609
## 653 -0.5339449 -0.6125297 -1.16046308 -2.17233748 -0.70335777
## 654 1.8728523 -0.6125297 -0.15586443 0.94288592 -1.38824471
## 660 -0.5339449 -0.6125297 -0.26748651 -0.85702094 -0.15544823
## 4 -0.5339449 -0.6125297 0.62549007 1.35824904 0.66641609
## 121 -0.5339449 1.6325740 -0.49073065 -0.71856656 -0.01847084
## 338 -0.5339449 1.6325740 -0.60235272 -0.44165782 0.66641609
## 496 -0.5339449 -0.6125297 -1.38370722 1.15056748 -1.38824471
## 580 -0.5339449 -0.6125297 0.29062385 -0.23397626 1.21432563
## 608 -0.5339449 -0.6125297 0.06737971 1.35824904 2.03618995
## 615 -0.5339449 -0.6125297 0.73711214 0.94288592 -0.42940300
## 616 -0.5339449 -0.6125297 -0.60235272 -0.09552188 1.48828040
## 623 -0.5339449 1.6325740 0.73711214 -1.27238406 -0.01847084
## 782 -0.5339449 -0.6125297 0.40224593 1.28902185 -0.01847084
## peak.vo2 interval cad bun sodium
## 249 -0.693924484 -1.049514102 1.210566 -0.641684402 -0.4769220790
## 318 -1.738238721 -1.235503717 1.210566 4.227534282 -0.1526089192
## 631 0.862694849 0.991835338 1.210566 -0.422390595 0.5500655214
## 883 -0.161915345 -0.369064288 -0.826060 -0.057513334 0.2721011650
## 1095 -1.088005328 0.102714249 1.210566 0.520623700 -0.7713182513
## 1405 -0.201323429 -0.369064288 -0.826060 -0.960977431 -2.4228010377
## 1554 -0.812148738 -0.913424139 -0.826060 4.546827311 -2.4228010377
## 1589 0.508022090 0.719655413 -0.826060 -0.561861145 -0.4769220790
## 1708 -1.620014468 -1.598410285 1.210566 0.954780740 -3.0714273572
## 1773 0.074533161 0.080032588 -0.826060 -1.040800688 -0.4769220790
## 1850 -1.107709371 -1.616555613 1.210566 3.588948225 -2.7471141974
## 1935 0.074533161 0.733264409 1.210566 -0.960977431 0.1717042406
## 2077 -0.693924484 -1.675527930 -0.826060 -0.242568117 0.8203305602
## 110 -1.541198299 -1.049514102 1.210566 2.152129597 -0.4769220790
## 116 0.173053372 0.447475488 -0.826060 -0.265705064 -0.5236517622
## 328 -0.654516400 -0.505154251 -0.826060 2.435139694 -0.7683400641
## 482 -1.186525539 -0.232974326 1.210566 1.825693057 -0.2163776266
## 723 0.862694849 0.855745376 -0.826060 -0.451209294 -0.0260617199
## 732 -0.457475978 -0.505154251 -0.826060 0.218509266 0.2691675741
## 737 -1.935279143 -1.979462180 -0.826060 0.813386626 0.0370445520
## 802 -1.541198299 -1.503147311 -0.826060 1.664010650 -0.0957450984
## 894 -0.989485117 -0.641244214 1.210566 0.436318974 0.3371765755
## 1204 -1.836758932 -1.593873952 1.210566 6.462585482 -1.7741747181
## 1564 -1.127413413 -1.185604064 1.210566 0.316194683 -0.4769220790
## 1632 -0.950077033 -0.641244214 1.210566 0.076724912 -0.4769220790
## 1794 -1.620014468 -1.339839355 1.210566 1.433720283 0.4960174004
## 2120 -1.974687227 -1.185604064 -0.826060 2.231952854 -4.0443668366
## 2127 -1.324453835 -1.299012367 -0.826060 1.034603997 -0.4769220790
## 3 -0.989485117 -1.253649046 -0.826060 0.371259290 0.1187684554
## 188 0.626246343 0.311385525 -0.826060 -0.466254413 0.4130236326
## 320 -1.068301286 -1.607482949 -0.826060 0.354270160 0.1496772962
## 539 0.173053372 -0.704752863 -0.826060 -0.162744860 -1.7741747181
## 632 -1.363861919 -1.807081561 -0.826060 0.635487712 0.1717042406
## 712 -0.398363851 -1.289939703 -0.826060 0.260539608 -0.4281594484
## 808 -0.772740653 -1.321694027 1.210566 1.671219097 -0.4785987099
## 1081 -0.871260864 -1.435102329 -0.826060 0.112358400 -0.4116439791
## 1232 -1.580606383 -1.457783990 -0.826060 4.626650568 -1.7741747181
## 1313 -1.068301286 -1.457783990 1.210566 0.197168570 0.1402657250
## 1674 -1.442678088 -1.602946617 1.210566 -0.322391374 -5.0173063159
## 1717 -1.344157877 -1.503147311 1.210566 3.349478454 -2.7471141974
## 1727 -1.541198299 -1.770790904 1.210566 -0.721507659 -0.4769220790
## 2104 -1.225933624 -0.931569467 1.210566 0.795134226 -0.4769220790
## 2154 -0.299843640 0.991835338 1.210566 -0.242568117 0.1717042406
## 2181 -1.186525539 -0.369064288 1.210566 2.152129597 -0.8012352387
## 2197 -1.817054890 -1.553046964 -0.826060 0.316194683 -0.8012352387
## 445 -1.403270004 -0.831770162 -0.826060 0.236371426 0.8203305602
## 777 -0.831852780 -1.185604064 1.210566 0.510033180 -0.4642852426
## 800 -1.028893202 -1.185604064 -0.826060 -0.801330916 -2.7471141974
## 904 0.842990807 1.300305920 -0.826060 -0.414540732 0.6962904638
## 906 -1.166821497 -0.845379158 1.210566 0.482846452 0.1875555555
## 933 -0.536292147 -0.641244214 1.210566 0.542034175 -0.1369259002
## 1155 0.724766554 0.447475488 1.210566 -0.280619464 0.3791838408
## 1162 -0.950077033 -1.185604064 -0.826060 1.912659826 2.1175831993
## 1424 -1.639718510 -1.489538314 1.210566 0.795134226 2.1175831993
## 1664 -0.339251725 -0.096884363 1.210566 0.015762907 -0.3555959421
## 2000 -0.536292147 -0.641244214 -0.826060 1.912659826 -0.8012352387
## 2035 -1.048597244 -0.958787460 1.210566 0.711284228 -0.1695319950
## 186 -0.871260864 -0.641244214 1.210566 -0.322391374 1.1446437200
## 375 0.330685710 0.547274793 -0.826060 0.135608416 0.0911609149
## 792 -0.812148738 -1.117559083 -0.826060 1.135552767 -0.4433520833
## 957 -1.127413413 -1.035905106 1.210566 0.628917596 0.0312427841
## 1014 -1.028893202 -0.369064288 1.210566 1.163037244 -0.1880021872
## 1112 -1.462382130 -0.913424139 1.210566 4.467004054 -2.0984878779
## 1253 -1.304749793 -0.831770162 1.210566 -0.082921602 -0.4769220790
## 1420 -0.910668949 -0.672998538 1.210566 -0.522383913 0.3985394696
## 1988 -1.166821497 -1.276330706 -0.826060 -0.082921602 -0.8012352387
## 88 -0.339251725 0.796773058 1.210566 0.275320240 0.2452810050
## 117 -0.122507261 -0.346382628 -0.826060 6.143292453 -0.1526089192
## 229 -0.831852780 -1.185604064 -0.826060 1.513543540 -1.7741747181
## 512 -1.068301286 -1.185604064 -0.826060 -0.111482702 -0.2401128517
## 583 -1.383565961 -1.185604064 1.210566 5.584529653 -2.0984878779
## 648 -0.516588105 -0.096884363 -0.826060 0.399078459 0.2202030067
## 1058 -1.147117455 -0.759188848 -0.826060 -0.162744860 -2.4228010377
## 1203 -0.142211303 -0.314628303 -0.826060 -0.881154174 1.1446437200
## 1225 -0.457475978 -0.913424139 -0.826060 0.954780740 1.7932700395
## 1415 -1.048597244 -0.976932788 1.210566 0.076724912 2.7662095189
## 1438 -1.442678088 -0.641244214 1.210566 1.114427254 -1.7741747181
## 1442 -0.969781075 -1.072195762 -0.826060 -0.242568117 0.1717042406
## 1824 -0.221027472 -0.096884363 1.210566 1.353897026 -0.8012352387
## 2178 -1.541198299 -1.444174994 1.210566 0.795134226 -2.4228010377
## 200 -1.580606383 -2.101943146 -0.826060 0.586570321 -0.3542692321
## 460 -1.403270004 -1.462320322 -0.826060 0.528913747 -1.0238251825
## 490 -0.221027472 -1.040441438 1.210566 0.829606155 -0.0684988785
## 505 -1.048597244 -0.777334176 -0.826060 0.455416733 0.6338229461
## 636 0.330685710 0.719655413 -0.826060 -0.194418247 0.4703422378
## 877 -1.954983185 -1.562119628 1.210566 0.912571268 -0.1396135352
## 885 -0.201323429 -0.913424139 -0.826060 -0.960977431 -0.8012352387
## 2214 -0.753036611 -1.299012367 -0.826060 3.908241254 -2.0984878779
## 157 -2.171727649 -1.630164609 -0.826060 1.753013311 -0.1526089192
## 170 -0.831852780 -0.096884363 -0.826060 1.556737585 -0.4113112792
## 317 -1.009189160 -0.464327262 1.210566 3.349478454 -1.1255483985
## 399 -0.004283007 0.175295562 -0.826060 0.290414853 0.2213276210
## 489 5.355216470 0.447475488 1.210566 -0.003098345 0.8203305602
## 552 -1.403270004 -1.235503717 -0.826060 0.339559283 0.1709368249
## 652 -1.442678088 -1.670991598 -0.826060 -0.058665973 0.4744651953
## 876 -0.280139598 -0.473399927 -0.826060 1.277877126 -0.3949955998
## 1573 0.389797836 0.175295562 1.210566 -0.561861145 -0.8012352387
## 1993 -0.221027472 -0.800015837 1.210566 -0.082921602 0.1717042406
## 2040 -1.639718510 -1.689136926 -0.826060 0.316194683 -2.4228010377
## 10 -0.240731514 -0.219365330 -0.826060 1.194250512 0.4960174004
## 238 -0.713628527 -1.457783990 1.210566 0.037792156 0.2040312510
## 360 -0.792444695 -0.913424139 -0.826060 1.470794546 -0.6624627014
## 416 -1.166821497 -1.085804759 -0.826060 0.677888864 0.3353765597
## 733 -1.521494257 -1.575728624 -0.826060 0.200894842 -0.0938702472
## 967 -1.403270004 -1.253649046 1.210566 0.532774962 -0.2344276777
## 968 -1.048597244 -0.777334176 -0.826060 0.550056088 0.2718885453
## 1134 -0.890964906 -0.232974326 -0.826060 -0.082921602 0.8203305602
## 1135 -0.477180020 0.007451275 1.210566 0.344821235 0.2421125595
## 1183 -0.457475978 -0.164929344 -0.826060 -0.561861145 0.8203305602
## 1184 -0.299843640 -0.228437994 -0.826060 -0.402214631 -0.4769220790
## 1686 0.192757414 0.447475488 1.210566 -1.200447202 -1.1255483985
## 2069 -0.102803218 -1.639237273 1.210566 1.606841534 -0.0008356013
## 2204 -1.265341708 -1.004150781 -0.826060 0.635487712 0.4960174004
## 38 1.158255482 1.808375114 -0.826060 -0.697277880 0.4532171441
## 392 0.113941246 0.002914943 -0.826060 0.236371426 0.1717042406
## 443 -0.280139598 0.066423592 -0.826060 4.227534282 -0.4769220790
## 532 -0.516588105 -0.210292665 -0.826060 0.393329299 0.0140374489
## 647 -1.304749793 -1.330766691 1.210566 1.368353004 -0.2158901904
## 724 0.448909963 -0.096884363 -0.826060 -0.272063857 -0.2553604284
## 1128 -0.654516400 -0.332773632 1.210566 -0.721507659 0.8203305602
## 1337 -0.122507261 0.447475488 -0.826060 -0.082921602 0.1717042406
## 1593 0.567134216 0.447475488 1.210566 0.134771571 -0.4105952185
## 1777 -0.240731514 -0.346382628 1.210566 0.555664455 -0.8012352387
## 8 -0.063395134 -0.096884363 -0.826060 0.171513492 0.1413939586
## 222 -0.221027472 0.030132935 1.210566 0.076724912 -2.4228010377
## 228 2.123753550 2.080555039 -0.826060 -0.047034713 0.0419669220
## 313 -0.910668949 -1.339839355 1.210566 0.316194683 -1.7741747181
## 342 -0.280139598 0.039205600 1.210566 0.227726503 0.0503992198
## 350 -1.206229582 -0.763725180 1.210566 1.302331740 -0.0930863337
## 459 1.572040368 1.808375114 1.210566 -1.429911809 0.5503892475
## 573 -0.457475978 -0.990541785 -0.826060 -0.108964250 0.3513907900
## 771 -0.772740653 -0.917960471 -0.826060 0.289174292 0.0106896095
## 788 -0.634812358 -0.232974326 -0.826060 1.814840500 -0.6204215973
## 789 -1.462382130 -0.976932788 1.210566 0.027261929 -0.3501481913
## 886 -0.004283007 -0.096884363 1.210566 0.130746849 -0.1644008873
## 1029 -1.383565961 -1.367057348 1.210566 0.954780740 -1.1255483985
## 1067 -1.560902341 -2.065652490 -0.826060 2.710892397 -4.6929931561
## 1143 -0.339251725 0.356748846 -0.826060 -1.040800688 -0.4769220790
## 1251 -1.245637666 -1.471392986 1.210566 0.246820233 0.0213235461
## 1360 -1.344157877 -1.049514102 1.210566 0.316194683 -1.4498615583
## 1547 -0.930372991 -1.049514102 -0.826060 1.114427254 -1.1255483985
## 1906 1.099143356 0.284167532 -0.826060 -0.561861145 -1.4498615583
## 2187 -0.496884062 -0.595880893 -0.826060 0.316194683 -0.1526089192
## 2193 0.113941246 -0.641244214 -0.826060 -0.482037888 -0.4769220790
## 2199 0.212461457 0.161686566 -0.826060 -1.040800688 -0.4769220790
## 74 -1.600310426 -1.525828971 -0.826060 -0.242568117 0.4960174004
## 241 0.980919102 2.080555039 -0.826060 -0.801330916 -1.4498615583
## 348 1.591744411 0.991835338 1.210566 -0.107494877 0.3927772290
## 442 -0.615108316 -1.457783990 1.210566 0.795134226 -0.4769220790
## 470 -0.102803218 0.125395909 1.210566 0.635487712 0.1717042406
## 474 0.488318047 1.400105226 -0.826060 -1.280270459 -1.1255483985
## 524 -1.462382130 -1.090341091 1.210566 0.296946815 -0.4064008375
## 711 -1.639718510 -1.321694027 1.210566 0.824586506 -0.0872079014
## 1299 -0.102803218 -0.423500274 1.210566 -0.322391374 -0.4769220790
## 1515 -0.930372991 -1.049514102 -0.826060 -0.402214631 0.1717042406
## 1624 -0.969781075 -0.641244214 1.210566 2.631069140 -1.7741747181
## 1736 -0.063395134 -0.323700968 -0.826060 -0.482037888 -0.1526089192
## 2044 0.173053372 0.515520469 -0.826060 1.673190054 -0.4769220790
## 2067 1.709968664 1.259478931 1.210566 -0.282755899 0.3309464127
## 2183 -0.457475978 0.039205600 1.210566 -0.641684402 -0.1526089192
## 2185 -0.319547683 -0.568662900 1.210566 0.475841197 -0.8012352387
## 2189 0.389797836 0.647074099 1.210566 0.156548169 -0.1526089192
## 2190 0.015421035 0.461084484 -0.826060 0.156548169 -0.1526089192
## 2191 -1.363861919 -0.908887807 1.210566 0.236371426 0.4960174004
## 2192 -0.280139598 -0.976932788 -0.826060 -0.242568117 0.1717042406
## 7 -2.014095312 -1.616555613 -0.826060 -1.519740231 -1.1255483985
## 62 -1.206229582 -1.943171523 1.210566 2.231952854 -1.4498615583
## 169 -0.831852780 -1.530365303 1.210566 -0.641684402 -1.1255483985
## 305 0.547430174 -0.096884363 1.210566 -1.200447202 0.4960174004
## 327 -0.871260864 -0.913424139 1.210566 0.236371426 -0.8012352387
## 542 1.414408031 2.080555039 -0.826060 -1.107436383 0.3206564199
## 613 -0.516588105 -0.854451822 1.210566 0.085143442 0.4453828667
## 635 0.842990807 0.719655413 -0.826060 -0.439063871 0.7936219834
## 812 -1.580606383 -0.995078117 -0.826060 -0.402214631 2.1175831993
## 903 -1.009189160 -0.641244214 1.210566 6.143292453 -3.0714273572
## 1355 -1.856462974 -1.661918934 -0.826060 -0.162744860 1.4689568797
## 1422 0.035125077 -0.160393012 1.210566 -0.322391374 0.4960174004
## 1509 1.177959524 0.787700394 -0.826060 1.912659826 0.4960174004
## 1601 -0.102803218 -0.577735565 1.210566 -0.561861145 0.8203305602
## 2175 -0.339251725 0.175295562 -0.826060 -0.801330916 0.4960174004
## 2177 1.059735271 1.400105226 -0.826060 -0.561861145 -1.4498615583
## 2184 -0.358955767 -0.505154251 1.210566 -0.322391374 1.1446437200
## 2186 -1.088005328 -1.534901635 1.210566 -1.200447202 -1.4498615583
## 2188 -0.181619387 -0.301019307 1.210566 -0.082921602 0.1717042406
## 174 -0.753036611 -0.641244214 1.210566 -0.561861145 -0.4769220790
## 253 -1.127413413 -0.641244214 -0.826060 -0.402214631 0.4960174004
## 286 -1.166821497 -0.899815143 1.210566 0.156548169 -1.4498615583
## 492 -0.102803218 0.447475488 -0.826060 1.099300605 0.0073406156
## 534 -1.245637666 -1.185604064 1.210566 0.521098798 -0.5511382170
## 607 -0.378659809 -1.144777076 -0.826060 -0.402214631 -0.1526089192
## 614 -0.083099176 0.447475488 1.210566 0.196920409 0.1757106692
## 663 -1.009189160 -1.185604064 1.210566 0.242082638 -0.3787407194
## 964 -1.186525539 -1.321694027 1.210566 0.567482844 -0.1343210363
## 1066 -0.831852780 -0.482472591 1.210566 -0.082921602 -2.4228010377
## 1433 0.035125077 0.583565450 1.210566 -1.280270459 1.4689568797
## 1524 -0.634812358 -1.543974299 -0.826060 1.353897026 0.8203305602
## 1555 -0.753036611 -0.096884363 1.210566 -0.561861145 1.4689568797
## 1675 0.094237203 -0.096884363 1.210566 0.442095989 -0.8498143178
## 1907 -0.812148738 -0.709289195 1.210566 3.588948225 -3.0714273572
## 2021 -1.560902341 -1.439638661 -0.826060 -0.881154174 0.4960174004
## 2113 -0.083099176 -0.096884363 -0.826060 -0.801330916 0.4960174004
## 2129 -1.068301286 -0.641244214 1.210566 4.387180796 0.1717042406
## 2170 0.921806976 0.021060271 1.210566 -0.322391374 0.8203305602
## 2172 0.724766554 1.082561980 1.210566 -0.645880293 0.6193139407
## 2173 0.251869541 0.197977223 -0.826060 -0.801330916 2.4418963591
## 2198 -0.378659809 -0.205756333 1.210566 0.715310969 -3.0714273572
## 2200 0.724766554 0.765018734 -0.826060 0.555664455 -2.7471141974
## 2201 -0.457475978 -0.432572938 1.210566 -0.554071588 -0.6419631585
## 1 -0.851556822 -0.577735565 1.210566 -0.003098345 0.4960174004
## 46 -0.023987050 0.266022204 -0.826060 -0.402214631 -1.1255483985
## 103 -0.358955767 -0.096884363 1.210566 1.230185693 -0.1831694217
## 220 -1.147117455 -1.185604064 1.210566 -0.801330916 0.1717042406
## 236 -0.102803218 -0.278337647 1.210566 0.076724912 -1.1255483985
## 514 -0.615108316 -0.890742479 -0.826060 0.112994814 0.1535050736
## 638 -0.851556822 -0.777334176 1.210566 0.633053740 -0.0363090072
## 1520 -1.580606383 -1.426029665 1.210566 2.790715654 0.4960174004
## 1722 -1.895871058 -1.897808202 1.210566 -0.881154174 -0.8719009650
## 1877 -0.950077033 -0.913424139 -0.826060 1.194250512 -1.4498615583
## 2145 -0.023987050 0.447475488 -0.826060 0.076724912 1.1446437200
## 2146 -0.831852780 -0.119566024 1.210566 -0.641684402 0.1717042406
## 172 0.468614005 -0.369064288 1.210566 -0.061798463 -0.0192864613
## 269 -0.890964906 -1.185604064 1.210566 0.316194683 2.7662095189
## 270 -1.147117455 -1.321694027 1.210566 -0.561861145 -0.8012352387
## 291 -0.792444695 -0.024303050 1.210566 0.156548169 -1.1255483985
## 343 -0.890964906 -0.096884363 -0.826060 1.912659826 -0.4769220790
## 707 -0.792444695 -1.249112714 -0.826060 0.924928429 -0.0532348753
## 745 -0.516588105 -0.641244214 1.210566 1.219598313 -0.5806686523
## 919 -1.344157877 -1.376130012 -0.826060 -0.143063008 -0.3378123273
## 1293 -1.797350848 -0.913424139 1.210566 -0.561861145 1.7932700395
## 1338 -0.358955767 -0.641244214 -0.826060 0.167789220 -0.1338320596
## 1952 0.448909963 0.297776529 1.210566 -0.003098345 1.1446437200
## 1975 2.044937381 0.769555066 -0.826060 -0.162744860 -0.1526089192
## 2108 0.330685710 -0.096884363 -0.826060 -0.960977431 0.4960174004
## 2156 1.788784833 2.311907976 -0.826060 -0.249306469 0.2373580572
## 2164 -0.142211303 0.991835338 -0.826060 -0.003098345 -1.1255483985
## 2171 -0.595404273 -0.096884363 1.210566 0.475841197 0.8203305602
## 2195 -0.358955767 0.025596603 1.210566 -0.881154174 -0.4769220790
## 237 0.350389752 0.447475488 -0.826060 -0.242568117 -0.4769220790
## 363 -0.122507261 -0.096884363 1.210566 0.148096582 -0.1075592073
## 411 -0.516588105 -0.301019307 -0.826060 -0.043006890 0.2046700344
## 806 1.158255482 0.991835338 -0.826060 0.749173811 0.1269233390
## 1854 -0.753036611 -1.521292639 -0.826060 -0.322391374 -0.1526089192
## 1879 -0.083099176 -0.255655986 1.210566 1.433720283 -0.1526089192
## 2015 -0.575700231 0.447475488 1.210566 0.236371426 -0.1526089192
## 2018 0.409501879 -0.164929344 1.210566 1.912659826 -0.8012352387
## 2056 0.803582723 0.579029118 -0.826060 -0.402214631 -1.1255483985
## 2081 0.035125077 0.719655413 -0.826060 -1.200447202 -0.1526089192
## 2132 0.133645288 0.311385525 1.210566 -0.322391374 -0.1526089192
## 2158 -0.575700231 -0.641244214 1.210566 2.391599368 0.8203305602
## 2159 -1.935279143 -1.779863568 -0.826060 -0.003098345 0.4960174004
## 2163 -0.378659809 -0.768261512 -0.826060 -0.402214631 0.8203305602
## 2165 -1.422974046 -1.185604064 -0.826060 0.076724912 -0.1526089192
## 2168 -0.910668949 -0.641244214 -0.826060 -0.162744860 -0.4769220790
## 2169 -1.679126594 -1.267258042 -0.826060 0.874957483 1.1446437200
## 2196 -1.225933624 -0.577735565 1.210566 0.954780740 -1.4498615583
## 161 0.173053372 0.279631200 -0.826060 -0.003098345 -0.8012352387
## 180 -1.383565961 -1.185604064 1.210566 0.316194683 -1.4498615583
## 367 -0.516588105 -0.369064288 -0.826060 -0.402214631 0.8203305602
## 685 -0.181619387 -0.301019307 -0.826060 1.387554577 0.0702586529
## 990 -0.831852780 -0.641244214 -0.826060 1.102577259 0.0859397163
## 1086 -1.304749793 -0.437109270 1.210566 0.735162977 -0.0575947403
## 1100 -1.068301286 -0.804552169 -0.826060 -0.045075490 0.2656275078
## 1969 -0.477180020 -0.641244214 1.210566 2.311776111 -0.4769220790
## 2135 1.138551440 0.583565450 -0.826060 -1.120623945 2.1175831993
## 2153 -0.536292147 -1.289939703 1.210566 -0.641684402 0.4960174004
## 2155 1.709968664 1.740330133 1.210566 -0.561861145 -0.1526089192
## 2166 -1.462382130 -1.643773605 -0.826060 0.316194683 1.1446437200
## 223 0.133645288 0.288703864 1.210566 0.316194683 -0.8012352387
## 681 -1.028893202 -0.836306494 1.210566 -0.265726415 -0.0758862944
## 963 -1.048597244 -0.096884363 1.210566 0.939452235 -0.8478268554
## 1192 0.330685710 0.447475488 -0.826060 -0.368547057 -0.3258421718
## 1500 1.453816115 2.624914890 1.210566 -1.200447202 0.8203305602
## 1522 -1.521494257 -1.607482949 1.210566 1.114427254 0.8203305602
## 1523 -0.260435556 -1.158386072 1.210566 0.316194683 0.4960174004
## 1545 -0.299843640 -0.369064288 1.210566 1.832836569 -0.4769220790
## 1560 -1.580606383 -1.729963915 -0.826060 0.316194683 1.1446437200
## 2019 2.182865676 0.447475488 1.210566 -0.003098345 -0.4769220790
## 2095 -1.560902341 -1.457783990 -0.826060 -0.721507659 1.7932700395
## 2121 -1.639718510 -1.258185378 -0.826060 -0.960977431 0.1717042406
## 2147 -1.344157877 -1.267258042 -0.826060 0.156548169 -1.7741747181
## 2151 -0.536292147 -0.913424139 1.210566 -0.242568117 0.8203305602
## 2157 0.310981668 -0.133175020 1.210566 -1.040800688 0.1717042406
## 2231 -0.004283007 0.447475488 -0.826060 -0.801330916 -0.8012352387
## 112 -1.639718510 -1.471392986 -0.826060 -0.003098345 -2.0984878779
## 176 -0.753036611 -0.986005453 1.210566 -0.162744860 0.8203305602
## 219 -0.733332569 -0.096884363 -0.826060 1.034603997 1.7932700395
## 382 -0.772740653 -0.890742479 1.210566 0.738402573 0.0698343605
## 684 -0.063395134 -0.709289195 -0.826060 -0.244598004 0.0475512670
## 1268 1.217367609 1.794766118 -0.826060 -1.120623945 0.4960174004
## 1617 0.074533161 -0.908887807 -0.826060 0.156548169 -0.1526089192
## 1786 -0.812148738 -0.369064288 1.210566 0.874957483 -1.1255483985
## 2143 -0.023987050 0.315921857 -0.826060 -0.721507659 -1.4498615583
## 2148 0.685358469 1.100707308 -0.826060 0.236371426 0.4960174004
## 2152 -1.659422552 -2.052043493 -0.826060 0.475841197 -1.4498615583
## 98 -0.122507261 -0.096884363 -0.826060 -0.641684402 -1.4498615583
## 99 -0.753036611 -1.185604064 1.210566 0.621600583 -0.6587949953
## 209 -0.969781075 -1.081268426 -0.826060 -0.881154174 1.1446437200
## 254 -1.265341708 -0.232974326 1.210566 0.644350596 -0.2401691168
## 331 0.350389752 0.107250581 -0.826060 -0.322391374 0.1717042406
## 357 -0.516588105 -0.573199232 1.210566 0.289683943 -0.0970827377
## 410 0.153349330 0.447475488 -0.826060 0.396017940 0.4960174004
## 1426 -0.753036611 -0.709289195 1.210566 3.429301711 1.4689568797
## 1709 0.626246343 0.057350928 1.210566 -0.242568117 -0.4769220790
## 1746 -0.358955767 -0.096884363 -0.826060 0.156548169 -0.1526089192
## 1796 0.192757414 0.447475488 -0.826060 -0.322391374 0.1717042406
## 2118 0.310981668 0.610783443 1.210566 -0.641684402 1.1446437200
## 2126 -1.817054890 -1.870590210 1.210566 0.283728832 -0.8044480276
## 2134 -0.792444695 -1.031368773 1.210566 3.509124968 -0.8012352387
## 2136 -1.028893202 -0.913424139 1.210566 0.236371426 0.4960174004
## 2137 1.276479735 0.338603517 -0.826060 -0.082921602 0.4960174004
## 2138 -1.324453835 -1.185604064 -0.826060 0.715310969 -4.0443668366
## 2141 -1.462382130 -1.063123098 -0.826060 0.076724912 -0.8012352387
## 2142 2.241977803 0.687901088 1.210566 -0.801330916 -0.4769220790
## 2144 0.389797836 0.129932241 -0.826060 -0.242568117 -0.4769220790
## 2220 -1.915575101 -1.693673258 1.210566 0.555664455 -0.8012352387
## 64 -0.122507261 -0.096884363 -0.826060 -0.561861145 -0.8012352387
## 261 -1.442678088 -1.262721710 1.210566 0.316194683 -1.1255483985
## 478 -1.028893202 -0.745579852 1.210566 0.839224037 -0.4113842692
## 485 -0.201323429 0.447475488 1.210566 -0.322391374 -1.7741747181
## 604 -1.186525539 -0.954251128 1.210566 1.042995453 -0.2435903547
## 828 -0.851556822 -0.981469120 1.210566 1.005644671 -0.4115125026
## 1392 -0.477180020 -0.641244214 1.210566 0.555664455 0.1717042406
## 1463 0.113941246 -0.242046990 -0.826060 0.715310969 0.4960174004
## 1647 2.576946520 1.536195189 -0.826060 -0.402214631 -0.8012352387
## 1881 -1.068301286 -1.394275341 1.210566 0.156548169 1.7932700395
## 2124 -1.915575101 -1.480465650 -0.826060 2.391599368 -2.4228010377
## 2131 1.177959524 1.536195189 -0.826060 0.795134226 1.1446437200
## 2140 -0.930372991 -1.049514102 1.210566 -0.003098345 -1.7741747181
## 2213 -0.772740653 0.447475488 -0.826060 -0.082921602 -1.4498615583
## 159 -0.161915345 -0.232974326 1.210566 0.221057046 -0.0187249001
## 454 -1.068301286 -1.049514102 1.210566 -0.003098345 -0.8012352387
## 458 -0.221027472 -0.096884363 -0.826060 0.277666878 -0.9077990395
## 475 -1.009189160 -0.437109270 1.210566 2.311776111 -3.7200536768
## 493 0.429205921 0.415721163 1.210566 0.032624811 0.0963645391
## 939 -0.713628527 -0.505154251 1.210566 0.465122999 0.0258141937
## 1150 -1.836758932 -1.480465650 -0.826060 0.782422572 -0.5011570447
## 1213 -0.753036611 -0.641244214 1.210566 -1.040800688 0.1717042406
## 1264 -1.225933624 -0.777334176 1.210566 -0.881154174 0.4960174004
## 1300 -1.245637666 -1.049514102 1.210566 1.194250512 0.8203305602
## 1562 -1.521494257 -0.836306494 -0.826060 5.345059882 1.4689568797
## 1661 -0.477180020 -0.800015837 -0.826060 0.076724912 -0.8012352387
## 1776 -1.462382130 -0.595880893 1.210566 -0.721507659 1.4689568797
## 2112 -1.225933624 -0.777334176 1.210566 2.950362168 0.4960174004
## 2115 -0.890964906 -0.654853210 1.210566 -0.082921602 1.4689568797
## 2122 -1.324453835 -1.534901635 -0.826060 1.753013311 -1.1255483985
## 2123 0.251869541 0.175295562 1.210566 -0.561861145 0.4960174004
## 2125 1.670560579 1.481759204 1.210566 -0.561861145 0.4960174004
## 2128 0.094237203 0.742337073 -0.826060 -1.040800688 -1.1255483985
## 45 0.508022090 0.991835338 -0.826060 1.274073769 0.1717042406
## 179 0.980919102 1.277624260 -0.826060 -0.641684402 0.1717042406
## 306 -0.280139598 -0.096884363 -0.826060 -0.003098345 -0.8012352387
## 1307 0.074533161 0.583565450 1.210566 0.396017940 0.8203305602
## 1872 -0.358955767 -0.096884363 1.210566 0.076724912 -0.1526089192
## 2082 -1.186525539 -0.641244214 1.210566 1.832836569 -0.4769220790
## 2117 3.010435449 2.284689983 -0.826060 -0.402214631 0.1717042406
## 2119 -1.186525539 -1.185604064 1.210566 -0.402214631 0.8203305602
## 304 -1.048597244 -0.972396456 -0.826060 0.635487712 -0.4769220790
## 334 -0.437771936 0.175295562 -0.826060 0.555664455 -0.1526089192
## 513 -0.418067894 0.175295562 1.210566 0.136799908 -0.0679441797
## 598 -0.950077033 -0.595880893 1.210566 -0.561861145 0.1717042406
## 909 -0.418067894 -0.323700968 1.210566 1.251675395 -1.4763433656
## 1030 -0.555996189 -0.369064288 1.210566 1.434263435 -0.5668050074
## 1238 -0.615108316 -0.505154251 1.210566 1.114427254 -1.7741747181
## 1700 -0.043691092 -0.301019307 -0.826060 0.236371426 1.4689568797
## 2017 -1.206229582 -0.940642132 -0.826060 0.076724912 0.1717042406
## 2087 0.350389752 0.225195215 1.210566 -0.082921602 -2.4228010377
## 2106 0.015421035 0.383966838 1.210566 -0.561861145 -0.1526089192
## 2109 0.645950385 0.356748846 -0.826060 -0.847179108 0.8318082494
## 2110 1.059735271 1.422786887 1.210566 -1.360093716 0.4960174004
## 2111 0.882398891 0.696973752 -0.826060 -0.402214631 -0.8012352387
## 2114 -0.043691092 -0.096884363 1.210566 -0.960977431 -0.1526089192
## 40 3.700076926 3.713634591 -0.826060 -0.641684402 -0.8012352387
## 95 -0.910668949 -0.641244214 1.210566 0.874957483 -0.4769220790
## 234 -0.004283007 0.093641585 1.210566 0.156548169 1.4689568797
## 358 -1.482086172 -1.426029665 -0.826060 1.353897026 -0.4769220790
## 450 0.232165499 0.039205600 -0.826060 -0.168284524 -0.0043269427
## 621 -0.043691092 -0.096884363 -0.826060 0.337002038 0.0808750409
## 934 -0.575700231 -0.641244214 1.210566 1.442347646 -0.1349011774
## 965 -0.654516400 -0.586808229 1.210566 0.440693162 0.3949135025
## 1033 -1.088005328 -0.981469120 1.210566 0.770364374 -0.0850651060
## 1068 -0.634812358 -0.042448378 -0.826060 2.072306340 0.8203305602
## 1120 -1.285045750 -1.230967385 1.210566 -0.721507659 1.1446437200
## 1234 -0.457475978 -0.555053904 1.210566 -0.721507659 0.4960174004
## 2011 5.434032639 0.719655413 -0.826060 -0.082921602 0.4960174004
## 2068 -0.378659809 -0.164929344 -0.826060 -0.881154174 0.1717042406
## 2078 2.005529297 -0.822697497 1.210566 1.513543540 -1.4498615583
## 2100 -1.324453835 -1.426029665 1.210566 -0.082921602 1.1446437200
## 2102 -0.437771936 -0.641244214 -0.826060 -0.881154174 0.8203305602
## 2105 -0.871260864 -1.117559083 -0.826060 -0.402214631 0.8203305602
## 2203 -1.127413413 -1.244576382 -0.826060 -0.322391374 1.1446437200
## 193 -0.457475978 -0.301019307 1.210566 1.433720283 -0.1526089192
## 387 -1.285045750 -0.913424139 1.210566 1.194250512 -0.4769220790
## 731 1.532632284 2.003437394 -0.826060 -0.561861145 -0.1526089192
## 1262 -0.575700231 0.075496256 -0.826060 -0.960977431 -0.8012352387
## 1342 -0.693924484 -0.940642132 -0.826060 -1.120623945 0.4960174004
## 1465 -0.575700231 -0.096884363 -0.826060 -0.082921602 -0.4769220790
## 2048 1.217367609 0.388503170 -0.826060 -0.242568117 -0.8012352387
## 2093 0.370093794 0.461084484 -0.826060 -1.120623945 -0.8012352387
## 2099 -0.595404273 -0.432572938 1.210566 -0.881154174 1.1446437200
## 2101 0.724766554 0.901108696 -0.826060 -0.322391374 -0.4769220790
## 2103 -0.555996189 -0.369064288 -0.826060 -0.482037888 -0.8012352387
## 2205 -1.679126594 -1.312621363 -0.826060 -0.641684402 0.4960174004
## 72 -0.831852780 -1.049514102 1.210566 2.631069140 0.8203305602
## 299 1.237071651 0.991835338 1.210566 -0.322391374 -0.4769220790
## 1043 0.113941246 0.447475488 1.210566 -0.561861145 0.1717042406
## 1049 -1.620014468 -1.757181908 -0.826060 1.600765377 -0.2124324673
## 1240 0.094237203 0.089105252 -0.826060 -0.721507659 1.1446437200
## 1561 -1.363861919 -1.013223445 -0.826060 1.673190054 -3.3957405170
## 1723 -0.339251725 0.111786913 1.210566 -1.040800688 -1.1255483985
## 1992 0.882398891 0.447475488 -0.826060 -0.960977431 -0.8012352387
## 2005 -0.181619387 0.388503170 -0.826060 -1.040800688 -0.8012352387
## 2088 0.389797836 0.964617346 1.210566 -0.960977431 0.1717042406
## 2094 -0.181619387 -0.355455292 -0.826060 -0.242568117 -1.1255483985
## 2097 -0.989485117 -0.999614449 -0.826060 -0.881154174 -1.7741747181
## 84 -0.792444695 -1.915953531 1.210566 -0.402214631 -2.0984878779
## 273 -0.457475978 0.175295562 1.210566 -0.322391374 0.8203305602
## 370 0.350389752 0.447475488 1.210566 -0.641684402 0.8203305602
## 418 -1.166821497 -0.809088501 1.210566 -0.641684402 0.8203305602
## 473 0.823286765 0.991835338 1.210566 0.684377392 -0.2940401109
## 646 -1.797350848 -1.661918934 -0.826060 0.475841197 0.4960174004
## 670 -0.299843640 -0.505154251 1.210566 0.155542761 0.1711875449
## 1381 0.271573583 0.061887260 1.210566 -0.561861145 1.1446437200
## 1512 -1.088005328 -1.185604064 1.210566 -0.561861145 -3.0714273572
## 1738 1.256775693 0.991835338 -0.826060 -0.482037888 0.4960174004
## 1928 -0.713628527 -0.573199232 -0.826060 -0.402214631 -0.1526089192
## 1973 0.192757414 -0.559590236 1.210566 -0.082921602 -0.8012352387
## 1976 -0.102803218 -0.346382628 -0.826060 3.988064511 -0.1526089192
## 2024 0.153349330 0.157150234 -0.826060 -0.881154174 -0.8012352387
## 2049 -0.083099176 0.107250581 -0.826060 -1.200447202 1.7932700395
## 2070 -0.713628527 -1.571192292 -0.826060 -0.801330916 0.4960174004
## 2079 1.217367609 0.991835338 -0.826060 -0.721507659 -1.7741747181
## 2080 0.035125077 -0.414427609 -0.826060 0.715310969 0.4960174004
## 2083 -1.048597244 -1.457783990 -0.826060 -0.721507659 0.8203305602
## 2085 -1.225933624 -1.625628277 1.210566 0.555664455 1.4689568797
## 2086 -0.772740653 -1.103950087 1.210566 -0.482037888 -0.1526089192
## 2090 -0.753036611 -0.496081587 1.210566 3.269655197 -1.1255483985
## 2091 -2.368768071 -0.641244214 1.210566 -0.402214631 0.4960174004
## 2092 1.256775693 0.969153678 -0.826060 0.156548169 -0.8012352387
## 75 -1.088005328 -0.800015837 -0.826060 -1.040800688 -0.1526089192
## 106 -0.063395134 -0.432572938 1.210566 1.034603997 -1.1255483985
## 413 -0.634812358 -1.126631747 1.210566 -0.801330916 0.4960174004
## 432 -0.536292147 -0.096884363 1.210566 0.635487712 0.4960174004
## 531 0.606542301 0.175295562 -0.826060 0.217410801 -0.6436338674
## 839 1.631152495 2.080555039 -0.826060 -0.809537998 0.4829681188
## 1062 -0.950077033 -0.641244214 1.210566 4.626650568 -0.4769220790
## 1163 0.941511018 0.823991051 -0.826060 -0.641684402 0.4960174004
## 1177 1.059735271 0.801309390 -0.826060 -0.162744860 -0.1526089192
## 1855 1.000623145 0.093641585 -0.826060 -0.561861145 -0.1526089192
## 2033 0.054829119 -0.260192318 -0.826060 -0.082921602 0.4960174004
## 2071 0.173053372 0.991835338 -0.826060 -0.561861145 1.1446437200
## 2073 -0.930372991 -1.362521016 1.210566 0.316194683 0.1717042406
## 2076 0.291277625 0.447475488 1.210566 0.446808031 -0.5367815091
## 2084 0.488318047 0.175295562 1.210566 -0.561861145 -0.1526089192
## 78 0.015421035 -0.319164636 -0.826060 -0.003098345 0.4960174004
## 264 0.941511018 0.447475488 -0.826060 -0.482037888 -0.4769220790
## 315 -0.950077033 -1.448711326 1.210566 -1.040800688 0.4960174004
## 439 0.232165499 0.256949540 -0.826060 0.646890272 -0.0959001946
## 529 -0.181619387 -0.037912046 1.210566 0.236371426 -1.1255483985
## 758 1.118847398 1.808375114 -0.826060 -0.960977431 -0.8012352387
## 1092 -0.536292147 -0.827233829 1.210566 2.072306340 1.1446437200
## 1130 0.251869541 -0.096884363 -0.826060 -0.082921602 -1.4498615583
## 1412 -0.418067894 -0.096884363 -0.826060 -0.641684402 0.8203305602
## 1614 -0.831852780 -0.963323792 1.210566 1.912659826 -1.7741747181
## 1802 -0.339251725 0.157150234 1.210566 0.076724912 0.1717042406
## 1970 -0.615108316 -0.586808229 1.210566 2.152129597 1.1446437200
## 1983 0.370093794 0.447475488 1.210566 -0.960977431 1.7932700395
## 2012 -0.142211303 -0.777334176 -0.826060 3.509124968 -2.0984878779
## 2053 1.512928242 1.141534297 -0.826060 -0.881154174 -0.4769220790
## 2057 1.611448453 0.719655413 -0.826060 -0.881154174 1.1446437200
## 2058 0.586838258 0.256949540 1.210566 -0.322391374 -0.1526089192
## 2059 -0.023987050 -0.632171550 -0.826060 -0.561861145 -1.7741747181
## 2074 0.015421035 -0.096884363 1.210566 -0.162744860 -4.6929931561
## 2075 -0.378659809 -1.553046964 -0.826060 -0.402214631 0.4960174004
## 2215 0.015421035 0.574492786 -0.826060 -0.402214631 1.4689568797
## 37 -0.615108316 -0.641244214 1.210566 -0.082921602 -0.4769220790
## 65 0.527726132 0.256949540 1.210566 -0.960977431 -0.1526089192
## 100 -0.004283007 1.871883763 1.210566 -0.274119100 -0.0679787904
## 108 -0.004283007 0.175295562 1.210566 -0.504028056 0.3537284717
## 173 1.473520157 0.742337073 1.210566 -0.082921602 0.8203305602
## 207 0.783878680 0.991835338 -0.826060 -0.322391374 -0.4769220790
## 824 -0.023987050 0.447475488 1.210566 0.593721355 0.1585724667
## 1141 -0.083099176 -0.096884363 -0.826060 3.668771482 -3.0714273572
## 1145 1.000623145 1.127925301 -0.826060 -0.493876817 0.1424350850
## 1618 -1.166821497 -0.573199232 -0.826060 -0.482037888 -0.1526089192
## 1812 -0.102803218 -0.777334176 1.210566 -0.721507659 0.1717042406
## 1856 -1.127413413 -0.913424139 -0.826060 0.076724912 1.1446437200
## 1937 -2.014095312 -1.185604064 1.210566 -0.082921602 -1.1255483985
## 2047 0.389797836 -0.078739035 -0.826060 -0.482037888 0.1717042406
## 2061 1.414408031 1.731257469 1.210566 -0.721507659 0.4960174004
## 2062 -1.620014468 -1.539437967 -0.826060 2.551245883 -1.7741747181
## 2063 -0.319547683 -0.228437994 1.210566 -0.960977431 -0.8012352387
## 2221 -0.240731514 -0.260192318 -0.826060 -0.361556468 -0.1183027877
## 295 1.138551440 0.774091398 -0.826060 -0.455177704 -0.0018161621
## 337 1.177959524 1.150606961 -0.826060 -0.833383176 0.5797224323
## 372 -1.186525539 -1.094877423 -0.826060 -0.026592274 -0.2362352825
## 546 -1.028893202 -1.675527930 -0.826060 1.453198658 -0.6786689932
## 874 -0.615108316 -0.505154251 1.210566 0.141269294 0.4290909562
## 972 0.862694849 0.311385525 1.210566 -0.130350434 -0.1847414325
## 1210 0.173053372 0.796773058 1.210566 -0.482037888 0.1717042406
## 1298 -0.615108316 -0.096884363 1.210566 -0.082921602 0.1717042406
## 1498 -1.403270004 -1.344375688 1.210566 0.236371426 0.4960174004
## 1979 0.468614005 -0.346382628 1.210566 0.236371426 0.4960174004
## 2066 0.232165499 0.334067185 -0.826060 0.236371426 -0.4769220790
## 383 1.296183778 0.787700394 -0.826060 -0.721507659 -0.4769220790
## 434 0.153349330 0.411184831 -0.826060 0.475841197 1.1446437200
## 843 0.567134216 1.400105226 1.210566 -0.130945083 0.2314500963
## 865 0.330685710 0.252413208 1.210566 -0.756211251 0.2907023570
## 922 -0.240731514 -1.081268426 1.210566 0.367266987 0.1857836121
## 1418 1.493224200 1.146070629 -0.826060 0.156548169 -0.1526089192
## 1485 0.823286765 0.719655413 -0.826060 -1.439916973 0.4960174004
## 1842 -0.753036611 -1.589337620 -0.826060 -0.242568117 0.1717042406
## 2037 -0.418067894 -1.258185378 1.210566 -0.162744860 0.4960174004
## 2052 0.685358469 0.860281708 -0.826060 -0.801330916 -0.1526089192
## 371 -0.280139598 -0.382673285 -0.826060 -0.027047315 -0.0753005427
## 376 -0.831852780 -0.369064288 -0.826060 0.353492154 -0.1871408796
## 377 -0.989485117 -1.457783990 1.210566 -0.242568117 0.1717042406
## 506 0.567134216 0.765018734 1.210566 0.169028487 0.3144963055
## 728 -0.181619387 -0.995078117 -0.826060 0.079403229 -0.0886462757
## 826 1.079439313 0.991835338 -0.826060 -0.626033285 0.4666304607
## 1642 1.000623145 0.098177917 -0.826060 -0.801330916 0.1717042406
## 1707 -0.772740653 -0.777334176 -0.826060 0.236371426 0.1717042406
## 1751 1.276479735 1.654139823 -0.826060 -0.561861145 0.1717042406
## 1864 0.389797836 -0.314628303 -0.826060 1.274073769 -2.0984878779
## 1895 -0.693924484 -0.995078117 -0.826060 3.269655197 -1.1255483985
## 1998 0.113941246 0.447475488 1.210566 -0.561861145 0.1717042406
## 31 -1.068301286 -1.367057348 1.210566 -0.003098345 -0.1526089192
## 35 -1.088005328 -0.727434523 -0.826060 0.076724912 -0.1526089192
## 90 0.015421035 0.447475488 -0.826060 -0.254537424 0.3710079676
## 312 -0.477180020 -0.437109270 1.210566 0.344606684 0.3136411598
## 457 1.572040368 1.536195189 1.210566 -0.881154174 0.8203305602
## 466 -1.304749793 -1.684600594 -0.826060 -0.162744860 -0.8012352387
## 870 -0.555996189 -1.335303023 1.210566 0.236371426 -1.4498615583
## 913 -1.422974046 -1.457783990 -0.826060 0.280713395 0.4239907167
## 1013 0.783878680 0.683364756 -0.826060 -0.488367890 -0.0755789850
## 1117 -0.398363851 -0.777334176 -0.826060 -0.242568117 0.4960174004
## 1306 0.074533161 0.447475488 -0.826060 -0.801330916 0.1717042406
## 1619 -0.477180020 -0.641244214 -0.826060 2.391599368 -1.1255483985
## 1756 -0.043691092 0.315921857 1.210566 -0.881154174 0.4960174004
## 2026 0.409501879 -0.505154251 1.210566 0.909516218 -0.0708582502
## 2028 -0.043691092 -1.185604064 1.210566 0.715310969 1.4689568797
## 2038 2.852803111 2.361807629 -0.826060 -0.589821891 0.6057748263
## 2039 1.315887820 0.447475488 -0.826060 -0.482037888 0.1717042406
## 2043 -0.733332569 -0.922496803 1.210566 -0.561861145 -0.1526089192
## 2227 -1.028893202 -1.022296109 1.210566 0.076724912 -1.7741747181
## 162 0.882398891 0.991835338 -0.826060 -0.881154174 0.4960174004
## 528 0.330685710 0.991835338 -0.826060 -0.324848109 0.3906334672
## 581 -0.654516400 0.039205600 -0.826060 0.963453208 -0.3621189305
## 600 0.724766554 0.538202129 1.210566 0.076724912 0.1717042406
## 601 -0.674220442 -0.468863594 1.210566 1.657170961 -0.4890657225
## 814 0.409501879 -0.437109270 -0.826060 0.127495253 -0.0829816923
## 1318 -0.437771936 -0.437109270 -0.826060 -0.402214631 -0.1526089192
## 1477 0.448909963 0.447475488 -0.826060 -0.881154174 -0.1526089192
## 1982 0.094237203 -0.641244214 1.210566 -0.721507659 0.1717042406
## 2029 0.173053372 -0.695680199 1.210566 -0.561861145 0.4960174004
## 2034 0.133645288 0.197977223 -0.826060 -0.610506048 0.1919833187
## 2041 1.138551440 -0.369064288 -0.826060 -0.641684402 0.8203305602
## 2042 0.724766554 1.195970282 1.210566 -0.641684402 -0.1526089192
## 2224 2.734578858 2.624914890 1.210566 -0.881154174 0.1717042406
## 2229 0.705062512 0.515520469 1.210566 -0.721507659 1.7932700395
## 612 -0.161915345 0.039205600 1.210566 0.797869086 -0.3379138471
## 703 0.370093794 -0.096884363 -0.826060 -1.120623945 -1.4498615583
## 916 -0.890964906 0.129932241 1.210566 0.173273751 0.4185602420
## 994 -1.206229582 -1.258185378 -0.826060 -0.605399202 0.8698942183
## 1188 0.921806976 -0.096884363 -0.826060 -0.689923455 0.1485326588
## 1216 -1.166821497 -0.895278811 1.210566 0.396017940 -1.1255483985
## 1890 -0.516588105 -0.999614449 -0.826060 -0.881154174 -0.8012352387
## 1991 -0.437771936 -1.376130012 -0.826060 0.076724912 -1.1255483985
## 2031 -0.319547683 -0.822697497 -0.826060 -0.641684402 0.4960174004
## 2032 0.724766554 -0.477936259 1.210566 -0.641684402 -0.1526089192
## 97 -0.102803218 -0.369064288 1.210566 0.421846156 -0.2204756283
## 201 1.887305044 0.660683096 -0.826060 -0.721507659 1.1446437200
## 341 -1.186525539 -1.040441438 1.210566 1.992483083 -4.0443668366
## 717 -0.713628527 1.313914916 -0.826060 0.068672165 0.1987962334
## 1364 -0.418067894 -1.049514102 -0.826060 -0.242568117 0.8203305602
## 1439 -0.221027472 0.447475488 -0.826060 -0.142065285 0.2860120299
## 1474 -0.280139598 -0.954251128 -0.826060 -0.402214631 -0.4769220790
## 1489 -0.693924484 -0.487008923 -0.826060 -0.801330916 0.4960174004
## 1669 -0.201323429 0.447475488 1.210566 -0.881154174 -0.4769220790
## 1687 -0.812148738 -1.185604064 -0.826060 0.076724912 0.4960174004
## 1704 -0.634812358 -0.967860124 1.210566 -0.082921602 -1.1255483985
## 1706 0.232165499 -0.232974326 -0.826060 -0.721507659 -0.8012352387
## 1780 1.256775693 1.037198659 -0.826060 -1.280270459 0.1717042406
## 1984 0.980919102 0.569956454 -0.826060 -0.482037888 -0.1526089192
## 2022 0.074533161 -0.096884363 1.210566 0.316194683 -0.1526089192
## 2025 0.803582723 0.175295562 -0.826060 -1.280270459 1.4689568797
## 2217 -0.221027472 -0.777334176 -0.826060 -0.003098345 0.4960174004
## 2222 1.650856537 1.127925301 -0.826060 -0.808847918 0.3685942897
## 135 -0.890964906 -0.641244214 -0.826060 -0.127557645 0.3383090090
## 166 -0.930372991 -0.232974326 -0.826060 1.593366797 -0.4769220790
## 468 -0.713628527 -1.072195762 -0.826060 -0.402214631 2.4418963591
## 626 -0.457475978 -0.096884363 1.210566 2.631069140 0.4960174004
## 1031 -1.107709371 -0.777334176 1.210566 0.054019104 -0.3933443367
## 1362 -1.186525539 -1.321694027 1.210566 0.715310969 0.8203305602
## 1510 -0.831852780 -0.981469120 -0.826060 -0.641684402 -1.4498615583
## 1625 0.941511018 0.855745376 -0.826060 -0.596816033 0.3204490346
## 1696 0.626246343 0.039205600 -0.826060 2.471422626 1.7932700395
## 1699 -0.989485117 -1.081268426 -0.826060 0.635487712 -0.8012352387
## 1705 2.616354605 2.021582722 -0.826060 -1.180611881 0.5630943907
## 1715 1.118847398 1.218651943 -0.826060 -0.482037888 0.8203305602
## 1861 1.493224200 1.291233256 1.210566 -0.721507659 0.1717042406
## 1941 -0.260435556 -0.473399927 -0.826060 -0.561861145 -0.1526089192
## 1964 -0.477180020 0.061887260 -0.826060 -0.641684402 -0.8012352387
## 2218 -0.713628527 -0.986005453 -0.826060 -1.126176037 -0.6106800116
## 2228 -0.575700231 -0.128638688 1.210566 0.926215870 -1.3497784783
## 2230 1.650856537 1.254942599 -0.826060 -1.200447202 1.4689568797
## 255 0.094237203 -0.500617919 1.210566 -0.561861145 0.4960174004
## 819 -0.713628527 -0.845379158 -0.826060 0.479908749 0.2608882288
## 917 -0.812148738 -0.232974326 1.210566 0.379722597 -0.2860813784
## 1085 0.527726132 0.855745376 1.210566 0.822797235 -0.0405476893
## 1187 -0.634812358 -0.641244214 -0.826060 -0.801330916 1.1446437200
## 1394 -0.063395134 0.107250581 -0.826060 -0.721507659 0.1717042406
## 1479 -1.620014468 -1.580264956 -0.826060 -0.322391374 -5.6659326355
## 1517 0.665654427 0.991835338 -0.826060 -0.561861145 0.1717042406
## 2009 -0.043691092 -0.251119654 1.210566 -0.801330916 -0.8012352387
## 2223 -0.969781075 -1.185604064 -0.826060 0.076724912 -1.4498615583
## 2225 0.645950385 0.991835338 -0.826060 -0.881154174 0.8203305602
## 2226 1.256775693 0.833063715 -0.826060 0.795134226 -0.8012352387
## 27 -0.437771936 -0.187611005 1.210566 -0.482037888 -2.7471141974
## 105 1.670560579 1.681357816 -0.826060 -1.280270459 -0.1526089192
## 147 0.547430174 0.039205600 1.210566 -0.653005850 0.7207017910
## 224 0.389797836 0.719655413 -0.826060 0.116536595 0.5283475404
## 274 -0.772740653 -1.303548699 -0.826060 -0.881154174 0.8203305602
## 356 -0.555996189 -1.135704411 1.210566 -0.402214631 0.1717042406
## 431 -0.950077033 -1.553046964 -0.826060 1.371373730 -0.6029988166
## 551 1.769080790 1.536195189 -0.826060 -0.402214631 0.8203305602
## 962 -0.418067894 -0.006157721 1.210566 1.042582192 -0.8361445894
## 997 -0.437771936 -0.505154251 -0.826060 -0.227139899 0.6541237597
## 1389 -0.122507261 -0.777334176 -0.826060 -0.881154174 0.4960174004
## 1831 0.567134216 -0.387209617 -0.826060 1.353897026 -1.1255483985
## 1996 -0.260435556 -0.840842826 1.210566 -0.641684402 -0.4769220790
## 1999 2.892211196 2.080555039 -0.826060 -0.322391374 -0.1526089192
## 2008 0.291277625 -1.131168079 -0.826060 -1.120623945 1.1446437200
## 2216 0.192757414 0.801309390 -0.826060 0.795134226 -0.8012352387
## 107 0.842990807 0.034669267 -0.826060 -0.322391374 -0.1526089192
## 148 -1.048597244 -1.049514102 -0.826060 -0.641684402 -1.7741747181
## 182 0.882398891 0.447475488 -0.826060 -0.561861145 0.1717042406
## 414 -1.698830637 -1.911417199 1.210566 1.034603997 -0.1526089192
## 511 -0.713628527 -0.378136953 1.210566 0.156548169 -0.1526089192
## 699 0.606542301 0.311385525 -0.826060 -0.828092763 0.0572998222
## 1017 -1.068301286 -0.641244214 1.210566 0.503396004 0.0203177051
## 1366 1.650856537 1.254942599 -0.826060 -0.482037888 -0.1526089192
## 1672 -0.753036611 -1.294476035 1.210566 -0.561861145 -1.1255483985
## 1931 1.040031229 0.583565450 -0.826060 -0.801330916 0.1717042406
## 1966 2.301089930 1.672285151 -0.826060 -0.634194809 -0.0170965367
## 1994 1.493224200 1.014516999 -0.826060 -1.200447202 -3.0714273572
## 2002 2.163161634 0.896572364 -0.826060 -0.561861145 -0.1526089192
## 2007 -0.851556822 -0.641244214 -0.826060 -0.591934456 0.3408738818
## 2 1.532632284 1.150606961 -0.826060 -0.402214631 -0.4769220790
## 143 0.153349330 -0.550517572 -0.826060 -0.003098345 -1.1255483985
## 160 4.172973938 4.144586140 -0.826060 -0.402214631 -0.1526089192
## 688 -0.398363851 -0.096884363 1.210566 0.733964084 -0.1376790267
## 776 -0.615108316 -0.709289195 -0.826060 -0.579552560 0.5030291125
## 850 -1.324453835 -0.641244214 -0.826060 2.551230473 -0.1486053835
## 1194 0.173053372 -0.355455292 1.210566 -0.162744860 -1.1255483985
## 1849 1.138551440 0.991835338 -0.826060 -0.385816358 -0.1809764097
## 5 0.192757414 0.356748846 1.210566 1.114427254 1.1446437200
## 55 -0.221027472 -0.210292665 -0.826060 -0.402214631 -0.4769220790
## 347 1.611448453 1.332060245 -0.826060 0.874957483 -0.1526089192
## 856 -0.280139598 -0.437109270 -0.826060 1.533315314 -0.0697576935
## 1972 0.330685710 0.447475488 -0.826060 0.396017940 1.7932700395
## 1977 0.350389752 0.175295562 -0.826060 1.753013311 -1.1255483985
## 1980 -0.733332569 -1.058586766 -0.826060 1.513543540 0.8203305602
## 1981 0.192757414 -0.913424139 -0.826060 -1.200447202 0.4960174004
## 1985 -0.023987050 -0.595880893 -0.826060 0.635487712 1.1446437200
## 1987 0.468614005 0.188904558 1.210566 -0.641684402 1.1446437200
## 1995 0.133645288 0.433866491 1.210566 1.753013311 0.4960174004
## 2207 0.035125077 0.538202129 -0.826060 -0.308227468 0.4833708866
## 244 -0.536292147 -0.232974326 1.210566 0.255367798 0.0425916756
## 308 -0.930372991 -1.775327236 -0.826060 -0.721507659 0.1717042406
## 560 0.547430174 0.991835338 -0.826060 -1.120623945 -0.1526089192
## 561 0.448909963 0.991835338 1.210566 -0.784868999 0.1531053090
## 1173 0.823286765 0.674292092 -0.826060 -0.003098345 0.8203305602
## 1190 -0.575700231 -1.090341091 -0.826060 1.593366797 -2.4228010377
## 1269 -0.102803218 -1.185604064 -0.826060 0.156548169 -0.1526089192
## 1453 0.133645288 -0.423500274 -0.826060 -0.482037888 2.4418963591
## 1543 0.409501879 1.127925301 -0.826060 -0.960977431 0.1717042406
## 1681 -0.240731514 -0.437109270 -0.826060 2.471422626 0.1717042406
## 1800 -0.575700231 -0.804552169 -0.826060 0.137761575 0.0602770592
## 1873 2.478426309 1.894565424 -0.826060 -0.710727120 0.4422842718
## 1971 1.237071651 1.717648472 -0.826060 -0.169128921 -0.1524381261
## 1974 -0.358955767 -1.680064262 1.210566 -0.162744860 -0.1526089192
## 130 0.567134216 -0.232974326 -0.826060 -0.482037888 -1.1255483985
## 721 -1.403270004 -1.729963915 1.210566 0.378871890 -0.4016198550
## 837 -0.280139598 -0.096884363 1.210566 0.558481515 -0.2003064923
## 1741 0.173053372 -0.423500274 -0.826060 -0.721507659 -0.1526089192
## 1860 -1.580606383 -1.593873952 1.210566 -0.003098345 0.4960174004
## 1957 0.350389752 0.175295562 -0.826060 -1.120623945 0.1717042406
## 1960 -0.142211303 0.175295562 1.210566 0.076724912 0.1717042406
## 1965 -1.403270004 -1.017759777 1.210566 -0.482037888 0.1717042406
## 1968 0.035125077 0.882963368 -0.826060 -0.162744860 0.1717042406
## 58 -0.043691092 0.039205600 1.210566 0.795134226 -0.8012352387
## 195 0.310981668 -0.509690583 1.210566 -0.721507659 1.1446437200
## 1228 0.291277625 0.175295562 -0.826060 -0.482037888 0.1717042406
## 1257 -2.033799354 -1.661918934 -0.826060 -0.202519686 0.2037192650
## 1592 0.744470596 -1.185604064 1.210566 1.194250512 -2.7471141974
## 1596 -0.812148738 -0.359991624 -0.826060 -0.482037888 1.1446437200
## 1750 -0.496884062 -0.981469120 -0.826060 -0.881154174 -0.1526089192
## 1837 -0.280139598 -0.369064288 -0.826060 0.236371426 -0.4769220790
## 1942 -0.181619387 0.447475488 1.210566 3.828417997 -2.7471141974
## 1961 -0.536292147 -0.323700968 -0.826060 -0.402214631 1.1446437200
## 1962 -0.319547683 0.243340544 1.210566 0.316194683 -1.4498615583
## 1963 0.330685710 -0.369064288 1.210566 -0.801330916 0.1717042406
## 422 2.044937381 2.080555039 -0.826060 -0.402214631 -0.4769220790
## 1401 -0.753036611 -0.641244214 -0.826060 2.311776111 -0.4769220790
## 1525 0.133645288 -0.641244214 -0.826060 -0.082921602 -0.1526089192
## 1646 -0.792444695 -0.672998538 -0.826060 0.715310969 -1.7741747181
## 1929 -0.615108316 -0.845379158 1.210566 -0.881154174 -0.8012352387
## 1940 -1.186525539 -1.185604064 1.210566 -0.003098345 -1.7741747181
## 1945 1.828192917 1.536195189 -0.826060 -0.003098345 -2.4228010377
## 1946 0.035125077 -0.505154251 -0.826060 -0.881154174 0.1717042406
## 1948 0.685358469 0.447475488 -0.826060 -0.960977431 -0.1526089192
## 1949 -0.339251725 -0.777334176 -0.826060 -0.402214631 -1.1255483985
## 1950 0.803582723 -1.416957001 1.210566 -0.641684402 -1.1255483985
## 1953 1.769080790 1.808375114 -0.826060 -1.120623945 -1.1255483985
## 1955 0.586838258 0.139004905 -0.826060 0.156548169 1.1446437200
## 202 -0.950077033 -1.729963915 -0.826060 -0.641684402 -5.6659326355
## 494 0.212461457 0.107250581 -0.826060 -0.721507659 -0.1526089192
## 740 -1.186525539 -1.049514102 -0.826060 1.236892489 -0.2671468929
## 780 -0.043691092 0.052814596 1.210566 0.036739866 0.3046969369
## 1069 3.266587997 2.897094815 -0.826060 -0.641684402 0.1717042406
## 1537 0.409501879 0.855745376 -0.826060 -1.120623945 1.7932700395
## 1920 0.271573583 -0.096884363 -0.826060 -0.480858846 -0.3263238125
## 1926 0.980919102 0.628928771 -0.826060 -1.200447202 -1.1255483985
## 1938 -0.654516400 -1.049514102 -0.826060 0.156548169 0.4960174004
## 1939 1.118847398 0.855745376 -0.826060 -0.960977431 -0.4769220790
## 1947 0.291277625 0.515520469 -0.826060 -1.120623945 -0.1526089192
## 1951 0.251869541 -0.400818613 1.210566 -0.242568117 -0.4769220790
## 25 1.788784833 2.080555039 -0.826060 -1.068930942 0.0717932768
## 134 -0.792444695 -0.695680199 1.210566 -0.162744860 0.1717042406
## 323 -0.043691092 0.388503170 1.210566 0.200097678 -0.2511857948
## 354 -0.418067894 -0.391745949 -0.826060 -0.162744860 1.1446437200
## 381 -1.127413413 -0.777334176 -0.826060 -0.482037888 -0.1526089192
## 1568 -1.580606383 -1.974925848 -0.826060 -0.322391374 0.4960174004
## 1766 0.212461457 -0.096884363 -0.826060 -1.519740231 -0.8012352387
## 1782 0.370093794 -0.096884363 1.210566 -0.641684402 0.4960174004
## 1903 0.429205921 -0.096884363 -0.826060 -0.721507659 -0.8012352387
## 1923 0.941511018 0.991835338 -0.826060 0.795134226 -0.1526089192
## 1925 -0.378659809 -0.096884363 1.210566 -0.561861145 1.7932700395
## 1930 1.000623145 0.765018734 -0.826060 -0.082921602 -0.4769220790
## 1932 2.399610141 1.808375114 -0.826060 -0.801330916 0.1717042406
## 1934 -0.201323429 -0.573199232 -0.826060 1.194250512 0.4960174004
## 385 -0.083099176 -0.096884363 1.210566 0.920140862 -0.2688148161
## 548 -0.299843640 -0.863524486 -0.826060 3.988064511 0.4960174004
## 1078 -0.063395134 -0.096884363 1.210566 1.211843971 -0.2269575067
## 1429 -1.028893202 -0.868060818 -0.826060 -0.881154174 1.4689568797
## 1581 -0.950077033 -1.344375688 -0.826060 0.316194683 -1.7741747181
## 1609 1.887305044 3.033184778 -0.826060 -0.561861145 -1.1255483985
## 1905 -0.378659809 -0.069666371 1.210566 -0.721507659 -0.4769220790
## 1908 -1.107709371 -1.090341091 1.210566 1.753013311 -0.1526089192
## 1916 1.079439313 1.159679625 -0.826060 -1.120623945 1.1446437200
## 1918 0.626246343 -0.096884363 -0.826060 -0.960977431 0.4960174004
## 1919 -0.221027472 -0.890742479 -0.826060 0.475841197 0.4960174004
## 1921 0.389797836 0.048278264 -0.826060 -0.801330916 0.4960174004
## 1927 1.493224200 0.243340544 1.210566 -0.482037888 -0.8012352387
## 1933 -0.595404273 -1.122095415 1.210566 -0.162744860 -1.4498615583
## 77 0.547430174 0.329530853 -0.826060 -0.721507659 0.8203305602
## 184 1.276479735 0.447475488 -0.826060 -0.638789473 -0.0248322520
## 498 0.212461457 0.175295562 -0.826060 -0.103480165 0.0353519681
## 555 0.291277625 -0.359991624 -0.826060 0.102699730 -0.3674674675
## 574 0.192757414 -0.641244214 -0.826060 -0.960977431 -0.8012352387
## 783 0.054829119 0.447475488 -0.826060 -0.322391374 1.1446437200
## 956 -0.457475978 -0.369064288 1.210566 1.347313242 -0.6585558305
## 1584 -0.615108316 -0.355455292 1.210566 1.353897026 -0.1526089192
## 1797 -1.068301286 -0.505154251 1.210566 -0.162744860 -0.4769220790
## 1863 1.966121212 1.808375114 -0.826060 -0.048492288 -0.0253852416
## 1874 0.468614005 0.592638114 -0.826060 0.076724912 -0.8012352387
## 1882 -0.536292147 -0.328237300 -0.826060 -0.881154174 -0.8012352387
## 1898 0.291277625 0.447475488 1.210566 -0.881154174 0.1717042406
## 1911 0.074533161 -0.169465677 -0.826060 0.316194683 0.8203305602
## 1913 -0.772740653 -0.641244214 -0.826060 2.072306340 0.4960174004
## 1914 1.887305044 1.395568894 -0.826060 -0.795489111 0.4798807418
## 1915 -0.201323429 0.402112167 -0.826060 0.316194683 -0.4769220790
## 115 -0.398363851 -0.096884363 1.210566 1.034603997 -1.1255483985
## 259 -0.201323429 -0.337309964 1.210566 -0.402214631 -0.4769220790
## 321 -1.068301286 -0.369064288 -0.826060 -0.402214631 -0.1526089192
## 825 -0.693924484 -0.278337647 -0.826060 -0.429500990 0.4945844616
## 1370 -0.693924484 -0.777334176 1.210566 -0.402214631 -0.1526089192
## 1621 -0.654516400 -0.405354945 -0.826060 0.954780740 0.4960174004
## 1643 -0.536292147 -0.813624833 1.210566 -0.482037888 -1.4498615583
## 1688 1.946417170 1.264015263 -0.826060 -1.273375586 0.3618740767
## 1865 0.232165499 0.855745376 -0.826060 -0.402214631 -0.8012352387
## 1880 -0.201323429 -0.096884363 1.210566 1.034603997 -0.4769220790
## 1886 0.921806976 0.719655413 -0.826060 -0.561861145 0.8203305602
## 1889 -0.989485117 -0.913424139 1.210566 0.555664455 0.1717042406
## 1892 -1.304749793 -1.072195762 -0.826060 0.156548169 1.1446437200
## 1897 0.902102934 1.264015263 -0.826060 -0.003098345 0.1717042406
## 1899 -0.358955767 -1.312621363 -0.826060 -0.721507659 1.1446437200
## 1900 0.744470596 0.175295562 -0.826060 -1.120623945 1.1446437200
## 1904 -0.260435556 -0.033375714 -0.826060 0.954780740 2.4418963591
## 1909 -0.280139598 -0.369064288 -0.826060 -1.040800688 -0.1526089192
## 83 0.685358469 0.991835338 -0.826060 -0.881154174 -0.4769220790
## 208 1.690264622 0.787700394 1.210566 -0.402214631 0.8203305602
## 329 -0.496884062 0.447475488 -0.826060 0.151503876 -0.2106727668
## 449 -0.930372991 1.876420095 -0.826060 2.231952854 0.8203305602
## 451 0.744470596 0.098177917 1.210566 0.076724912 -1.7741747181
## 605 0.902102934 0.991835338 1.210566 -0.322391374 0.8203305602
## 754 -0.102803218 0.011987607 1.210566 0.874957483 -1.7741747181
## 822 -1.147117455 -1.521292639 -0.826060 0.795134226 0.1717042406
## 844 1.256775693 0.855745376 -0.826060 -0.842986742 0.4607757390
## 1074 -0.595404273 0.175295562 -0.826060 -0.078931467 0.6365102374
## 1185 0.113941246 -0.369064288 -0.826060 0.076724912 -1.1255483985
## 1829 0.251869541 -0.096884363 -0.826060 -0.482037888 -0.4769220790
## 1843 -0.102803218 -1.067659430 1.210566 0.715310969 -0.1526089192
## 1888 3.168067786 2.624914890 -0.826060 -0.322391374 -0.4769220790
## 1893 -0.004283007 -0.096884363 1.210566 -0.242568117 -0.4769220790
## 1894 -0.969781075 -0.641244214 1.210566 -0.003098345 1.1446437200
## 30 -0.615108316 -0.700216531 -0.826060 0.143327248 -0.1803602114
## 211 0.094237203 -0.096884363 -0.826060 -0.881154174 -1.1255483985
## 666 -0.437771936 -0.142247684 -0.826060 0.568913453 0.0045611545
## 895 0.035125077 -0.096884363 -0.826060 0.374949460 -0.0943768341
## 1165 1.079439313 0.991835338 1.210566 -0.801330916 0.8203305602
## 1452 -0.142211303 -0.477936259 -0.826060 -0.019253551 -0.2043228968
## 1526 0.113941246 -0.355455292 -0.826060 -0.421643081 0.0997569131
## 1698 -0.418067894 -1.085804759 -0.826060 -0.322391374 0.1717042406
## 1862 0.567134216 1.195970282 -0.826060 -0.436402570 0.3054780646
## 1883 0.350389752 0.991835338 1.210566 -0.242568117 0.8203305602
## 1884 1.315887820 0.878427036 1.210566 -0.003098345 0.4960174004
## 1885 0.094237203 -1.185604064 -0.826060 -0.482037888 0.1717042406
## 1887 -0.890964906 -1.035905106 1.210566 -0.881154174 -0.8012352387
## 96 0.074533161 0.510984137 -0.826060 0.460943254 0.4271790296
## 311 -0.812148738 -0.641244214 1.210566 -0.082921602 1.4689568797
## 437 1.020327187 1.241333603 1.210566 -0.733706387 0.0118434671
## 599 -0.378659809 -0.455254598 -0.826060 -0.628211669 -0.0796079177
## 619 1.138551440 1.536195189 -0.826060 -0.767838265 0.3496908085
## 778 0.429205921 0.039205600 -0.826060 -0.571065702 0.4331622280
## 1008 0.448909963 0.447475488 1.210566 0.032619676 0.5216928581
## 1082 -0.496884062 -0.527835912 1.210566 0.156548169 -1.4498615583
## 1732 2.202569719 1.640530827 -0.826060 -0.881154174 -0.1526089192
## 1784 -0.280139598 -0.641244214 -0.826060 2.231952854 -1.7741747181
## 1820 -0.595404273 -0.831770162 1.210566 1.753013311 0.1717042406
## 1834 0.015421035 0.447475488 1.210566 -0.003098345 0.8203305602
## 1869 -1.265341708 -0.849915490 1.210566 2.152129597 -1.4498615583
## 1870 -0.555996189 -0.641244214 -0.826060 -1.120623945 -1.1255483985
## 1871 1.237071651 0.901108696 -0.826060 -0.721507659 0.1717042406
## 1875 -0.615108316 -0.641244214 -0.826060 -1.040800688 0.4960174004
## 1876 0.764174638 0.801309390 1.210566 -0.561861145 -0.1526089192
## 82 0.606542301 0.397575835 -0.826060 -0.960977431 1.4689568797
## 258 -1.127413413 -1.652846270 -0.826060 0.489537994 -0.1965957076
## 310 -0.398363851 -0.232974326 1.210566 0.510165464 0.2081779969
## 345 0.803582723 0.896572364 -0.826060 -0.641684402 -0.1526089192
## 464 -0.378659809 0.583565450 -0.826060 -0.916545335 0.1762938653
## 595 -0.674220442 -0.369064288 -0.826060 0.076724912 -0.1526089192
## 676 -0.477180020 -0.777334176 1.210566 0.064098560 0.4021671134
## 841 0.448909963 0.447475488 1.210566 -0.591174146 0.3257782750
## 1434 -0.910668949 -1.367057348 -0.826060 -1.120623945 1.7932700395
## 1605 -0.063395134 0.157150234 1.210566 -0.960977431 -0.1526089192
## 1616 -0.831852780 -0.505154251 1.210566 0.475841197 -0.4769220790
## 1769 0.389797836 0.011987607 -0.826060 -0.960977431 0.4960174004
## 1833 1.473520157 1.536195189 -0.826060 -1.280270459 2.4418963591
## 1846 -0.575700231 -1.525828971 -0.826060 0.316194683 1.4689568797
## 1858 1.059735271 0.805845723 -0.826060 0.236371426 0.4960174004
## 1859 -0.083099176 0.084568920 -0.826060 -0.960977431 -0.8012352387
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## 196 -0.260435556 -0.096884363 1.210566 1.139292150 -0.6746134183
## 221 -0.023987050 0.447475488 -0.826060 -0.451739437 0.1338714954
## 497 0.842990807 0.991835338 -0.826060 0.506700412 -0.5986145340
## 503 -0.555996189 -0.096884363 1.210566 -0.085940442 0.1423820987
## 516 -1.245637666 -1.004150781 1.210566 0.368843919 0.2259706957
## 637 -1.147117455 -1.367057348 -0.826060 0.198778137 0.0229689619
## 656 0.783878680 0.628928771 -0.826060 -0.207390018 0.0946157484
## 661 0.468614005 0.447475488 1.210566 -0.406664697 0.2158469815
## 664 0.961215060 0.991835338 -0.826060 -0.501966823 0.5410303092
## 671 -1.304749793 -0.949714796 -0.826060 -0.118340098 -0.0455645383
## 674 -0.201323429 0.025596603 1.210566 -0.051476504 0.0430754081
## 678 3.266587997 3.713634591 -0.826060 -1.653719745 0.7282520997
## 683 0.251869541 -0.096884363 1.210566 -0.212617614 0.2138163239
## 206 1.059735271 0.991835338 -0.826060 -0.192959479 0.4520010897
## 256 -0.221027472 0.175295562 -0.826060 0.197398751 0.5170643029
## 453 0.705062512 0.719655413 1.210566 0.345391573 -0.2289016441
## 456 0.409501879 0.901108696 -0.826060 -0.401139370 0.2255161935
## 509 -0.418067894 -0.096884363 -0.826060 0.384861272 0.7014566265
## 544 0.448909963 0.991835338 1.210566 -0.390742029 0.3559020412
## 627 -0.969781075 -0.505154251 -0.826060 0.466107186 0.2478472626
## 634 -0.280139598 -0.096884363 1.210566 0.077204437 -0.1319225719
## 641 1.335591862 0.855745376 -0.826060 -1.146055619 0.1515693586
## 642 -0.122507261 -0.505154251 -0.826060 -0.508966556 0.3103908933
## 643 1.276479735 1.264015263 -0.826060 -0.805275665 0.5652952836
## 653 -0.260435556 -0.228437994 1.210566 -0.087027024 -0.1756305191
## 654 0.035125077 0.447475488 -0.826060 -0.231464841 0.3053889531
## 660 -0.496884062 -0.641244214 1.210566 0.191634437 0.1349939076
## 4 -0.122507261 -0.437109270 -0.826060 0.226580522 -0.0037650896
## 121 -0.063395134 -0.232974326 1.210566 1.397737543 -0.4298818367
## 338 0.764174638 0.855745376 1.210566 0.011472544 -0.2262614529
## 496 0.626246343 0.560883790 -0.826060 -0.722249870 -0.0864096273
## 580 0.113941246 0.669755760 -0.826060 -0.002276930 -0.1113245934
## 608 0.842990807 0.923790357 -0.826060 -1.148281692 0.3174094614
## 615 0.547430174 0.651610432 -0.826060 -0.486736984 0.2818641197
## 616 0.074533161 0.202513555 -0.826060 -0.167304389 0.2915729984
## 623 0.094237203 0.243340544 1.210566 0.182848047 0.3859888649
## 782 1.788784833 1.876420095 -0.826060 -1.121411650 0.5632643438
## hgb glucose male crcl
## 249 -0.7495234781 -8.724623e-01 0.6064784 -0.1317682722
## 318 -1.7420906990 1.710539e+00 0.6064784 -0.7421109797
## 631 0.6331693113 -3.715915e-01 0.6064784 0.5717210286
## 883 -0.0116602710 -3.428591e-01 0.6064784 -0.6744751413
## 1095 -0.5630893558 -5.522727e-01 0.6064784 -0.5717129059
## 1405 -1.8838860163 2.310165e+00 0.6064784 0.0132141255
## 1554 -0.1823422090 8.572262e-01 -1.6488632 -1.6116524451
## 1589 1.0938156464 -3.189619e-01 -1.6488632 0.0534702276
## 1708 -0.6786258195 1.572164e+00 0.6064784 -0.6142916844
## 1773 0.3848390601 -4.573370e-01 -1.6488632 1.7809115145
## 1850 -2.1674766508 -2.036494e-01 0.6064784 -1.1944129455
## 1935 -0.2532398676 -5.495871e-01 0.6064784 2.4691475878
## 2077 1.6609969155 -1.033900e+00 0.6064784 0.0267687661
## 110 -2.8055555786 -2.728369e-01 0.6064784 -1.4777075269
## 116 0.0045601647 -5.544574e-01 0.6064784 -0.4366428617
## 328 -1.2658655449 1.056390e+00 0.6064784 -1.1594996036
## 482 -0.2682909939 6.705646e-01 0.6064784 -1.2250689331
## 723 0.5153346745 -3.571493e-01 0.6064784 0.7262500168
## 732 0.0790890950 -5.337742e-01 0.6064784 -0.7446251221
## 737 -1.1182007111 -3.205041e-01 0.6064784 -1.1760103023
## 802 -0.7767979889 -3.669177e-01 0.6064784 -0.8645531773
## 894 -0.2354897285 -1.720189e-01 0.6064784 -1.0998676824
## 1204 -3.5854298236 -2.036494e-01 0.6064784 -1.6204089903
## 1564 0.3848390601 -1.805869e-01 0.6064784 -0.9252900173
## 1632 -0.5368305022 -9.416498e-01 0.6064784 -0.2896814729
## 1794 -0.3956958107 -4.573370e-01 0.6064784 -1.0601797215
## 2120 -2.0256813336 -7.110247e-01 -1.6488632 -0.8587581602
## 2127 -2.7346579199 3.037259e-01 -1.6488632 -0.8078949117
## 3 -0.4835350299 -2.556703e-01 0.6064784 -1.3335427256
## 188 0.5505319490 -2.848298e-01 0.6064784 0.2672273469
## 320 -0.8856001959 -6.821022e-01 -1.6488632 -1.3446725965
## 539 1.1647133051 -2.497744e-01 0.6064784 -0.3763025179
## 632 1.3065086224 1.987289e+00 0.6064784 0.4556655322
## 712 -0.3301932491 -3.007871e-01 0.6064784 -0.1058987136
## 808 -1.4424330088 1.277462e+00 -1.6488632 -1.3807969424
## 1081 -0.2189560691 -2.845960e-01 0.6064784 -0.6108242014
## 1232 -4.0108157754 -3.881495e-01 0.6064784 -0.4612602127
## 1313 -0.3403860849 -2.354833e-01 0.6064784 0.4889455764
## 1674 -1.9547836749 -1.805869e-01 0.6064784 0.2952536788
## 1717 -2.8764532372 -6.879622e-01 0.6064784 -0.5350887961
## 1727 -1.5293977231 -1.113993e-01 0.6064784 -0.7225172338
## 2104 0.1721460842 7.880387e-01 0.6064784 -0.4961098137
## 2154 -0.0405468917 9.725388e-01 0.6064784 -0.6283865073
## 2181 -2.8764532372 -6.187746e-01 0.6064784 -1.1764543449
## 2197 -2.3092719681 -8.032747e-01 0.6064784 -0.9979999859
## 445 -0.0405468917 -6.648996e-01 0.6064784 -0.1127491480
## 777 -0.1424924721 8.295107e-01 0.6064784 -0.6391643511
## 800 -0.4659328436 -2.267119e-01 0.6064784 0.0143946564
## 904 0.6209605452 -3.441645e-01 0.6064784 0.1879334414
## 906 -0.4142735439 5.223006e-02 0.6064784 -0.3402546005
## 933 -0.5351115828 7.658899e-01 0.6064784 -0.5426619610
## 1155 0.3175913887 -5.243551e-02 0.6064784 0.1653468491
## 1162 -1.7420906990 -3.881495e-01 0.6064784 -1.6921721619
## 1424 -1.0331141127 -7.802122e-01 0.6064784 -0.8843486162
## 1664 -0.2507556966 -3.363121e-01 0.6064784 -1.0820291888
## 2000 0.1721460842 1.203164e+00 0.6064784 -0.5029871951
## 2035 -0.4797418634 -1.078188e-01 0.6064784 -0.9396954117
## 186 2.5117688192 -3.189619e-01 0.6064784 -0.4165874877
## 375 0.0246465717 -4.472596e-01 0.6064784 -0.3315391315
## 792 -0.1961517074 -1.084130e-01 0.6064784 -0.4599958418
## 957 -0.4884751710 -4.822058e-01 0.6064784 -1.2100477501
## 1014 -0.3112262530 -2.361588e-01 0.6064784 -0.5458972627
## 1112 -1.4585000645 2.114759e-01 0.6064784 -1.3349618673
## 1253 3.2916430642 -5.957121e-01 0.6064784 -1.0182344636
## 1420 -0.6639924158 -1.415862e-01 -1.6488632 -0.1977055267
## 1988 -1.0331141127 -4.342745e-01 -1.6488632 3.1260900111
## 88 0.0881157389 -7.148138e-01 0.6064784 -0.9767041095
## 117 -2.1674766508 -4.573370e-01 0.6064784 -1.2015214611
## 229 -1.1749094299 -6.418371e-01 0.6064784 1.2168496557
## 512 -0.0761900211 1.265472e+00 -1.6488632 -0.0660794003
## 583 0.5975320360 -1.575243e-01 0.6064784 -0.9692481825
## 648 0.0713587695 -3.781677e-01 0.6064784 -1.1743711047
## 1058 0.0303507669 3.913244e-03 0.6064784 0.3599682340
## 1203 -0.4659328436 -3.189619e-01 0.6064784 0.3078980425
## 1225 -0.9622164540 -9.647123e-01 -1.6488632 -1.0067707248
## 1415 0.5975320360 -6.879622e-01 0.6064784 0.0383272364
## 1438 -1.7420906990 5.077666e+00 -1.6488632 -1.0183069053
## 1442 0.8102250119 -2.036494e-01 0.6064784 0.7232631895
## 1824 0.2430437428 -9.647123e-01 -1.6488632 -0.8229379922
## 2178 -0.9622164540 2.697576e-02 0.6064784 -0.5554895401
## 200 -1.3786525484 1.995710e-01 -1.6488632 -1.2071917033
## 460 -0.5239184951 -3.686309e-01 -1.6488632 0.2296600006
## 490 0.1664819866 1.106338e+00 0.6064784 0.9584494840
## 505 -0.3239851594 -4.617157e-01 0.6064784 -0.3175939655
## 636 -0.3160456724 -6.475393e-01 -1.6488632 -0.8427002065
## 877 -0.8325921569 4.790793e-01 0.6064784 -0.9763077185
## 885 0.5975320360 2.697576e-02 0.6064784 0.9526617987
## 2214 -0.0405468917 3.913244e-03 -1.6488632 -1.2563711477
## 157 -0.5368305022 5.574136e-01 0.6064784 -0.8289841620
## 170 -1.1604962606 9.226950e-01 0.6064784 -1.0671438161
## 317 -0.6786258195 -2.728369e-01 0.6064784 -1.1099613278
## 399 0.0333264749 -5.313981e-01 0.6064784 0.0103116930
## 489 -2.1674766508 -7.340872e-01 0.6064784 -0.4465489820
## 552 -0.2129792291 -3.984235e-01 0.6064784 -0.7780954072
## 652 -0.1505998327 -4.581505e-01 0.6064784 -0.0814223756
## 876 -0.2307537266 -2.663811e-01 0.6064784 -0.2587408604
## 1573 1.1647133051 1.502977e+00 0.6064784 -0.6625539322
## 1993 1.5192015983 -4.112120e-01 0.6064784 -0.0259043742
## 2040 -1.6002953817 -5.726496e-01 0.6064784 -0.1165369476
## 10 2.7244617951 1.192258e-01 -1.6488632 -0.8836868742
## 238 0.0940582225 -2.059188e-01 0.6064784 0.0879251392
## 360 -0.3720548184 -3.095569e-01 0.6064784 -0.5203041930
## 416 -0.3304860047 -3.964480e-01 0.6064784 -1.2002784870
## 733 0.0932042652 9.175189e-01 0.6064784 0.2804552568
## 967 -0.6947999491 -3.106494e-01 0.6064784 -0.8428361299
## 968 -0.3655259197 -5.503660e-01 0.6064784 -1.1681126013
## 1134 -0.7748245306 -3.881495e-01 -1.6488632 -0.0726329075
## 1135 -0.2667147589 -2.175751e-01 0.6064784 -0.9951414664
## 1183 -0.2532398676 -8.263372e-01 0.6064784 0.0199485174
## 1184 2.5826664778 -2.728369e-01 0.6064784 -0.4717479497
## 1686 0.5266343774 -8.032747e-01 -1.6488632 1.3462917018
## 2069 -0.1653867319 -2.809557e-01 0.6064784 -0.3938639416
## 2204 -1.3876024058 9.616330e-02 0.6064784 1.5048685213
## 38 0.8208136212 -1.428519e-01 0.6064784 -0.3185810573
## 392 -0.2532398676 3.913244e-03 0.6064784 0.8978219940
## 443 0.0303507669 1.192258e-01 0.6064784 -0.9892098860
## 532 -1.1914868658 -5.336099e-01 -1.6488632 -0.7513986192
## 647 -0.4699503594 -1.574050e-02 0.6064784 -0.5908957377
## 724 0.3753606947 -5.138370e-01 0.6064784 -0.1236858476
## 1128 -2.0256813336 -1.126150e+00 0.6064784 1.1049905244
## 1337 -0.0405468917 4.916229e+00 -1.6488632 -0.5502881309
## 1593 0.0523752146 -5.611531e-02 0.6064784 -0.1200843953
## 1777 -0.1114445504 -5.034620e-01 0.6064784 -0.8809076788
## 8 -0.0691467733 -5.968804e-01 0.6064784 -0.8899219857
## 222 0.8811226705 1.064789e+00 0.6064784 0.2962195678
## 228 0.8297996257 -4.646445e-01 0.6064784 -0.1064198383
## 313 1.6609969155 3.729135e-01 0.6064784 0.0546400264
## 342 -0.0184954257 -2.618155e-01 0.6064784 -0.4606532829
## 350 -1.1034936441 -5.095547e-01 0.6064784 -1.4047001094
## 459 1.1736485810 -1.771841e-01 0.6064784 5.3297021814
## 573 0.1616357948 -2.208109e-01 0.6064784 -0.0767324321
## 771 0.0452626573 -4.464931e-01 0.6064784 -0.8887073817
## 788 -1.0688477074 1.264874e+00 0.6064784 -1.1625326467
## 789 -0.9313566196 5.075557e-01 -1.6488632 -0.6140575523
## 886 -0.6892645052 7.226193e-01 -1.6488632 -1.0959707754
## 1029 -0.5368305022 6.035386e-01 -1.6488632 -0.9425984650
## 1067 1.8736898915 3.094290e+00 0.6064784 -1.1747218775
## 1143 -0.4659328436 -4.112120e-01 0.6064784 1.6452041266
## 1251 0.0576091059 1.475354e-01 0.6064784 0.0056327632
## 1360 -0.0405468917 1.595227e+00 0.6064784 -0.3456672544
## 1547 0.5266343774 1.549102e+00 -1.6488632 1.1028065422
## 1906 0.4557367187 -1.575243e-01 -1.6488632 0.1946603764
## 2187 -0.4659328436 3.729135e-01 0.6064784 -0.0591780078
## 2193 0.1012484255 1.041726e+00 0.6064784 0.0047260111
## 2199 -0.3950351849 -7.340872e-01 -1.6488632 -0.4945053650
## 74 -0.7495234781 -6.648996e-01 -1.6488632 0.3174691230
## 241 2.0863828674 -6.187746e-01 0.6064784 0.3578754747
## 348 0.6673254367 -1.677280e-01 0.6064784 0.8046452847
## 442 4.0006196506 -2.958994e-01 0.6064784 -0.7439890970
## 470 1.4483039396 2.010352e+00 0.6064784 -0.9239717579
## 474 0.9520203292 -4.112120e-01 0.6064784 -0.0527882814
## 524 -0.7448695906 5.697787e-01 0.6064784 0.3340071052
## 711 -1.4262932688 6.326999e-01 -1.6488632 -1.1589102550
## 1299 -0.3950351849 -5.495871e-01 0.6064784 -0.3909484554
## 1515 0.5266343774 9.616330e-02 -1.6488632 -0.2731683504
## 1624 0.6684296946 2.114759e-01 0.6064784 -0.9884126444
## 1736 0.2430437428 -4.342745e-01 0.6064784 0.1284124728
## 2044 -0.3241375263 -1.679650e+00 0.6064784 -1.7241648315
## 2067 0.6315261623 -1.576432e-01 0.6064784 -0.2066900946
## 2183 -0.2532398676 -6.527430e-02 -1.6488632 -0.2969714298
## 2185 -0.6786258195 -6.187746e-01 0.6064784 -0.6558249063
## 2189 1.0229179878 -2.958994e-01 0.6064784 -0.1106830755
## 2190 -0.6786258195 -8.833681e-02 -1.6488632 -0.5770848398
## 2191 -0.3950351849 2.379352e+00 -1.6488632 -0.5353845996
## 2192 0.3139414014 -4.803995e-01 0.6064784 0.0356656759
## 7 -3.0891462131 -9.877748e-01 -1.6488632 -0.1311283089
## 62 -3.0891462131 -1.472088e+00 0.6064784 -0.5148265938
## 169 2.0863828674 2.697576e-02 0.6064784 1.4451121958
## 305 -0.3950351849 -7.340872e-01 -1.6488632 -0.3516503994
## 327 0.4557367187 3.809228e+00 0.6064784 0.8315366500
## 542 0.0226901409 -5.772852e-01 -1.6488632 0.1525766165
## 613 -0.1858321420 -3.569257e-01 0.6064784 -0.8992300615
## 635 0.2751170077 -2.993844e-01 0.6064784 0.1860199431
## 812 1.3065086224 -9.185873e-01 0.6064784 -0.5182394012
## 903 -1.3876024058 3.555540e+00 0.6064784 -1.0662375312
## 1355 0.1721460842 -1.241462e+00 0.6064784 -0.6035595857
## 1422 0.1012484255 -1.113993e-01 0.6064784 0.1018344304
## 1509 -1.6002953817 -6.648996e-01 0.6064784 -1.6120548988
## 1601 1.0229179878 -3.650870e-01 0.6064784 -0.5229078641
## 2175 0.3139414014 -1.113993e-01 -1.6488632 0.3010490121
## 2177 -0.4659328436 7.310078e-02 -1.6488632 3.3817902346
## 2184 -0.4659328436 -2.958994e-01 0.6064784 0.5222375660
## 2186 -0.2532398676 -2.958994e-01 -1.6488632 2.1770466918
## 2188 -1.0331141127 5.169916e+00 0.6064784 -0.4562078708
## 174 0.3139414014 -2.267119e-01 0.6064784 0.1387796801
## 253 0.8811226705 2.114759e-01 0.6064784 -0.1813588236
## 286 -0.5368305022 3.913244e-03 -1.6488632 -0.6720858245
## 492 -0.4628995248 5.950590e-01 0.6064784 -0.6080546311
## 534 -0.9927269977 -1.695149e-01 0.6064784 -0.8874335708
## 607 0.3848390601 -4.573370e-01 -1.6488632 -0.7765207711
## 614 -0.1012639904 -1.851472e-01 0.6064784 -0.4197807684
## 663 -0.0057913813 -2.910428e-02 0.6064784 -0.8102453471
## 964 -0.8917086900 -1.573581e-01 0.6064784 -1.1072592537
## 1066 -0.1823422090 4.651635e-01 0.6064784 0.6883704535
## 1433 -0.0405468917 -8.955248e-01 -1.6488632 -0.7175756134
## 1524 1.1647133051 -5.265246e-01 0.6064784 -0.2595743588
## 1555 0.2430437428 -3.420245e-01 0.6064784 -0.6805417551
## 1675 -0.2056519986 1.099645e+00 0.6064784 -0.0310932401
## 1907 -0.3950351849 1.318477e+00 0.6064784 -0.0699673050
## 2021 -1.6002953817 -5.265246e-01 0.6064784 1.0101545456
## 2113 1.3065086224 2.863665e+00 0.6064784 0.3245845046
## 2129 -2.3801696267 8.572262e-01 -1.6488632 -1.6943733978
## 2170 -0.4031987036 -7.110247e-01 -1.6488632 -0.7682982761
## 2172 0.2387559191 -1.204708e-01 0.6064784 0.0604892262
## 2173 0.3848390601 4.421010e-01 0.6064784 2.9356719174
## 2198 -0.8204211367 -6.879622e-01 0.6064784 -0.4024400564
## 2200 0.6684296946 -6.418371e-01 0.6064784 -0.1051609229
## 2201 0.3063287420 -2.265698e-01 0.6064784 0.3990159722
## 1 0.7393273533 -4.803995e-01 0.6064784 -0.9553148521
## 46 0.7393273533 1.041726e+00 0.6064784 -0.0279971334
## 103 -0.4627605434 -3.057891e-01 0.6064784 -0.5996579446
## 220 -1.8838860163 -1.805869e-01 -1.6488632 0.5042478855
## 236 0.2430437428 -4.112120e-01 -1.6488632 -0.7952421855
## 514 -0.7011077577 -6.904883e-01 -1.6488632 -0.8782697879
## 638 -0.2084842920 -3.555446e-01 0.6064784 -0.5849907268
## 1520 -1.3876024058 -1.333713e+00 -1.6488632 -1.2654808884
## 1722 -1.6711930404 1.884134e-01 0.6064784 -0.8301958885
## 1877 -0.1823422090 3.163478e+00 -1.6488632 -1.8103035917
## 2145 -1.6002953817 -5.726496e-01 0.6064784 -0.5078457764
## 2146 -1.1040117713 1.318477e+00 0.6064784 -0.4008302416
## 172 0.4674096857 -4.834775e-02 0.6064784 -0.4714783436
## 269 -0.7495234781 -3.650870e-01 0.6064784 -0.9356107189
## 270 -0.5368305022 -2.267119e-01 -1.6488632 -0.9942738338
## 291 1.0229179878 -4.112120e-01 0.6064784 -0.6267102557
## 343 -1.6711930404 -5.265246e-01 -1.6488632 -0.7202818910
## 707 -0.6127902806 -6.037619e-01 -1.6488632 -1.0919669792
## 745 -0.3977718664 7.741943e-01 0.6064784 -0.5773112321
## 919 0.5919097907 -1.738072e-01 0.6064784 0.2229899100
## 1293 -0.8204211367 -2.728369e-01 0.6064784 -0.2691226244
## 1338 0.0811030104 9.510980e-01 0.6064784 0.5719581574
## 1952 1.4483039396 -1.113993e-01 0.6064784 -0.9212709840
## 1975 -0.2532398676 -4.112120e-01 -1.6488632 0.0493287108
## 2108 2.0863828674 -2.958994e-01 0.6064784 1.8957315552
## 2156 0.3822560039 -2.837224e-01 0.6064784 0.2434559626
## 2164 1.0229179878 1.595227e+00 0.6064784 -0.3754917566
## 2171 -0.4659328436 3.498509e-01 0.6064784 -0.5840023997
## 2195 0.5266343774 -8.833681e-02 0.6064784 0.5647366767
## 237 -1.3876024058 3.959760e-01 -1.6488632 -0.7118665841
## 363 -0.1474881125 -2.601607e-01 0.6064784 -0.7033911991
## 411 -0.6033067928 -7.504266e-01 -1.6488632 -1.0868682128
## 806 0.5337413324 -4.332741e-01 0.6064784 -0.1803214779
## 1854 -0.3241375263 -3.420245e-01 0.6064784 0.0328538661
## 1879 0.0303507669 -6.418371e-01 0.6064784 -0.1916348081
## 2015 -1.5293977231 -7.802122e-01 0.6064784 -1.3759020665
## 2018 -0.3950351849 -6.648996e-01 0.6064784 -1.1907224610
## 2056 1.4483039396 -1.914927e-02 0.6064784 0.8010574896
## 2081 0.2430437428 2.697576e-02 -1.6488632 0.3024564503
## 2132 0.3139414014 -2.036494e-01 0.6064784 0.5551743768
## 2158 -0.2532398676 1.226226e+00 0.6064784 -1.1522806891
## 2159 -0.6786258195 -5.034620e-01 0.6064784 -0.9128922075
## 2163 0.9520203292 -2.958994e-01 -1.6488632 0.0762987431
## 2165 0.5975320360 -1.113993e-01 -1.6488632 -0.7436805491
## 2168 1.1647133051 -3.650870e-01 0.6064784 0.8704258731
## 2169 -1.8838860163 -4.342745e-01 -1.6488632 -1.3913026895
## 2196 -3.3018391890 -3.650870e-01 -1.6488632 -1.1422787432
## 161 -0.3950351849 -5.034620e-01 0.6064784 -0.7598189425
## 180 -0.6786258195 -9.877748e-01 0.6064784 0.1587413836
## 367 0.8102250119 -2.267119e-01 0.6064784 1.2021062608
## 685 -0.2021932174 -3.660879e-01 0.6064784 -0.6148103430
## 990 -0.3613640371 -4.456463e-01 0.6064784 -0.7876928662
## 1086 -0.4041868043 -2.730672e-01 0.6064784 -1.1911483101
## 1100 -0.4648383361 -3.632657e-01 -1.6488632 -0.4319687802
## 1969 -1.4585000645 1.422883e-01 0.6064784 -1.1148233518
## 2135 0.3139414014 -1.575243e-01 -1.6488632 0.1842951813
## 2153 0.4557367187 -1.113993e-01 0.6064784 -0.1128343735
## 2155 -0.8204211367 3.555540e+00 -1.6488632 -0.6833974565
## 2166 -0.3241375263 -5.495871e-01 -1.6488632 -0.2523718822
## 223 -0.0405468917 8.802888e-01 0.6064784 0.4685770725
## 681 0.1469781598 -7.090201e-02 0.6064784 1.6854791993
## 963 -1.2131035662 -4.096223e-01 0.6064784 -0.6939136594
## 1192 0.6571910283 -2.468664e-01 0.6064784 0.1473679210
## 1500 1.0938156464 -5.034620e-01 0.6064784 0.5020343902
## 1522 -1.4585000645 -2.728369e-01 0.6064784 -0.4936123912
## 1523 2.4408711605 6.727261e-01 0.6064784 0.2356905313
## 1545 -1.9547836749 -3.650870e-01 0.6064784 -1.2417617206
## 1560 -0.2532398676 -6.418371e-01 0.6064784 -0.2285800578
## 2019 -0.8913187954 -5.726496e-01 0.6064784 -0.1326672919
## 2095 0.1721460842 -5.495871e-01 0.6064784 1.1307878066
## 2121 -0.5368305022 -7.571497e-01 -1.6488632 0.6258023182
## 2147 -0.2532398676 -7.802122e-01 -1.6488632 -0.2166497203
## 2151 0.4557367187 3.729135e-01 0.6064784 -0.2886632995
## 2157 0.9520203292 -4.803995e-01 0.6064784 2.0627230106
## 2231 -2.1674766508 -3.881495e-01 0.6064784 -0.2489808017
## 112 -1.3167047472 2.725290e+00 -1.6488632 -0.5176455584
## 176 0.5266343774 -3.881495e-01 0.6064784 -1.0418354985
## 219 -0.3950351849 -2.267119e-01 0.6064784 -1.1288177645
## 382 -0.7587622442 -4.248422e-01 0.6064784 -1.0879271889
## 684 0.3584672977 -2.479270e-01 0.6064784 -0.1846937254
## 1268 -0.0405468917 2.114759e-01 -1.6488632 0.8454213026
## 1617 1.7318945742 -6.879622e-01 0.6064784 -0.5167076380
## 1786 -0.7495234781 3.267884e-01 0.6064784 -0.4290556611
## 2143 -0.7495234781 5.112885e-01 -1.6488632 0.1957961007
## 2148 1.4483039396 -7.110247e-01 0.6064784 0.5735906580
## 2152 1.8736898915 -6.879622e-01 0.6064784 -0.3211444089
## 98 -1.8129883577 -7.802122e-01 0.6064784 -0.4158161540
## 99 -0.9464037890 -3.364869e-01 0.6064784 -0.7412531996
## 209 0.4557367187 -7.802122e-01 -1.6488632 -0.3267465644
## 254 -0.4034206728 1.221192e+00 0.6064784 -0.6236089630
## 331 -0.3950351849 3.324915e+00 -1.6488632 -0.1510513769
## 357 -0.0559289442 1.146735e+00 0.6064784 -0.2416681183
## 410 0.8811226705 5.343511e-01 0.6064784 -0.9313715399
## 1426 -1.1749094299 -4.342745e-01 0.6064784 -1.1729717199
## 1709 1.0938156464 2.217915e+00 0.6064784 2.8262045109
## 1746 -2.5928626027 1.595227e+00 0.6064784 -1.1727163166
## 1796 0.5266343774 -1.575243e-01 0.6064784 -0.1840253373
## 2118 1.7318945742 -2.958994e-01 0.6064784 -0.0749715294
## 2126 -1.0552733701 -3.950361e-01 -1.6488632 -1.0251619263
## 2134 -1.2458070886 -7.802122e-01 0.6064784 -1.8679547046
## 2136 0.5266343774 -2.728369e-01 0.6064784 0.2425322440
## 2137 1.0938156464 1.133976e+00 0.6064784 0.8291755883
## 2138 -1.0331141127 -4.221178e-02 0.6064784 -0.6326033278
## 2141 1.8027922328 -4.342745e-01 -1.6488632 -1.2649453928
## 2142 1.2356109637 -1.805869e-01 0.6064784 1.5963060020
## 2144 0.8811226705 -1.113993e-01 0.6064784 -0.0002009978
## 2220 0.7393273533 1.653508e-01 0.6064784 -0.0940986058
## 64 -0.6077281608 4.421010e-01 -1.6488632 -0.6691742955
## 261 0.4557367187 -3.189619e-01 0.6064784 -1.0754633798
## 478 -0.3700537029 1.473655e+00 0.6064784 -0.3540039556
## 485 0.8811226705 3.913244e-03 0.6064784 0.5531460102
## 604 -1.6980285777 -2.843587e-01 -1.6488632 -1.3821218256
## 828 -0.3502130045 8.832762e-01 0.6064784 -0.5660827561
## 1392 0.9520203292 -2.497744e-01 0.6064784 -0.4603176333
## 1463 2.5826664778 -6.648996e-01 0.6064784 -0.3530417393
## 1647 1.0229179878 -8.724623e-01 0.6064784 0.6289682873
## 1881 -0.5368305022 7.310078e-02 -1.6488632 -0.8620919404
## 2124 -2.0965789922 1.226226e+00 0.6064784 -1.4490417212
## 2131 0.3848390601 -3.420245e-01 -1.6488632 1.1028977651
## 2140 -1.4585000645 3.037259e-01 0.6064784 -0.7363156464
## 2213 -0.6077281608 -5.265246e-01 -1.6488632 -0.7037775743
## 159 0.0696348972 -5.573272e-01 0.6064784 -0.2678749498
## 454 -0.1114445504 4.190385e-01 0.6064784 1.3010337710
## 458 -0.1529928839 1.196565e+00 0.6064784 0.2575120632
## 475 -1.8129883577 -7.802122e-01 0.6064784 -0.7616587308
## 493 0.1230764647 -2.402647e-01 0.6064784 -0.6593636152
## 939 -0.2773805622 -1.161626e-01 0.6064784 -0.7991437694
## 1150 -0.6355925543 1.636983e-01 -1.6488632 -0.1767333118
## 1213 0.1721460842 7.310078e-02 -1.6488632 2.0205630343
## 1264 0.3848390601 -2.267119e-01 0.6064784 0.6931998980
## 1300 -2.3092719681 8.111012e-01 0.6064784 -0.9183941732
## 1562 -2.7346579199 -8.032747e-01 -1.6488632 -1.3218052899
## 1661 -1.1040117713 4.651635e-01 0.6064784 0.0109603848
## 1776 -0.3241375263 -1.113993e-01 -1.6488632 0.0300259581
## 2112 -0.7495234781 9.264138e-01 0.6064784 -0.7975805983
## 2115 -0.1823422090 -9.416498e-01 0.6064784 0.6837709826
## 2122 1.5192015983 4.190385e-01 0.6064784 0.2360326168
## 2123 3.0080524296 -6.187746e-01 0.6064784 1.5684025455
## 2125 1.3774062810 2.806634e-01 0.6064784 0.0674970806
## 2128 -1.2458070886 -4.112120e-01 -1.6488632 0.4351465851
## 45 0.1721460842 3.959760e-01 0.6064784 -0.8246077967
## 179 1.0229179878 -6.187746e-01 0.6064784 1.4036290569
## 306 -0.1114445504 6.266011e-01 0.6064784 1.1514117294
## 1307 -0.5368305022 -9.877748e-01 0.6064784 -0.6932964416
## 1872 0.6684296946 -2.497744e-01 0.6064784 -0.3103338834
## 2082 -2.0965789922 3.417165e+00 0.6064784 -1.3804204356
## 2117 1.8736898915 -7.110247e-01 0.6064784 0.0875875694
## 2119 -0.4659328436 9.616330e-02 0.6064784 -0.4528093729
## 304 -0.2532398676 -7.571497e-01 0.6064784 -0.6014489396
## 334 0.9520203292 8.802888e-01 0.6064784 -1.0657595109
## 513 0.0269964009 8.684314e-01 0.6064784 0.2778269899
## 598 -0.7495234781 -2.958994e-01 0.6064784 -0.0360462074
## 909 -0.5958301760 1.538904e+00 0.6064784 -0.5429935218
## 1030 -0.6677465631 -3.509569e-01 0.6064784 -1.0024346833
## 1238 -0.3241375263 3.209603e+00 0.6064784 -0.4482996556
## 1700 -1.5293977231 -5.726496e-01 -1.6488632 -1.0970139706
## 2017 -1.9547836749 -1.010837e+00 0.6064784 -0.5322716202
## 2087 1.8736898915 7.245542e+00 0.6064784 1.1085321168
## 2106 1.2356109637 -5.034620e-01 0.6064784 0.1115416136
## 2109 -0.1575656900 -1.000514e-01 -1.6488632 0.0155292484
## 2110 0.9520203292 -9.416498e-01 -1.6488632 -0.5142094981
## 2111 -0.1823422090 -3.189619e-01 -1.6488632 0.1335638801
## 2114 -0.5368305022 -1.914927e-02 0.6064784 1.6571570015
## 40 1.3774062810 -5.265246e-01 0.6064784 0.9246912665
## 95 -0.6786258195 -3.420245e-01 0.6064784 -0.9826173111
## 234 0.4557367187 1.884134e-01 0.6064784 -0.8034248010
## 358 0.3139414014 -2.497744e-01 0.6064784 -0.0169040460
## 450 0.2025437134 -2.780062e-01 0.6064784 0.1999103618
## 621 -0.3412724368 -3.040338e-01 -1.6488632 -0.0821040083
## 934 -0.7828608284 -2.745877e-01 0.6064784 -1.3725004247
## 965 -0.2460829872 -2.490642e-01 0.6064784 -0.5977723242
## 1033 -0.5915898558 -1.910845e-01 0.6064784 -1.2643043656
## 1068 -0.1823422090 6.496636e-01 -1.6488632 -1.3482485103
## 1120 1.6609969155 -5.265246e-01 0.6064784 0.0822751805
## 1234 0.1721460842 -2.036494e-01 0.6064784 0.0534099628
## 2011 -0.3950351849 9.616330e-02 0.6064784 0.5466819847
## 2068 -0.4659328436 -5.495871e-01 0.6064784 -0.2173113542
## 2078 -2.0965789922 1.422883e-01 -1.6488632 -0.1782129772
## 2100 1.8736898915 1.192258e-01 0.6064784 0.4454347651
## 2102 -0.7495234781 1.041726e+00 -1.6488632 2.1045245350
## 2105 -0.3241375263 2.576009e-01 -1.6488632 0.0066598795
## 2203 -0.3241375263 7.310078e-02 0.6064784 0.1648091471
## 193 -2.6637602613 5.003827e-02 0.6064784 -1.3517548444
## 387 -2.2383743095 2.079539e+00 0.6064784 -1.6319092813
## 731 2.0863828674 -3.420245e-01 0.6064784 -0.4735294781
## 1262 0.8811226705 6.727261e-01 0.6064784 1.5443626444
## 1342 0.2430437428 -7.110247e-01 -1.6488632 0.6445942231
## 1465 1.5900992569 -4.803995e-01 0.6064784 0.2401980126
## 2048 0.3848390601 -2.036494e-01 0.6064784 2.9128125473
## 2093 0.3996916403 -5.034620e-01 0.6064784 0.3067236094
## 2099 -1.0331141127 -1.805869e-01 0.6064784 -0.4796253101
## 2101 2.2281781846 -2.497744e-01 0.6064784 -0.0263873186
## 2103 1.3065086224 -4.342745e-01 -1.6488632 0.7692046208
## 2205 0.8102250119 -1.472088e+00 -1.6488632 0.9861021238
## 72 -1.1749094299 -2.036494e-01 0.6064784 -1.2444805189
## 299 0.4557367187 3.729135e-01 0.6064784 1.0188976288
## 1043 0.5266343774 -4.803995e-01 -1.6488632 0.4765389483
## 1049 0.0033980563 6.840555e-01 0.6064784 -0.5943800791
## 1240 -1.3876024058 -5.495871e-01 -1.6488632 -0.6329512266
## 1561 -3.0891462131 -2.958994e-01 0.6064784 -1.2315751703
## 1723 0.3139414014 2.345384e-01 0.6064784 0.4444835110
## 1992 1.2356109637 -3.881495e-01 0.6064784 3.2986851553
## 2005 -0.7229027182 2.702227e+00 -1.6488632 0.2794976165
## 2088 1.0938156464 -1.113993e-01 0.6064784 1.8999573189
## 2094 0.1721460842 -4.221178e-02 0.6064784 1.3215642758
## 2097 -0.0405468917 -3.420245e-01 0.6064784 0.1806992575
## 84 -2.3801696267 -2.728369e-01 0.6064784 0.0931414304
## 273 -0.9314495891 7.310078e-02 -1.6488632 -0.8703617053
## 370 -1.5293977231 -4.221178e-02 0.6064784 0.0153390810
## 418 1.1647133051 -2.267119e-01 0.6064784 -0.1138539229
## 473 0.3871206694 -2.597198e-01 0.6064784 -0.2923039491
## 646 -0.1823422090 -1.575243e-01 0.6064784 -0.2819228811
## 670 -0.2341107017 6.407558e-01 0.6064784 -0.3102587945
## 1381 -0.3241375263 -4.573370e-01 -1.6488632 -0.7372493390
## 1512 -3.0891462131 -3.650870e-01 0.6064784 -0.2073626987
## 1738 1.1647133051 -7.802122e-01 0.6064784 2.6918171711
## 1928 0.2430437428 -6.648996e-01 -1.6488632 -0.4501710653
## 1973 0.1721460842 1.192258e-01 0.6064784 -0.7891405692
## 1976 -0.2532398676 1.318477e+00 0.6064784 -1.6749603286
## 2024 0.1721460842 2.576009e-01 0.6064784 0.8379954221
## 2049 -1.7420906990 -3.189619e-01 0.6064784 0.3470897155
## 2070 -1.2458070886 5.804761e-01 0.6064784 3.2003970181
## 2079 -0.9622164540 -4.112120e-01 0.6064784 1.2781261064
## 2080 -0.6786258195 1.192258e-01 -1.6488632 0.1466999689
## 2083 0.8811226705 7.649762e-01 0.6064784 1.1230204500
## 2085 0.6684296946 -9.647123e-01 0.6064784 -0.8484660776
## 2086 -0.3950351849 -4.112120e-01 -1.6488632 0.4930150370
## 2090 0.5975320360 -9.877748e-01 0.6064784 -0.5021197890
## 2091 0.5266343774 -4.573370e-01 0.6064784 -0.4612442914
## 2092 2.0863828674 -1.033900e+00 0.6064784 -0.3219493163
## 75 -1.5293977231 -7.110247e-01 -1.6488632 -0.1982439922
## 106 0.0593388237 2.806634e-01 0.6064784 -0.9583091076
## 413 0.0303507669 -4.342745e-01 0.6064784 1.4047380405
## 432 0.3848390601 -2.497744e-01 0.6064784 -1.0147492146
## 531 -0.3387106733 -2.574509e-01 0.6064784 0.9393561632
## 839 0.5894209972 -4.345934e-01 0.6064784 0.4291227230
## 1062 -1.5293977231 -3.881495e-01 0.6064784 -1.4311653755
## 1163 0.2430437428 -1.914927e-02 0.6064784 2.0526884984
## 1177 -0.8204211367 -4.803995e-01 0.6064784 3.5111002183
## 1855 0.1721460842 -5.957121e-01 0.6064784 0.7341882594
## 2033 0.6684296946 -7.340872e-01 -1.6488632 1.3989462845
## 2071 0.3139414014 -8.493998e-01 0.6064784 0.1586340626
## 2073 -0.8204211367 3.729135e-01 0.6064784 1.3438870410
## 2076 -0.0818071667 1.433876e+00 0.6064784 -0.4114934813
## 2084 1.3774062810 -1.805869e-01 0.6064784 0.1909376797
## 78 1.5900992569 -4.221178e-02 0.6064784 -0.5298878560
## 264 -0.3950351849 -6.418371e-01 0.6064784 -0.4908188891
## 315 -0.3950351849 1.422883e-01 0.6064784 1.8934778143
## 439 0.2157523960 -3.624574e-01 0.6064784 -0.2539000900
## 529 -0.3950351849 4.431916e+00 0.6064784 -0.3373115490
## 758 -0.6786258195 2.863665e+00 0.6064784 5.2173160969
## 1092 -0.5348835724 -7.110247e-01 0.6064784 -1.0132737843
## 1130 0.2430437428 5.574136e-01 0.6064784 -0.2986193849
## 1412 0.5266343774 -6.418371e-01 0.6064784 0.3906717927
## 1614 1.5900992569 4.616416e+00 0.6064784 -0.8200260161
## 1802 -0.0405468917 1.884134e-01 0.6064784 0.0461616685
## 1970 -0.4659328436 -9.877748e-01 0.6064784 -1.9791902382
## 1983 -0.8204211367 1.192258e-01 -1.6488632 -0.5166725148
## 2012 -1.3167047472 2.576009e-01 0.6064784 -0.7966128696
## 2053 1.3774062810 -3.189619e-01 0.6064784 1.3361599300
## 2057 1.0229179878 -5.957121e-01 0.6064784 0.3715296311
## 2058 0.5266343774 -1.805869e-01 0.6064784 1.6542593348
## 2059 -0.0405468917 -2.267119e-01 0.6064784 4.2192309197
## 2074 -2.1674766508 4.639478e+00 0.6064784 0.8735869638
## 2075 -0.7495234781 -6.527430e-02 0.6064784 0.5081882731
## 2215 -0.6077281608 -2.036494e-01 -1.6488632 1.1246392083
## 37 -0.0405468917 -8.955248e-01 0.6064784 -0.6422146539
## 65 -0.3241375263 -4.342745e-01 -1.6488632 -0.2943320490
## 100 0.0897055054 8.109027e-01 0.6064784 -0.1856226756
## 108 0.6542587061 1.136927e-01 0.6064784 0.3008846709
## 173 0.5266343774 -4.342745e-01 0.6064784 0.0116628494
## 207 -0.3241375263 3.913244e-03 0.6064784 0.5184947465
## 824 -0.0578351518 -2.599659e-01 0.6064784 -0.6360145006
## 1141 -0.0843815387 2.310165e+00 0.6064784 -0.1909103914
## 1145 -0.2288393594 -6.830397e-01 -1.6488632 -0.5325831285
## 1618 0.1012484255 -2.728369e-01 0.6064784 0.8338977116
## 1812 -0.7495234781 -3.420245e-01 0.6064784 0.1722235826
## 1856 -1.9547836749 3.037259e-01 0.6064784 0.1123572531
## 1937 -1.8838860163 -4.573370e-01 0.6064784 0.2383540430
## 2047 1.8736898915 6.957886e-01 0.6064784 1.1874130422
## 2061 1.5192015983 -3.881495e-01 0.6064784 0.4149031639
## 2062 0.5266343774 -1.113993e-01 0.6064784 -1.0249847098
## 2063 1.2356109637 3.278790e+00 0.6064784 1.9919984802
## 2221 0.1174628952 6.361838e-01 0.6064784 0.5044248970
## 295 0.7260641716 -4.836585e-01 0.6064784 0.3542131013
## 337 0.5981741944 -4.606581e-01 0.6064784 0.7016957377
## 372 -0.8903706874 -5.632262e-01 -1.6488632 -1.1448349402
## 546 -0.1215642872 7.854448e-01 0.6064784 -0.9731172151
## 874 -0.7727976601 -3.905125e-01 -1.6488632 -0.8976072201
## 972 0.4295455887 -2.538487e-01 0.6064784 1.0250484258
## 1210 -1.7420906990 -7.340872e-01 0.6064784 -0.0133917228
## 1298 -0.7495234781 -1.575243e-01 0.6064784 0.3268704416
## 1498 -2.5219649440 -2.958994e-01 -1.6488632 -0.9127761151
## 1979 0.1721460842 -8.263372e-01 0.6064784 -0.2156210486
## 2066 1.5192015983 6.727261e-01 0.6064784 1.0837731653
## 383 1.3065086224 -5.265246e-01 0.6064784 1.0167726732
## 434 -0.1823422090 -4.573370e-01 0.6064784 -0.9587920520
## 843 0.2738950192 9.163161e-02 0.6064784 -0.5476623780
## 865 0.4466433609 -3.442337e-01 0.6064784 0.5667127034
## 922 -0.3308085422 -3.332508e-01 -1.6488632 -0.7248708183
## 1418 0.5975320360 -4.342745e-01 0.6064784 -0.1287715401
## 1485 -0.6786258195 -8.724623e-01 -1.6488632 2.9908195730
## 1842 -0.0293480677 -1.056962e+00 0.6064784 -0.0577787073
## 2037 1.1647133051 -5.265246e-01 0.6064784 -0.0695303553
## 2052 -2.6637602613 -1.344618e-01 -1.6488632 -0.0089870093
## 371 0.0825407079 -2.607257e-01 0.6064784 -0.5012130430
## 376 -0.0107659460 7.484398e-01 0.6064784 -0.7200164645
## 377 0.3139414014 1.687477e+00 0.6064784 -0.5266257697
## 506 0.5021765263 -2.281353e-01 0.6064784 -0.4288375512
## 728 0.2366525025 -6.459497e-01 0.6064784 -0.5318030125
## 826 0.1105749182 -4.870829e-01 -1.6488632 -0.5580454839
## 1642 0.3848390601 -2.267119e-01 -1.6488632 0.1997916611
## 1707 -1.6002953817 8.572262e-01 -1.6488632 -0.5512763783
## 1751 0.0303507669 -4.342745e-01 0.6064784 -0.0439343000
## 1864 1.4483039396 -1.113993e-01 -1.6488632 -1.0351997164
## 1895 -2.0965789922 -7.571497e-01 0.6064784 -1.1455475145
## 1998 -0.6786258195 -1.264525e+00 0.6064784 0.1955524821
## 31 -1.5293977231 -5.265246e-01 0.6064784 -0.1763635195
## 35 -1.6002953817 -8.263372e-01 0.6064784 -0.7662582017
## 90 -0.4821664768 -3.590723e-01 -1.6488632 -0.9132323839
## 312 -0.3101972756 -3.802805e-01 0.6064784 -0.4683186747
## 457 0.0303507669 -6.879622e-01 0.6064784 0.7658204324
## 466 0.8811226705 -3.650870e-01 0.6064784 -0.7068931851
## 870 -1.6711930404 7.419137e-01 -1.6488632 -0.4585179550
## 913 -1.0051587419 -5.879935e-01 -1.6488632 -0.8824316965
## 1013 0.4898738520 -3.903087e-01 0.6064784 -0.0868354145
## 1117 -2.0965789922 -7.110247e-01 0.6064784 1.4031282258
## 1306 0.3848390601 1.526039e+00 -1.6488632 0.3215741509
## 1619 0.8102250119 4.882260e-01 0.6064784 -0.9573556019
## 1756 0.9520203292 -4.342745e-01 0.6064784 -0.2026620395
## 2026 0.4763998527 -3.828844e-01 0.6064784 0.0736766825
## 2028 -0.1114445504 -5.726496e-01 0.6064784 -0.6981535661
## 2038 1.0534590550 -4.418251e-01 0.6064784 0.7837957679
## 2039 1.2356109637 -8.833681e-02 0.6064784 0.4590612783
## 2043 -0.6077281608 -8.955248e-01 -1.6488632 1.3242271776
## 2227 -2.2383743095 3.913244e-03 0.6064784 -1.1533435369
## 162 -1.2458070886 -8.032747e-01 -1.6488632 0.5617769173
## 528 0.4609991194 -2.946182e-01 0.6064784 0.6695664283
## 581 -0.1736498654 1.106888e+00 0.6064784 -0.5837931358
## 600 0.5975320360 5.343511e-01 0.6064784 -0.1379035804
## 601 -0.9744206869 1.420392e+00 0.6064784 -0.7905687382
## 814 0.1420741971 -4.651524e-01 0.6064784 -0.3520775846
## 1318 -0.3241375263 -1.010837e+00 0.6064784 1.5279507888
## 1477 -0.7495234781 -2.728369e-01 -1.6488632 0.8141774804
## 1982 1.3774062810 3.498509e-01 0.6064784 0.7246644600
## 2029 0.0303507669 3.498509e-01 0.6064784 0.1973769389
## 2034 0.2575626212 -1.012409e-01 -1.6488632 2.3888594097
## 2041 0.1012484255 5.003827e-02 0.6064784 -0.1707438023
## 2042 1.1647133051 -4.803995e-01 0.6064784 -0.1405553844
## 2224 1.2356109637 -5.957121e-01 0.6064784 3.0615134403
## 2229 -0.0405468917 -2.958994e-01 0.6064784 -0.2596866330
## 612 -0.3702139572 1.094949e+00 0.6064784 -1.0637597320
## 703 -2.8055555786 -6.418371e-01 -1.6488632 -0.1375172248
## 916 -0.4910024662 -4.943792e-01 0.6064784 -0.9631002707
## 994 -0.2806824385 1.890067e-01 -1.6488632 0.0490781126
## 1188 0.9958240158 -2.578842e-01 0.6064784 0.0926485200
## 1216 1.0938156464 9.616330e-02 0.6064784 -0.4641189607
## 1890 0.5975320360 -6.187746e-01 0.6064784 0.8895513091
## 1991 -0.6786258195 -1.344618e-01 0.6064784 -0.1286642191
## 2031 -0.7495234781 -3.420245e-01 0.6064784 -0.2044006395
## 2032 1.4483039396 -4.342745e-01 0.6064784 1.0778383147
## 97 -0.8278780546 1.038550e+00 0.6064784 -0.7424325677
## 201 1.7318945742 -4.221178e-02 0.6064784 0.3953841594
## 341 -1.5293977231 1.318477e+00 0.6064784 -0.9636214965
## 717 0.0961100568 -5.660364e-01 0.6064784 -0.7209974437
## 1364 -0.0405468917 -3.650870e-01 0.6064784 2.3040521364
## 1439 -0.3248956612 -4.887097e-01 -1.6488632 -0.8694987729
## 1474 -1.3876024058 -7.340872e-01 0.6064784 0.5763675887
## 1489 0.6684296946 -6.187746e-01 -1.6488632 -1.2340629533
## 1669 0.9520203292 3.670853e+00 0.6064784 0.8346131849
## 1687 0.3139414014 8.111012e-01 -1.6488632 -0.8641817727
## 1704 0.3139414014 -5.726496e-01 -1.6488632 -0.7286287807
## 1706 -0.3950351849 -5.495871e-01 -1.6488632 0.1157363311
## 1780 0.4557367187 -8.833681e-02 0.6064784 0.9623931292
## 1984 0.6684296946 -4.573370e-01 -1.6488632 0.9435444976
## 2022 0.8102250119 -3.650870e-01 0.6064784 -0.0232964742
## 2025 1.0938156464 -5.265246e-01 -1.6488632 -0.1040976786
## 2217 -1.5293977231 -4.112120e-01 -1.6488632 -0.4353929654
## 2222 0.4351046769 -3.323013e-01 -1.6488632 2.2977824004
## 135 -1.1672440431 2.260644e-01 -1.6488632 -1.2094272018
## 166 -3.1600438718 3.267884e-01 0.6064784 -1.3128593191
## 468 0.5266343774 -8.955248e-01 0.6064784 1.0576619692
## 626 -1.8129883577 3.301853e+00 0.6064784 -1.2849405310
## 1031 0.0633329958 -3.684865e-01 0.6064784 -0.8452616616
## 1362 1.7318945742 1.018664e+00 0.6064784 -0.4405831977
## 1510 -1.9547836749 -3.650870e-01 0.6064784 -0.3523748161
## 1625 0.6242500072 -2.864494e-01 0.6064784 0.6437230929
## 1696 -0.4659328436 1.422883e-01 0.6064784 -0.0131063465
## 1699 -0.2532398676 -1.010837e+00 0.6064784 0.4593474676
## 1705 1.2636651630 -6.423827e-01 0.6064784 0.0697341988
## 1715 -0.1823422090 -1.575243e-01 0.6064784 0.4418756110
## 1861 1.1647133051 -6.187746e-01 0.6064784 0.9893414290
## 1941 0.5975320360 2.310165e+00 0.6064784 0.2660541151
## 1964 0.5975320360 1.249289e+00 -1.6488632 0.3276860810
## 2218 1.3821546708 -5.455031e-01 -1.6488632 1.4703063989
## 2228 -0.7587708426 1.247937e+00 0.6064784 -0.4483608006
## 2230 1.3065086224 -7.110247e-01 0.6064784 2.3535628850
## 255 1.3065086224 -6.187746e-01 0.6064784 0.3219766046
## 819 -0.4295823612 -2.882506e-01 0.6064784 -0.7508093498
## 917 -0.3787870552 7.420615e-01 0.6064784 -0.6335160010
## 1085 0.2086875102 1.246345e+00 0.6064784 -0.1486573855
## 1187 -0.6786258195 1.041726e+00 0.6064784 1.0808433023
## 1394 0.9520203292 -5.726496e-01 0.6064784 0.2548795235
## 1479 1.3065086224 -1.805869e-01 0.6064784 -0.8825673212
## 1517 0.9520203292 1.157039e+00 -1.6488632 0.0144434386
## 2009 0.7393273533 1.064789e+00 0.6064784 -0.0403555578
## 2223 0.1012484255 -5.265246e-01 0.6064784 -0.4676251727
## 2225 -0.5368305022 -4.112120e-01 -1.6488632 2.4778865822
## 2226 0.3848390601 -1.033900e+00 0.6064784 0.1049735692
## 27 0.6684296946 -4.803995e-01 0.6064784 -1.0949823843
## 105 0.9520203292 -4.573370e-01 0.6064784 1.3760833370
## 147 0.6915586903 -4.410241e-01 0.6064784 0.4016420769
## 224 0.1565816986 -5.771358e-01 0.6064784 -1.0811715264
## 274 -0.1823422090 -4.573370e-01 0.6064784 0.8279853010
## 356 1.3774062810 2.056477e+00 0.6064784 0.4093358879
## 431 -0.0537298661 1.414284e+00 0.6064784 -0.3713031910
## 551 0.3848390601 -1.195337e+00 0.6064784 0.3225034530
## 962 -0.8383952111 -1.601245e-01 0.6064784 -0.9002695662
## 997 0.4245619101 -1.065979e-01 0.6064784 -0.6843632386
## 1389 0.5266343774 5.804761e-01 0.6064784 -0.7846100904
## 1831 -1.0331141127 -4.342745e-01 0.6064784 -0.7778488683
## 1996 0.2430437428 9.494763e-01 0.6064784 0.1474726800
## 1999 1.5900992569 -3.189619e-01 0.6064784 -0.2597138761
## 2008 0.5975320360 -6.879622e-01 0.6064784 0.7003290778
## 2216 -1.1749094299 -4.342745e-01 -1.6488632 -1.2848810917
## 107 -1.2458070886 -8.955248e-01 0.6064784 -0.0969264763
## 148 1.8027922328 -7.110247e-01 0.6064784 0.5607658001
## 182 -0.1768015705 -2.958994e-01 -1.6488632 -0.1587140954
## 414 -3.0182485545 -3.650870e-01 0.6064784 -1.2010691798
## 511 -0.0405468917 -1.914927e-02 0.6064784 -0.4577681528
## 699 0.6583120698 -4.152127e-01 0.6064784 2.2731788542
## 1017 -0.4892799367 -2.361457e-01 0.6064784 -1.0494281317
## 1366 -1.5293977231 -4.112120e-01 -1.6488632 2.1487024527
## 1672 -0.8204211367 -1.575243e-01 -1.6488632 -0.0749259179
## 1931 0.3848390601 -2.497744e-01 0.6064784 -0.2530492428
## 1966 0.9028921710 -2.008534e-01 0.6064784 1.8914026186
## 1994 0.1721460842 -2.958994e-01 0.6064784 0.6846448822
## 2002 0.4557367187 -5.034620e-01 0.6064784 2.0148578506
## 2007 0.1073902798 -1.896484e-01 0.6064784 -0.2924979750
## 2 -0.3241375263 -4.573370e-01 -1.6488632 -0.7137138466
## 143 1.4483039396 -1.344618e-01 0.6064784 0.0658674527
## 160 1.6609969155 -7.340872e-01 0.6064784 0.8358294893
## 688 -0.8668652606 -2.360700e-01 0.6064784 -1.1389963594
## 776 0.1951344997 -2.605057e-01 0.6064784 1.5174761059
## 850 -1.2126460771 6.244720e-01 0.6064784 -1.2281429161
## 1194 0.7393273533 9.956013e-01 0.6064784 1.2390702105
## 1849 -0.0550486725 -7.825202e-01 -1.6488632 -0.1596778488
## 5 -0.7495234781 -1.344618e-01 0.6064784 -1.0770383153
## 55 -1.4585000645 -3.881495e-01 0.6064784 0.0766408287
## 347 -1.1040117713 9.494763e-01 0.6064784 -0.7421109797
## 856 -0.7241787222 1.194754e+00 -1.6488632 -0.9793936093
## 1972 3.0789500883 -6.648996e-01 -1.6488632 -0.9345011850
## 1977 -1.9547836749 -3.189619e-01 -1.6488632 -1.5787930586
## 1980 0.0303507669 8.802888e-01 -1.6488632 -0.7573943826
## 1981 0.2430437428 -7.110247e-01 -1.6488632 -0.2444635638
## 1985 -1.3876024058 9.494763e-01 0.6064784 -0.8871874896
## 1987 1.0229179878 -5.495871e-01 0.6064784 -0.1106830755
## 1995 0.9520203292 5.343511e-01 0.6064784 -0.4708974309
## 2207 -0.1621835084 -5.328688e-01 -1.6488632 -0.3629342754
## 244 -0.0357672523 3.372700e-02 0.6064784 -0.5071842111
## 308 -2.4510672854 -6.648996e-01 0.6064784 0.4927779550
## 560 0.1721460842 -8.032747e-01 -1.6488632 0.5807040272
## 561 0.5699264395 -1.525443e-01 0.6064784 0.3215997851
## 1173 0.5975320360 -3.189619e-01 0.6064784 0.1745175687
## 1190 -1.4585000645 -4.342745e-01 0.6064784 0.1075492729
## 1269 -0.3241375263 -6.187746e-01 -1.6488632 -0.7776002413
## 1453 -0.0405468917 -4.573370e-01 -1.6488632 -0.3136104756
## 1543 -0.0405468917 -4.573370e-01 0.6064784 0.6331001452
## 1681 -0.0405468917 -9.647123e-01 0.6064784 -0.7796686589
## 1800 -0.0848237203 -4.234339e-01 0.6064784 0.0143785280
## 1873 1.1642613361 -2.395192e-01 0.6064784 2.5922940319
## 1971 1.1647133051 1.325183e+00 0.6064784 0.3282985323
## 1974 -0.3241375263 6.266011e-01 0.6064784 -0.0156882418
## 130 0.8811226705 -4.342745e-01 0.6064784 0.2528189606
## 721 1.1559941217 -3.173597e-01 -1.6488632 0.3607149870
## 837 -0.4317770784 5.974603e-01 0.6064784 -0.9961651160
## 1741 1.4483039396 -5.034620e-01 0.6064784 2.3084075797
## 1860 -0.8913187954 9.264138e-01 0.6064784 -0.5434891025
## 1957 0.7393273533 -1.344618e-01 -1.6488632 -0.0079618383
## 1960 -0.0405468917 -3.189619e-01 0.6064784 -0.0819259293
## 1965 1.0229179878 -8.032747e-01 0.6064784 -0.3376181804
## 1968 1.7318945742 -4.803995e-01 0.6064784 0.4586820776
## 58 1.9445875501 -9.877748e-01 0.6064784 -0.8804745619
## 195 -0.6786258195 8.802888e-01 0.6064784 0.1811909828
## 1228 0.7534646513 6.266011e-01 0.6064784 2.5114535208
## 1257 -0.0405468917 -2.330995e-01 0.6064784 0.6470875349
## 1592 0.8102250119 3.763103e+00 0.6064784 -0.5479673145
## 1596 0.8811226705 -2.958994e-01 0.6064784 0.0961342225
## 1750 0.5975320360 -6.418371e-01 -1.6488632 -0.2243945393
## 1837 -0.9622164540 4.570291e+00 -1.6488632 3.0564804692
## 1942 -0.7495234781 1.871977e+00 -1.6488632 -0.9695548139
## 1961 0.8102250119 -5.034620e-01 -1.6488632 0.1940837177
## 1962 -1.8129883577 -5.495871e-01 0.6064784 -0.3569554709
## 1963 1.1647133051 -5.726496e-01 0.6064784 1.3973328925
## 422 0.6684296946 -4.342745e-01 0.6064784 0.3242303453
## 1401 0.6684296946 1.641352e+00 0.6064784 -0.4668326485
## 1525 2.0154852087 -7.571497e-01 0.6064784 0.2218956567
## 1646 -0.6786258195 -5.957121e-01 0.6064784 -0.5302056912
## 1929 -0.7495234781 2.240977e+00 -1.6488632 1.0567473017
## 1940 2.0863828674 2.148727e+00 0.6064784 0.4662160108
## 1945 -1.1040117713 -5.726496e-01 0.6064784 0.3349839082
## 1946 -0.3950351849 -8.263372e-01 -1.6488632 -0.0761004120
## 1948 0.9520203292 -2.036494e-01 0.6064784 0.0402590142
## 1949 1.7318945742 -9.185873e-01 0.6064784 -0.0843406515
## 1950 0.1012484255 -1.080025e+00 0.6064784 -0.0669546516
## 1953 0.5975320360 -6.527430e-02 0.6064784 1.0913714912
## 1955 1.3774062810 -2.036494e-01 0.6064784 -0.3869858343
## 202 1.3065086224 -6.418371e-01 0.6064784 0.8223070451
## 494 1.0938156464 1.595227e+00 0.6064784 0.6728089105
## 740 -1.1152430891 1.277483e+00 0.6064784 -1.1241156364
## 780 -0.2961529648 -3.323190e-01 0.6064784 -0.8851633900
## 1069 1.3065086224 -5.495871e-01 -1.6488632 0.0483435041
## 1537 -0.5368305022 7.310078e-02 -1.6488632 0.5802690898
## 1920 0.3095457076 -3.506023e-01 0.6064784 0.0892508614
## 1926 -0.6077281608 -5.034620e-01 -1.6488632 0.4721830578
## 1938 -0.7495234781 -9.185873e-01 0.6064784 -0.1886566507
## 1939 0.8102250119 -1.575243e-01 0.6064784 0.1892205439
## 1947 -0.2532398676 -2.728369e-01 -1.6488632 2.7897123091
## 1951 2.6535641365 -2.267119e-01 0.6064784 0.4794164921
## 25 0.6499357972 -4.426689e-01 0.6064784 0.2329704747
## 134 -0.0405468917 -7.340872e-01 0.6064784 0.8759138780
## 323 0.1498265909 1.349416e+00 0.6064784 -0.0699320500
## 354 -2.0256813336 4.190385e-01 0.6064784 0.1348283165
## 381 -2.8055555786 -4.342745e-01 -1.6488632 -0.3338619458
## 1568 0.8811226705 -4.573370e-01 0.6064784 -1.4494521051
## 1766 -0.1823422090 7.419137e-01 -1.6488632 0.9047161478
## 1782 0.3139414014 -8.032747e-01 0.6064784 -0.6264296880
## 1903 0.3848390601 -3.189619e-01 -1.6488632 -0.0753820321
## 1923 -4.2944064099 -4.573370e-01 0.6064784 -1.0042348242
## 1925 0.5975320360 -7.340872e-01 0.6064784 0.4397506554
## 1930 1.5900992569 -1.113993e-01 0.6064784 0.1088371247
## 1932 0.7393273533 -4.573370e-01 0.6064784 -0.6531418816
## 1934 -1.3876024058 -9.416498e-01 0.6064784 -1.8257003204
## 385 -0.3657487723 1.133657e+00 0.6064784 0.0593292279
## 548 -0.6077281608 -1.805869e-01 0.6064784 -0.9469330830
## 1078 -0.0677493981 1.049209e+00 0.6064784 -0.4334113200
## 1429 -0.2532398676 5.343511e-01 -1.6488632 -0.1817022507
## 1581 0.5975320360 4.109041e+00 0.6064784 0.4920315863
## 1609 0.8102250119 -8.493998e-01 0.6064784 0.4852923163
## 1905 1.0229179878 2.697576e-02 0.6064784 0.7520313115
## 1908 -0.7495234781 -5.034620e-01 0.6064784 -0.9400578323
## 1916 0.7393273533 -3.189619e-01 0.6064784 -0.2729036253
## 1918 1.3774062810 3.267884e-01 0.6064784 5.5160762602
## 1919 0.5266343774 2.114759e-01 0.6064784 -0.3480283161
## 1921 1.3065086224 -7.110247e-01 0.6064784 1.1395612972
## 1927 1.0229179878 -2.497744e-01 0.6064784 -0.3338619458
## 1933 0.7393273533 -3.881495e-01 0.6064784 -0.5806319203
## 77 0.8102250119 -3.650870e-01 0.6064784 -0.4638606827
## 184 0.7317743187 -2.230051e-01 0.6064784 2.3030739551
## 498 -0.3166487413 -3.170750e-01 -1.6488632 -0.8467466946
## 555 0.4340736130 6.122724e-01 0.6064784 0.4560700295
## 574 1.0938156464 2.840602e+00 0.6064784 1.4753910236
## 783 0.7393273533 -4.112120e-01 -1.6488632 0.0532877820
## 956 -0.2741935863 1.222503e+00 0.6064784 -0.7836789397
## 1584 0.6684296946 -4.573370e-01 0.6064784 -0.9392443009
## 1797 0.2430437428 -3.881495e-01 0.6064784 -0.3205183698
## 1863 1.5192015983 -1.057669e-01 0.6064784 0.9393979304
## 1874 -0.2532398676 -6.527430e-02 0.6064784 -0.1113855402
## 1882 -0.0405468917 1.180101e+00 -1.6488632 3.8455645407
## 1898 -0.4659328436 1.180101e+00 -1.6488632 1.9156932588
## 1911 -0.3241375263 5.003827e-02 0.6064784 -0.1866282840
## 1913 -0.1823422090 -9.877748e-01 -1.6488632 -0.4175624090
## 1914 1.0925981316 -5.011621e-01 0.6064784 0.6839614188
## 1915 0.8811226705 -8.032747e-01 0.6064784 0.0281164111
## 115 2.7953594537 -9.877748e-01 0.6064784 0.1624976182
## 259 0.5266343774 1.192258e-01 0.6064784 -0.2382849413
## 321 1.6609969155 2.697576e-02 0.6064784 -0.2798014379
## 825 -0.6478668766 -1.126832e-01 -1.6488632 -0.4933500236
## 1370 0.8811226705 -1.344618e-01 0.6064784 0.0931414304
## 1621 1.9445875501 -8.263372e-01 -1.6488632 -1.2987076672
## 1643 1.4483039396 4.062916e+00 0.6064784 2.0552105417
## 1688 0.3429730079 -3.651952e-01 -1.6488632 0.5445036445
## 1865 0.1097733982 -1.113993e-01 -1.6488632 1.0688602435
## 1880 -0.5368305022 -1.805869e-01 0.6064784 -0.4235905463
## 1886 1.8736898915 -5.726496e-01 0.6064784 1.1016789053
## 1889 0.1721460842 7.649762e-01 0.6064784 -0.7165243938
## 1892 -0.5368305022 5.343511e-01 -1.6488632 -0.4652228337
## 1897 -0.6786258195 5.343511e-01 -1.6488632 2.0621059150
## 1899 1.0229179878 -8.493998e-01 -1.6488632 -0.2483405345
## 1900 -0.1823422090 -5.495871e-01 -1.6488632 0.3440149692
## 1904 -0.1114445504 -7.340872e-01 0.6064784 -0.5007130585
## 1909 0.3139414014 -5.034620e-01 -1.6488632 0.8585508754
## 83 -0.0405468917 -4.342745e-01 -1.6488632 0.0216334569
## 208 0.4764015968 -8.833681e-02 0.6064784 -0.1495381510
## 329 0.3480268468 -2.040157e-01 0.6064784 -0.2792299541
## 449 -0.1114445504 -9.877748e-01 0.6064784 -1.9231939728
## 451 1.5900992569 -5.495871e-01 0.6064784 2.1304425534
## 605 -0.1823422090 -8.955248e-01 0.6064784 0.4789657441
## 754 0.5975320360 5.585041e+00 0.6064784 -0.2458970656
## 822 2.4408711605 -2.036494e-01 0.6064784 -0.5944641321
## 844 0.0051235465 -5.331303e-01 -1.6488632 -0.1853868923
## 1074 -0.9662448540 -7.461925e-01 -1.6488632 -1.2206181980
## 1185 1.0229179878 -5.265246e-01 0.6064784 0.1997916611
## 1829 2.3699735019 -2.497744e-01 0.6064784 0.1635708280
## 1843 0.2430437428 7.310078e-02 0.6064784 -1.2239745447
## 1888 1.1647133051 -1.033900e+00 0.6064784 1.9891008136
## 1893 -2.0256813336 -5.265246e-01 0.6064784 -0.9652313113
## 1894 -2.3801696267 -6.648996e-01 0.6064784 -0.5912023406
## 30 -0.2471762752 -4.955058e-01 0.6064784 -0.0229671846
## 211 0.8811226705 -7.802122e-01 -1.6488632 3.3606630251
## 666 -0.4605913551 -1.798594e-01 -1.6488632 -0.1066541596
## 895 -0.0415173212 1.296045e+00 0.6064784 -0.5651087689
## 1165 0.5266343774 -4.342745e-01 0.6064784 0.4318303667
## 1452 -0.3805885349 -3.796735e-01 0.6064784 -1.1055503792
## 1526 0.0597156143 -2.325654e-01 -1.6488632 -0.0511455348
## 1698 -0.8204211367 -3.189619e-01 0.6064784 -1.2424414202
## 1862 0.6060276996 -2.563752e-01 0.6064784 0.0964931783
## 1883 -0.4659328436 2.345384e-01 0.6064784 0.4372393444
## 1884 0.0303507669 -2.728369e-01 0.6064784 -0.0968614333
## 1885 -1.2458070886 2.806634e-01 -1.6488632 0.2035546032
## 1887 -1.3876024058 3.959760e-01 0.6064784 0.2789945493
## 96 -0.8083731481 -6.774257e-01 0.6064784 -0.9879573746
## 311 0.5266343774 -6.187746e-01 -1.6488632 -1.0125598665
## 437 0.2700619259 -6.208447e-01 -1.6488632 0.3672903501
## 599 0.6630629149 -4.400354e-01 0.6064784 0.2298060066
## 619 1.0433787699 -3.181994e-01 0.6064784 1.1311762842
## 778 0.7263768431 -2.901905e-01 0.6064784 0.8406849157
## 1008 0.2533065497 -2.654563e-01 0.6064784 -0.6194205708
## 1082 -0.1883280026 6.346104e+00 0.6064784 -0.5534159185
## 1732 1.5900992569 -1.914927e-02 0.6064784 2.5530994297
## 1784 -1.0331141127 5.654229e+00 -1.6488632 -0.7367533148
## 1820 -1.3876024058 -1.010837e+00 0.6064784 -0.5093317593
## 1834 1.0229179878 -6.418371e-01 0.6064784 -0.1384304289
## 1869 1.0229179878 -1.310650e+00 0.6064784 -0.6966337115
## 1870 0.3848390601 2.079539e+00 -1.6488632 2.3799445298
## 1871 0.7393273533 -6.648996e-01 0.6064784 1.8673585692
## 1875 1.5192015983 -1.033900e+00 -1.6488632 -0.3710026731
## 1876 -0.0405468917 -7.110247e-01 0.6064784 1.1589423176
## 82 1.0229179878 -4.803995e-01 0.6064784 1.2080186717
## 258 -1.4207515555 2.043410e-01 -1.6488632 -0.7990056845
## 310 -0.5178432350 -4.973937e-01 0.6064784 -0.9141926991
## 345 -0.1823422090 2.576009e-01 0.6064784 0.5683030356
## 464 0.4449574040 -1.712783e-01 0.6064784 1.8991422037
## 595 0.4557367187 4.570291e+00 0.6064784 0.0081271107
## 676 -0.1490974447 8.588373e-02 0.6064784 -0.6786104715
## 841 0.4483656317 8.238358e-01 0.6064784 -0.0966937575
## 1434 0.0303507669 -5.034620e-01 0.6064784 3.8787819193
## 1605 -0.8913187954 -1.033900e+00 0.6064784 0.3174691230
## 1616 -1.7420906990 -4.112120e-01 0.6064784 -0.9102216397
## 1769 -3.1600438718 -5.957121e-01 -1.6488632 -0.5829002957
## 1833 -1.1040117713 -8.955248e-01 -1.6488632 1.7950510544
## 1846 -0.1823422090 -2.036494e-01 0.6064784 -0.7643264239
## 1858 -0.3950351849 -4.112120e-01 -1.6488632 -0.7672616529
## 1859 0.2430437428 -7.110247e-01 -1.6488632 0.2764188457
## 1866 2.5117688192 -5.495871e-01 0.6064784 0.4072645927
## 50 1.0229179878 -2.267119e-01 -1.6488632 -0.0155854613
## 52 0.5266343774 8.341637e-01 0.6064784 -0.6101354353
## 53 -0.8913187954 -8.032747e-01 0.6064784 -0.4211461044
## 250 -0.4659328436 7.649762e-01 0.6064784 0.4037551966
## 349 -2.0965789922 -3.650870e-01 -1.6488632 -1.1758487479
## 396 1.2356109637 -1.344618e-01 0.6064784 -0.0550420221
## 868 0.0728239647 -2.928032e-01 -1.6488632 -0.5023064635
## 979 0.8811226705 -7.340872e-01 0.6064784 -0.1538846510
## 1191 0.0936853235 -4.722792e-01 -1.6488632 -1.0330235651
## 1409 0.7393273533 2.114759e-01 0.6064784 1.5959035483
## 1803 0.4557367187 -5.957121e-01 0.6064784 1.0544423396
## 1832 1.3774062810 -4.573370e-01 0.6064784 1.4930766278
## 1836 0.4557367187 7.419137e-01 0.6064784 -0.3691540396
## 1839 -0.6786258195 -5.726496e-01 -1.6488632 -0.0581409156
## 1841 0.1721460842 3.693915e+00 0.6064784 2.7817736223
## 1844 1.3774062810 -5.726496e-01 0.6064784 0.4602450613
## 1845 -0.6077281608 -7.110247e-01 -1.6488632 -0.2369148739
## 1848 -0.8913187954 -3.650870e-01 0.6064784 1.6442427093
## 1851 -0.3950351849 -1.056962e+00 0.6064784 -0.7138855601
## 1852 1.2356109637 -3.650870e-01 0.6064784 1.6868133676
## 1857 0.8811226705 -3.189619e-01 0.6064784 0.3427968761
## 2208 -0.6077281608 3.959760e-01 -1.6488632 0.6556452182
## 204 -0.6077281608 -2.958994e-01 0.6064784 -0.8351582752
## 369 1.5192015983 9.264138e-01 0.6064784 -0.1115611563
## 715 -0.7689677367 5.992804e-01 0.6064784 -0.9771421579
## 926 0.1213914458 8.817244e-01 0.6064784 0.2833103449
## 1219 1.8027922328 6.957886e-01 0.6064784 1.1259181167
## 1468 -0.1823422090 -1.379838e+00 0.6064784 1.1152933391
## 1480 -0.7495234781 -5.726496e-01 -1.6488632 -1.0877060214
## 1574 0.0198175067 -5.671319e-02 0.6064784 -0.3906431345
## 1823 0.8102250119 -8.032747e-01 0.6064784 1.8593497406
## 1827 1.3065086224 1.653508e-01 0.6064784 -0.3255982299
## 1847 1.2356109637 -1.010837e+00 0.6064784 0.5771859113
## 47 -0.6027692773 9.272789e-01 -1.6488632 -0.5940849122
## 66 0.1016714577 -4.931036e-01 0.6064784 -0.9298946472
## 405 0.3711056958 -4.218638e-01 0.6064784 -0.1644641527
## 818 -1.0984877632 -5.856672e-01 0.6064784 -1.1904439799
## 1133 -1.1040117713 -3.650870e-01 0.6064784 -0.7773659238
## 1576 0.8811226705 -6.418371e-01 0.6064784 1.8304374668
## 1811 -0.7495234781 -1.033900e+00 -1.6488632 -0.6069831252
## 1813 -1.7420906990 5.003827e-02 -1.6488632 -0.4690541929
## 1825 -0.8913187954 -2.728369e-01 0.6064784 -1.2769834464
## 1826 -1.1749094299 5.003827e-02 -1.6488632 -0.6160826034
## 1828 1.0229179878 -8.833681e-02 0.6064784 0.3943538781
## 181 0.0303507669 7.649762e-01 0.6064784 -0.1736853730
## 279 1.1647133051 -8.955248e-01 0.6064784 1.3501250733
## 307 0.5975320360 2.697576e-02 0.6064784 -0.1923445901
## 863 0.5115041158 -3.134422e-01 0.6064784 -0.0676364164
## 1011 0.1767504114 1.469528e+00 0.6064784 1.1658088879
## 1330 -0.0405468917 -4.803995e-01 -1.6488632 1.1284401600
## 1755 1.3774062810 -3.650870e-01 0.6064784 1.3466058393
## 1795 0.6684296946 -7.340872e-01 0.6064784 1.2470119637
## 1806 -0.6077281608 -8.833681e-02 0.6064784 -0.3746439208
## 1814 -0.0925081402 8.943485e-01 -1.6488632 4.3267625634
## 1815 0.4952785386 1.226226e+00 0.6064784 0.7026656090
## 1816 0.8102250119 -8.955248e-01 0.6064784 -0.7025095356
## 1818 0.3139414014 -5.265246e-01 0.6064784 0.1987721117
## 1821 -0.4449925813 -2.837686e-01 0.6064784 -0.8915630208
## 1990 0.7344706729 -2.119033e-01 0.6064784 -0.1109932239
## 126 -0.0405468917 -2.958994e-01 0.6064784 -0.5407231479
## 165 -0.1823422090 -8.724623e-01 -1.6488632 -0.1646739875
## 167 0.2333861417 -9.298053e-02 0.6064784 -0.7177468901
## 263 1.5900992569 -5.265246e-01 0.6064784 1.4754983445
## 268 -0.4659328436 -3.189619e-01 0.6064784 0.7260043462
## 781 -0.0552224816 -3.422698e-01 0.6064784 -0.3001832048
## 973 -0.1823422090 -5.265246e-01 0.6064784 -1.0664515127
## 1528 1.5900992569 -7.802122e-01 0.6064784 -0.1025637551
## 1673 1.7318945742 -2.728369e-01 0.6064784 -0.8849882350
## 1768 -0.7495234781 -1.113993e-01 -1.6488632 0.4351465851
## 1788 -0.8204211367 -5.726496e-01 -1.6488632 0.6622914537
## 1798 -0.2070349957 -7.571497e-01 0.6064784 -0.7587292217
## 1801 -0.1466337638 -1.805869e-01 -1.6488632 -1.1373527099
## 1804 -1.8838860163 3.071228e+00 0.6064784 -0.8503852294
## 1807 1.5900992569 1.526039e+00 0.6064784 -0.3981165538
## 1809 1.5192015983 -3.189619e-01 0.6064784 0.9008660074
## 43 -0.1114445504 -1.402900e+00 0.6064784 -0.8542920740
## 280 2.0154852087 -5.495871e-01 0.6064784 -0.0594690128
## 440 0.1012484255 -1.287588e+00 0.6064784 -0.3646201407
## 572 0.9520203292 -3.189619e-01 0.6064784 0.8700291106
## 1063 0.4624865948 -4.053534e-01 0.6064784 -0.1467053756
## 1111 -1.7420906990 3.498509e-01 0.6064784 -0.4753110065
## 1496 0.4557367187 -5.034620e-01 0.6064784 0.1290939610
## 1718 0.1012484255 2.114759e-01 0.6064784 0.1386558482
## 1719 -0.6077281608 6.727261e-01 0.6064784 1.4894545445
## 1774 -0.6786258195 1.064789e+00 -1.6488632 -0.8137396893
## 1779 -0.1829481025 8.639959e-01 -1.6488632 0.2806641522
## 1785 -0.0405468917 -5.495871e-01 -1.6488632 0.7331031823
## 1787 0.9134571025 -2.773449e-01 0.6064784 0.9443580852
## 1789 1.0229179878 -5.265246e-01 -1.6488632 0.2513021575
## 32 -0.2532398676 -5.034620e-01 -1.6488632 0.0520714775
## 226 -0.8176089279 -9.901383e-01 -1.6488632 -1.2216234254
## 361 0.1116373544 -2.975396e-01 -1.6488632 -0.5643632128
## 620 0.6730005702 -6.879622e-01 0.6064784 1.3326505337
## 710 0.5649832435 -3.814109e-01 0.6064784 -0.3911995627
## 790 -0.0405468917 -8.833681e-02 0.6064784 -0.3148661312
## 1344 -1.4585000645 6.323042e+00 -1.6488632 -1.4378169436
## 1383 -1.1040117713 -9.647123e-01 -1.6488632 -0.7334179797
## 1388 -1.1749094299 9.956013e-01 0.6064784 -1.5644503753
## 1417 -0.8913187954 -8.833681e-02 0.6064784 -0.1723975212
## 1443 -1.8838860163 -5.495871e-01 -1.6488632 -1.5518112385
## 1667 1.6609969155 -4.342745e-01 -1.6488632 6.8316008182
## 1720 -0.8204211367 -5.265246e-01 0.6064784 -0.7236671015
## 1748 0.4557367187 -3.650870e-01 -1.6488632 -0.5736502999
## 1765 -0.1114445504 -6.648996e-01 0.6064784 0.7160592682
## 1778 0.7393273533 -5.034620e-01 0.6064784 -0.4157095299
## 1781 0.1721460842 1.653508e-01 0.6064784 -0.9545421410
## 1783 1.4483039396 1.295414e+00 0.6064784 0.0663380140
## 2206 -1.9547836749 -8.833681e-02 0.6064784 0.5715438935
## 80 -0.8913187954 -2.958994e-01 0.6064784 -0.6015051554
## 297 -1.1047279312 1.186239e+00 0.6064784 -0.9134124766
## 424 -0.5368305022 -5.957121e-01 -1.6488632 0.9414333404
## 438 0.9499897203 -4.789356e-01 0.6064784 0.8078737816
## 875 -0.8913187954 8.111012e-01 0.6064784 -0.6957281907
## 1073 -0.3241375263 1.422883e-01 0.6064784 1.6803383346
## 1227 1.7318945742 -2.365340e-01 0.6064784 0.6755198703
## 1419 -0.1114445504 5.003827e-02 0.6064784 -0.7305663078
## 1772 0.3712410529 -2.627826e-01 -1.6488632 0.7738020290
## 1775 1.3774062810 -5.726496e-01 0.6064784 0.1243450074
## 69 -0.7511889754 -4.112120e-01 0.6064784 -1.0982020139
## 217 1.0229179878 -4.573370e-01 0.6064784 0.8279735933
## 388 -0.4071981873 4.651635e-01 -1.6488632 0.6416568475
## 579 0.2972325256 -3.710941e-01 0.6064784 -0.4713012913
## 942 0.2686586195 -5.748728e-01 -1.6488632 0.1287990047
## 1093 0.5975320360 6.496636e-01 0.6064784 1.7499283289
## 1380 1.5900992569 -1.914927e-02 0.6064784 0.6765651449
## 1729 1.0938156464 -2.958994e-01 0.6064784 -0.1422939844
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## 671 -1.0595269567 4.378936e-01 -1.6488632 0.2274292990
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## 678 0.5050393348 -5.328019e-01 -1.6488632 0.3019222877
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## 509 -0.2261727410 -4.986365e-01 0.6064784 -0.8661794783
## 544 0.4134156695 -2.841487e-01 0.6064784 0.5012999991
## 627 -0.3681799048 4.733204e-01 0.6064784 -0.5389642023
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## 642 0.3802484764 -2.339460e-01 0.6064784 0.5374143185
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## 660 0.0434207574 -3.754514e-01 0.6064784 -0.4230690797
## 4 -0.2800035934 6.236986e-01 0.6064784 -0.5073736244
## 121 -0.3863233118 1.417291e+00 0.6064784 -0.8816783495
## 338 -0.0676471524 -2.200221e-01 0.6064784 0.0226286950
## 496 0.5296109376 -3.866031e-01 0.6064784 0.8488016947
## 580 -0.6272614272 1.989815e+00 -1.6488632 -0.0021515220
## 608 0.1388223434 7.541137e-01 -1.6488632 4.0087934416
## 615 0.4353505585 -3.022925e-01 0.6064784 0.1305470608
## 616 -0.3668968039 -4.762521e-01 -1.6488632 -0.6457420816
## 623 -0.3200971942 -3.652017e-01 0.6064784 -1.0072229791
## 782 0.6821294130 -3.005859e-01 0.6064784 0.9686349538
## attr(,"center")
## [1] 54.27563025 0.64369748 0.76526611 0.13221289 0.22072829
## [6] 0.40168067 0.46666667 0.05714286 0.84257703 0.12549020
## [11] 0.09579832 0.22184874 0.27282913 110.79271709 76.37983193
## [16] 20.13484594 16.22173669 501.35742297 0.40560224 25.03881507
## [21] 139.47056037 13.55719073 109.83032013 0.73109244 91.13830031
## attr(,"scale")
## [1] 11.0845083 0.4789061 0.4238324 0.3387221 0.4147376 0.4902380
## [7] 0.4988877 0.2321154 0.3641991 0.3312739 0.2943145 0.4154899
## [13] 0.4454137 17.9176033 14.4451920 7.3004751 5.0751008 220.4424148
## [19] 0.4910082 12.5276772 3.0834395 1.4104838 43.3604085 0.4433918
## [25] 43.1381575
## attr(,"nonsingular")
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
##
## attr(,"class")
## [1] "ncvsurv" "ncvreg"(SCAD_Train_CoxPH_Model <- SCAD_Train_CoxPH_Fit$model$beta)## 0.0111
## age 0.0027219110
## betablok -0.3787249214
## acei 0.0479447613
## angioten.II 0.0000000000
## anti.arrhy 0.0000000000
## anti.coag 0.0000000000
## aspirin -0.2074697706
## vasodilator 0.0000000000
## diuretic.loop 0.0000000000
## diuretic.thiazide 0.0202248333
## insulin 0.0814959210
## surgery.pacemaker 0.0000000000
## surgery.cabg 0.0000000000
## resting.systolic.bp -0.0016822946
## resting.hr 0.0028380170
## lvef.metabl -0.0118827416
## peak.vo2 -0.0642100265
## interval -0.0011147473
## cad 0.0953210387
## bun 0.0169187797
## sodium -0.0002547134
## hgb 0.0000000000
## glucose 0.0000000000
## male 0.5225160066
## crcl 0.0000000000(SCAD_Train_CoxPH_Lambda <- SCAD_Train_CoxPH_Fit$lambda)## [1] 0.01114746(SCAD_Train_CoxPH_Gamma <- SCAD_Train_CoxPH_Fit$gamma)## [1] 5##################################
# Conducting an internal validation
# of the SCAD model
# using a 50-cycle bootstrap
##################################
SCAD_Train_CoxPH_Model_InternalValidation <- validate(SCAD_Train_Predictors,
SCAD_Train_Time,
SCAD_Train_Status,
model.type = "scad",
alpha = 1,
lambda = SCAD_Train_CoxPH_Fit$lambda,
gamma = SCAD_Train_CoxPH_Fit$gamma,
method = "bootstrap",
boot.times = 50,
tauc.type = "UNO",
tauc.time = seq(12, 120, 12),
seed = 88888888)## Start bootstrap sample 1
## Start bootstrap sample 2
## Start bootstrap sample 3
## Start bootstrap sample 4
## Start bootstrap sample 5
## Start bootstrap sample 6
## Start bootstrap sample 7
## Start bootstrap sample 8
## Start bootstrap sample 9
## Start bootstrap sample 10
## Start bootstrap sample 11
## Start bootstrap sample 12
## Start bootstrap sample 13
## Start bootstrap sample 14
## Start bootstrap sample 15
## Start bootstrap sample 16
## Start bootstrap sample 17
## Start bootstrap sample 18
## Start bootstrap sample 19
## Start bootstrap sample 20
## Start bootstrap sample 21
## Start bootstrap sample 22
## Start bootstrap sample 23
## Start bootstrap sample 24
## Start bootstrap sample 25
## Start bootstrap sample 26
## Start bootstrap sample 27
## Start bootstrap sample 28
## Start bootstrap sample 29
## Start bootstrap sample 30
## Start bootstrap sample 31
## Start bootstrap sample 32
## Start bootstrap sample 33
## Start bootstrap sample 34
## Start bootstrap sample 35
## Start bootstrap sample 36
## Start bootstrap sample 37
## Start bootstrap sample 38
## Start bootstrap sample 39
## Start bootstrap sample 40
## Start bootstrap sample 41
## Start bootstrap sample 42
## Start bootstrap sample 43
## Start bootstrap sample 44
## Start bootstrap sample 45
## Start bootstrap sample 46
## Start bootstrap sample 47
## Start bootstrap sample 48
## Start bootstrap sample 49
## Start bootstrap sample 50print(SCAD_Train_CoxPH_Model_InternalValidation)## High-Dimensional Cox Model Validation Object
## Random seed: 88888888
## Validation method: bootstrap
## Bootstrap samples: 50
## Model type: scad
## ncvreg model gamma: 5
## ncvreg model alpha: 1
## ncvreg model lambda: 0.01114746
## Time-dependent AUC type: UNO
## Evaluation time points for tAUC: 12 24 36 48 60 72 84 96 108 120summary(SCAD_Train_CoxPH_Model_InternalValidation)## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7658423 0.7781017 0.6848162 0.6788424 0.6610580 0.6756440 0.6876452
## Min 0.7562769 0.7656373 0.6674727 0.6507736 0.6342292 0.6469130 0.6496756
## 0.25 Qt. 0.7625246 0.7749432 0.6773948 0.6744442 0.6558523 0.6692546 0.6753458
## Median 0.7664699 0.7787000 0.6853263 0.6800789 0.6619458 0.6770086 0.6874433
## 0.75 Qt. 0.7683688 0.7818902 0.6914573 0.6833197 0.6681648 0.6834903 0.7032024
## Max 0.7745815 0.7871087 0.7082630 0.6956888 0.6785181 0.6986667 0.7283556
## 96 108 120
## Mean 0.6135738 0.6282538 0.25947385
## Min 0.5652901 0.5318523 0.08551219
## 0.25 Qt. 0.5943249 0.5917744 0.20725953
## Median 0.6159158 0.6252510 0.26660433
## 0.75 Qt. 0.6282015 0.6615902 0.31016200
## Max 0.6634437 0.7337697 0.41747344SCAD_Train_IV_Summary <- data.frame(matrix(ncol=4, nrow=10))
names(SCAD_Train_IV_Summary) <- c("Model","Validation","MedianTDAUROC","Time")
for (i in 1:10){
SCAD_Train_IV_Summary[i,1] <- "SCAD"
SCAD_Train_IV_Summary[i,2] <- "Cross-Validation"
SCAD_Train_IV_Summary[i,3] <- summary(SCAD_Train_CoxPH_Model_InternalValidation)[[4,i]]
SCAD_Train_IV_Summary[i,4] <- i*12
}## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7658423 0.7781017 0.6848162 0.6788424 0.6610580 0.6756440 0.6876452
## Min 0.7562769 0.7656373 0.6674727 0.6507736 0.6342292 0.6469130 0.6496756
## 0.25 Qt. 0.7625246 0.7749432 0.6773948 0.6744442 0.6558523 0.6692546 0.6753458
## Median 0.7664699 0.7787000 0.6853263 0.6800789 0.6619458 0.6770086 0.6874433
## 0.75 Qt. 0.7683688 0.7818902 0.6914573 0.6833197 0.6681648 0.6834903 0.7032024
## Max 0.7745815 0.7871087 0.7082630 0.6956888 0.6785181 0.6986667 0.7283556
## 96 108 120
## Mean 0.6135738 0.6282538 0.25947385
## Min 0.5652901 0.5318523 0.08551219
## 0.25 Qt. 0.5943249 0.5917744 0.20725953
## Median 0.6159158 0.6252510 0.26660433
## 0.75 Qt. 0.6282015 0.6615902 0.31016200
## Max 0.6634437 0.7337697 0.41747344
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7658423 0.7781017 0.6848162 0.6788424 0.6610580 0.6756440 0.6876452
## Min 0.7562769 0.7656373 0.6674727 0.6507736 0.6342292 0.6469130 0.6496756
## 0.25 Qt. 0.7625246 0.7749432 0.6773948 0.6744442 0.6558523 0.6692546 0.6753458
## Median 0.7664699 0.7787000 0.6853263 0.6800789 0.6619458 0.6770086 0.6874433
## 0.75 Qt. 0.7683688 0.7818902 0.6914573 0.6833197 0.6681648 0.6834903 0.7032024
## Max 0.7745815 0.7871087 0.7082630 0.6956888 0.6785181 0.6986667 0.7283556
## 96 108 120
## Mean 0.6135738 0.6282538 0.25947385
## Min 0.5652901 0.5318523 0.08551219
## 0.25 Qt. 0.5943249 0.5917744 0.20725953
## Median 0.6159158 0.6252510 0.26660433
## 0.75 Qt. 0.6282015 0.6615902 0.31016200
## Max 0.6634437 0.7337697 0.41747344
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7658423 0.7781017 0.6848162 0.6788424 0.6610580 0.6756440 0.6876452
## Min 0.7562769 0.7656373 0.6674727 0.6507736 0.6342292 0.6469130 0.6496756
## 0.25 Qt. 0.7625246 0.7749432 0.6773948 0.6744442 0.6558523 0.6692546 0.6753458
## Median 0.7664699 0.7787000 0.6853263 0.6800789 0.6619458 0.6770086 0.6874433
## 0.75 Qt. 0.7683688 0.7818902 0.6914573 0.6833197 0.6681648 0.6834903 0.7032024
## Max 0.7745815 0.7871087 0.7082630 0.6956888 0.6785181 0.6986667 0.7283556
## 96 108 120
## Mean 0.6135738 0.6282538 0.25947385
## Min 0.5652901 0.5318523 0.08551219
## 0.25 Qt. 0.5943249 0.5917744 0.20725953
## Median 0.6159158 0.6252510 0.26660433
## 0.75 Qt. 0.6282015 0.6615902 0.31016200
## Max 0.6634437 0.7337697 0.41747344
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7658423 0.7781017 0.6848162 0.6788424 0.6610580 0.6756440 0.6876452
## Min 0.7562769 0.7656373 0.6674727 0.6507736 0.6342292 0.6469130 0.6496756
## 0.25 Qt. 0.7625246 0.7749432 0.6773948 0.6744442 0.6558523 0.6692546 0.6753458
## Median 0.7664699 0.7787000 0.6853263 0.6800789 0.6619458 0.6770086 0.6874433
## 0.75 Qt. 0.7683688 0.7818902 0.6914573 0.6833197 0.6681648 0.6834903 0.7032024
## Max 0.7745815 0.7871087 0.7082630 0.6956888 0.6785181 0.6986667 0.7283556
## 96 108 120
## Mean 0.6135738 0.6282538 0.25947385
## Min 0.5652901 0.5318523 0.08551219
## 0.25 Qt. 0.5943249 0.5917744 0.20725953
## Median 0.6159158 0.6252510 0.26660433
## 0.75 Qt. 0.6282015 0.6615902 0.31016200
## Max 0.6634437 0.7337697 0.41747344
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7658423 0.7781017 0.6848162 0.6788424 0.6610580 0.6756440 0.6876452
## Min 0.7562769 0.7656373 0.6674727 0.6507736 0.6342292 0.6469130 0.6496756
## 0.25 Qt. 0.7625246 0.7749432 0.6773948 0.6744442 0.6558523 0.6692546 0.6753458
## Median 0.7664699 0.7787000 0.6853263 0.6800789 0.6619458 0.6770086 0.6874433
## 0.75 Qt. 0.7683688 0.7818902 0.6914573 0.6833197 0.6681648 0.6834903 0.7032024
## Max 0.7745815 0.7871087 0.7082630 0.6956888 0.6785181 0.6986667 0.7283556
## 96 108 120
## Mean 0.6135738 0.6282538 0.25947385
## Min 0.5652901 0.5318523 0.08551219
## 0.25 Qt. 0.5943249 0.5917744 0.20725953
## Median 0.6159158 0.6252510 0.26660433
## 0.75 Qt. 0.6282015 0.6615902 0.31016200
## Max 0.6634437 0.7337697 0.41747344
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7658423 0.7781017 0.6848162 0.6788424 0.6610580 0.6756440 0.6876452
## Min 0.7562769 0.7656373 0.6674727 0.6507736 0.6342292 0.6469130 0.6496756
## 0.25 Qt. 0.7625246 0.7749432 0.6773948 0.6744442 0.6558523 0.6692546 0.6753458
## Median 0.7664699 0.7787000 0.6853263 0.6800789 0.6619458 0.6770086 0.6874433
## 0.75 Qt. 0.7683688 0.7818902 0.6914573 0.6833197 0.6681648 0.6834903 0.7032024
## Max 0.7745815 0.7871087 0.7082630 0.6956888 0.6785181 0.6986667 0.7283556
## 96 108 120
## Mean 0.6135738 0.6282538 0.25947385
## Min 0.5652901 0.5318523 0.08551219
## 0.25 Qt. 0.5943249 0.5917744 0.20725953
## Median 0.6159158 0.6252510 0.26660433
## 0.75 Qt. 0.6282015 0.6615902 0.31016200
## Max 0.6634437 0.7337697 0.41747344
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7658423 0.7781017 0.6848162 0.6788424 0.6610580 0.6756440 0.6876452
## Min 0.7562769 0.7656373 0.6674727 0.6507736 0.6342292 0.6469130 0.6496756
## 0.25 Qt. 0.7625246 0.7749432 0.6773948 0.6744442 0.6558523 0.6692546 0.6753458
## Median 0.7664699 0.7787000 0.6853263 0.6800789 0.6619458 0.6770086 0.6874433
## 0.75 Qt. 0.7683688 0.7818902 0.6914573 0.6833197 0.6681648 0.6834903 0.7032024
## Max 0.7745815 0.7871087 0.7082630 0.6956888 0.6785181 0.6986667 0.7283556
## 96 108 120
## Mean 0.6135738 0.6282538 0.25947385
## Min 0.5652901 0.5318523 0.08551219
## 0.25 Qt. 0.5943249 0.5917744 0.20725953
## Median 0.6159158 0.6252510 0.26660433
## 0.75 Qt. 0.6282015 0.6615902 0.31016200
## Max 0.6634437 0.7337697 0.41747344
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7658423 0.7781017 0.6848162 0.6788424 0.6610580 0.6756440 0.6876452
## Min 0.7562769 0.7656373 0.6674727 0.6507736 0.6342292 0.6469130 0.6496756
## 0.25 Qt. 0.7625246 0.7749432 0.6773948 0.6744442 0.6558523 0.6692546 0.6753458
## Median 0.7664699 0.7787000 0.6853263 0.6800789 0.6619458 0.6770086 0.6874433
## 0.75 Qt. 0.7683688 0.7818902 0.6914573 0.6833197 0.6681648 0.6834903 0.7032024
## Max 0.7745815 0.7871087 0.7082630 0.6956888 0.6785181 0.6986667 0.7283556
## 96 108 120
## Mean 0.6135738 0.6282538 0.25947385
## Min 0.5652901 0.5318523 0.08551219
## 0.25 Qt. 0.5943249 0.5917744 0.20725953
## Median 0.6159158 0.6252510 0.26660433
## 0.75 Qt. 0.6282015 0.6615902 0.31016200
## Max 0.6634437 0.7337697 0.41747344
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7658423 0.7781017 0.6848162 0.6788424 0.6610580 0.6756440 0.6876452
## Min 0.7562769 0.7656373 0.6674727 0.6507736 0.6342292 0.6469130 0.6496756
## 0.25 Qt. 0.7625246 0.7749432 0.6773948 0.6744442 0.6558523 0.6692546 0.6753458
## Median 0.7664699 0.7787000 0.6853263 0.6800789 0.6619458 0.6770086 0.6874433
## 0.75 Qt. 0.7683688 0.7818902 0.6914573 0.6833197 0.6681648 0.6834903 0.7032024
## Max 0.7745815 0.7871087 0.7082630 0.6956888 0.6785181 0.6986667 0.7283556
## 96 108 120
## Mean 0.6135738 0.6282538 0.25947385
## Min 0.5652901 0.5318523 0.08551219
## 0.25 Qt. 0.5943249 0.5917744 0.20725953
## Median 0.6159158 0.6252510 0.26660433
## 0.75 Qt. 0.6282015 0.6615902 0.31016200
## Max 0.6634437 0.7337697 0.41747344
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7658423 0.7781017 0.6848162 0.6788424 0.6610580 0.6756440 0.6876452
## Min 0.7562769 0.7656373 0.6674727 0.6507736 0.6342292 0.6469130 0.6496756
## 0.25 Qt. 0.7625246 0.7749432 0.6773948 0.6744442 0.6558523 0.6692546 0.6753458
## Median 0.7664699 0.7787000 0.6853263 0.6800789 0.6619458 0.6770086 0.6874433
## 0.75 Qt. 0.7683688 0.7818902 0.6914573 0.6833197 0.6681648 0.6834903 0.7032024
## Max 0.7745815 0.7871087 0.7082630 0.6956888 0.6785181 0.6986667 0.7283556
## 96 108 120
## Mean 0.6135738 0.6282538 0.25947385
## Min 0.5652901 0.5318523 0.08551219
## 0.25 Qt. 0.5943249 0.5917744 0.20725953
## Median 0.6159158 0.6252510 0.26660433
## 0.75 Qt. 0.6282015 0.6615902 0.31016200
## Max 0.6634437 0.7337697 0.41747344SCAD_Train_IV_Summary## Model Validation MedianTDAUROC Time
## 1 SCAD Cross-Validation 0.7664699 12
## 2 SCAD Cross-Validation 0.7787000 24
## 3 SCAD Cross-Validation 0.6853263 36
## 4 SCAD Cross-Validation 0.6800789 48
## 5 SCAD Cross-Validation 0.6619458 60
## 6 SCAD Cross-Validation 0.6770086 72
## 7 SCAD Cross-Validation 0.6874433 84
## 8 SCAD Cross-Validation 0.6159158 96
## 9 SCAD Cross-Validation 0.6252510 108
## 10 SCAD Cross-Validation 0.2666043 120plot(SCAD_Train_CoxPH_Model_InternalValidation,
ylim = c(0, 1))## 12 24 36 48 60 72 84
## Mean 0.7658423 0.7781017 0.6848162 0.6788424 0.6610580 0.6756440 0.6876452
## Min 0.7562769 0.7656373 0.6674727 0.6507736 0.6342292 0.6469130 0.6496756
## 0.25 Qt. 0.7625246 0.7749432 0.6773948 0.6744442 0.6558523 0.6692546 0.6753458
## Median 0.7664699 0.7787000 0.6853263 0.6800789 0.6619458 0.6770086 0.6874433
## 0.75 Qt. 0.7683688 0.7818902 0.6914573 0.6833197 0.6681648 0.6834903 0.7032024
## Max 0.7745815 0.7871087 0.7082630 0.6956888 0.6785181 0.6986667 0.7283556
## 96 108 120
## Mean 0.6135738 0.6282538 0.25947385
## Min 0.5652901 0.5318523 0.08551219
## 0.25 Qt. 0.5943249 0.5917744 0.20725953
## Median 0.6159158 0.6252510 0.26660433
## 0.75 Qt. 0.6282015 0.6615902 0.31016200
## Max 0.6634437 0.7337697 0.41747344
##################################
# Conducting an external validation
# of the SCAD model
# using the test data
##################################
SCAD_Test_Predictors <- as.matrix(
PMA_PreModelling_SCAD_Test[,!names(PMA_PreModelling_SCAD_Test)
%in% c("ttodead","died")])
SCAD_Test_Time <- PMA_PreModelling_SCAD_Test$ttodead
SCAD_Test_Status <- PMA_PreModelling_SCAD_Test$died
SCAD_Test_Response <- survival::Surv(SCAD_Test_Time, SCAD_Test_Status)
SCAD_Train_CoxPH_Model_ExternalValidation <- validate_external(SCAD_Train_CoxPH_Fit,
SCAD_Train_Predictors,
SCAD_Train_Time,
SCAD_Train_Status,
SCAD_Test_Predictors,
SCAD_Test_Time,
SCAD_Test_Status,
tauc.type = "UNO",
tauc.time = seq(12, 120, 12))
print(SCAD_Train_CoxPH_Model_ExternalValidation)## High-Dimensional Cox Model External Validation Object
## Model type: scad
## Time-dependent AUC type: UNO
## Evaluation time points for tAUC: 12 24 36 48 60 72 84 96 108 120summary(SCAD_Train_CoxPH_Model_ExternalValidation)## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8029733 0.7451414 0.6442855 0.600773 0.648913 0.604765 0.5663698
## 96 108 120
## AUC 0.6516541 0.4566631 0.275SCAD_Train_EV_Summary <- data.frame(matrix(ncol=4, nrow=10))
names(SCAD_Train_EV_Summary) <- c("Model","Validation","MedianTDAUROC","Time")
for (i in 1:10){
SCAD_Train_EV_Summary[i,1] <- "SCAD"
SCAD_Train_EV_Summary[i,2] <- "Test"
SCAD_Train_EV_Summary[i,3] <- summary(SCAD_Train_CoxPH_Model_ExternalValidation)[[1,i]]
SCAD_Train_EV_Summary[i,4] <- i*12
}## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8029733 0.7451414 0.6442855 0.600773 0.648913 0.604765 0.5663698
## 96 108 120
## AUC 0.6516541 0.4566631 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8029733 0.7451414 0.6442855 0.600773 0.648913 0.604765 0.5663698
## 96 108 120
## AUC 0.6516541 0.4566631 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8029733 0.7451414 0.6442855 0.600773 0.648913 0.604765 0.5663698
## 96 108 120
## AUC 0.6516541 0.4566631 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8029733 0.7451414 0.6442855 0.600773 0.648913 0.604765 0.5663698
## 96 108 120
## AUC 0.6516541 0.4566631 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8029733 0.7451414 0.6442855 0.600773 0.648913 0.604765 0.5663698
## 96 108 120
## AUC 0.6516541 0.4566631 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8029733 0.7451414 0.6442855 0.600773 0.648913 0.604765 0.5663698
## 96 108 120
## AUC 0.6516541 0.4566631 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8029733 0.7451414 0.6442855 0.600773 0.648913 0.604765 0.5663698
## 96 108 120
## AUC 0.6516541 0.4566631 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8029733 0.7451414 0.6442855 0.600773 0.648913 0.604765 0.5663698
## 96 108 120
## AUC 0.6516541 0.4566631 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8029733 0.7451414 0.6442855 0.600773 0.648913 0.604765 0.5663698
## 96 108 120
## AUC 0.6516541 0.4566631 0.275
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.8029733 0.7451414 0.6442855 0.600773 0.648913 0.604765 0.5663698
## 96 108 120
## AUC 0.6516541 0.4566631 0.275SCAD_Train_EV_Summary## Model Validation MedianTDAUROC Time
## 1 SCAD Test 0.8029733 12
## 2 SCAD Test 0.7451414 24
## 3 SCAD Test 0.6442855 36
## 4 SCAD Test 0.6007730 48
## 5 SCAD Test 0.6489130 60
## 6 SCAD Test 0.6047650 72
## 7 SCAD Test 0.5663698 84
## 8 SCAD Test 0.6516541 96
## 9 SCAD Test 0.4566631 108
## 10 SCAD Test 0.2750000 120plot(SCAD_Train_CoxPH_Model_ExternalValidation,
ylim = c(0, 1))## 12 24 36 48 60 72 84
## AUC 0.8029733 0.7451414 0.6442855 0.600773 0.648913 0.604765 0.5663698
## 96 108 120
## AUC 0.6516541 0.4566631 0.275
##################################
# Conducting an internal calibration
# of the SCAD model
# using a 50-cycle bootstrap
# for Year 5 (Time = 60)
##################################
SCAD_Train_CoxPH_Model_InternalCalibration <- calibrate(SCAD_Train_Predictors,
SCAD_Train_Time,
SCAD_Train_Status,
model.type = "scad",
alpha = 1,
lambda = SCAD_Train_CoxPH_Fit$lambda,
gamma = SCAD_Train_CoxPH_Fit$gamma,
method = "bootstrap",
boot.times = 50,
pred.at = 60,
ngroup = 3,
seed = 88888888)## Start bootstrap sample 1
## Start bootstrap sample 2
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## Start bootstrap sample 50print(SCAD_Train_CoxPH_Model_InternalCalibration)## High-Dimensional Cox Model Calibration Object
## Random seed: 88888888
## Calibration method: bootstrap
## Bootstrap samples: 50
## Model type: scad
## ncvreg model gamma: 5
## ncvreg model alpha: 1
## ncvreg model lambda: 0.01114746
## Calibration time point: 60
## Number of groups formed for calibration: 3summary(SCAD_Train_CoxPH_Model_InternalCalibration)## Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.5665011 0.5173624 0.4763953 0.5618523
## 2 0.7573817 0.7665736 0.7306859 0.8042239
## 3 0.8814423 0.8940749 0.8677195 0.9212308plot(SCAD_Train_CoxPH_Model_InternalCalibration)
##################################
# Estimating survival curves
# of the SCAD model
# for the risk groups
# using the train data
##################################
SCAD_Train_CoxPH_Model_KMPlot <- kmplot( SCAD_Train_CoxPH_Model_InternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
##################################
# Conducting an internal calibration
# of the SCAD model
# using a 50-cycle bootstrap
# for Year 10 (Time = 120)
##################################
SCAD_Train_CoxPH_Model_InternalCalibration <- calibrate(SCAD_Train_Predictors,
SCAD_Train_Time,
SCAD_Train_Status,
model.type = "scad",
alpha = 1,
lambda = SCAD_Train_CoxPH_Fit$lambda,
gamma = SCAD_Train_CoxPH_Fit$gamma,
method = "bootstrap",
boot.times = 50,
pred.at = 120,
ngroup = 3,
seed = 88888888)## Start bootstrap sample 1
## Start bootstrap sample 2
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## Start bootstrap sample 50print(SCAD_Train_CoxPH_Model_InternalCalibration)## High-Dimensional Cox Model Calibration Object
## Random seed: 88888888
## Calibration method: bootstrap
## Bootstrap samples: 50
## Model type: scad
## ncvreg model gamma: 5
## ncvreg model alpha: 1
## ncvreg model lambda: 0.01114746
## Calibration time point: 120
## Number of groups formed for calibration: 3summary(SCAD_Train_CoxPH_Model_InternalCalibration)## Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.3530235 0.3573237 0.3128191 0.4081599
## 2 0.5991648 0.5486103 0.4925124 0.6110978
## 3 0.7921034 0.7998532 0.7512528 0.8515977plot(SCAD_Train_CoxPH_Model_InternalCalibration)
##################################
# Estimating survival curves
# of the SCAD model
# for the risk groups
# using the train data
##################################
SCAD_Train_CoxPH_Model_KMPlot <- kmplot( SCAD_Train_CoxPH_Model_InternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
##################################
# Conducting an external calibration
# of the SCAD model
# using the test data
# for Year 5 (Time = 60)
##################################
SCAD_Train_CoxPH_Model_ExternalCalibration <- calibrate_external(SCAD_Train_CoxPH_Fit,
SCAD_Train_Predictors,
SCAD_Train_Time,
SCAD_Train_Status,
SCAD_Test_Predictors,
SCAD_Test_Time,
SCAD_Test_Status,
pred.at = 60,
ngroup = 3)
print(SCAD_Train_CoxPH_Model_ExternalCalibration)## High-Dimensional Cox Model External Calibration Object
## Model type: scad
## Calibration time point: 60
## Number of groups formed for calibration: 3summary(SCAD_Train_CoxPH_Model_ExternalCalibration)## External Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.5741353 0.5616451 0.4827729 0.6534030
## 2 0.7609469 0.7129824 0.6401460 0.7941063
## 3 0.8872246 0.8483783 0.7888815 0.9123623plot(SCAD_Train_CoxPH_Model_ExternalCalibration)
##################################
# Estimating survival curves
# of the SCAD model
# for the risk groups
# using the test data
##################################
SCAD_Test_CoxPH_Model_KMPlot <- kmplot( SCAD_Train_CoxPH_Model_ExternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
##################################
# Conducting an external calibration
# of the SCAD model
# using the test data
# for Year 10 (Time = 120)
##################################
SCAD_Train_CoxPH_Model_ExternalCalibration <- calibrate_external(SCAD_Train_CoxPH_Fit,
SCAD_Train_Predictors,
SCAD_Train_Time,
SCAD_Train_Status,
SCAD_Test_Predictors,
SCAD_Test_Time,
SCAD_Test_Status,
pred.at = 120,
ngroup = 3)
print(SCAD_Train_CoxPH_Model_ExternalCalibration)## High-Dimensional Cox Model External Calibration Object
## Model type: scad
## Calibration time point: 120
## Number of groups formed for calibration: 3summary(SCAD_Train_CoxPH_Model_ExternalCalibration)## External Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.3544595 0.4670681 0.3846865 0.5670919
## 2 0.6001166 0.5920318 0.5038132 0.6956977
## 3 0.7995910 0.7351355 0.6252714 0.8643033plot(SCAD_Train_CoxPH_Model_ExternalCalibration)
##################################
# Estimating survival curves
# of the SCAD model
# for the risk groups
# using the test data
##################################
SCAD_Test_CoxPH_Model_KMPlot <- kmplot( SCAD_Train_CoxPH_Model_ExternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
1.5.5 Fused Least Absolute Shrinkage and Selection Operator (FLASSO)
Fused Least Absolute Shrinkage and Selection Operator is a regularization technique which extends the LASSO method by incorporating the concept of fused penalties, which encourages grouping or clustering of related variables. The method is aimed at identifying a subset of predictors that have a significant impact on survival outcomes while considering their interdependencies. The penalty term in the fused LASSO method consists of two components - the LASSO penalty and the fused penalty. The LASSO penalty promotes sparsity in the coefficients, forcing some coefficients to be zero. The fused penalty promotes grouping or clustering of related variables by penalizing the differences between neighboring coefficients. This enables the neighboring variables to be either all included or all excluded from the model. The tuning parameters lambda controls the amount of penalty applied. The method formulates an optimization problem that minimizes the negative log-partial likelihood of the Cox proportional hazards model subject to the penalty terms. The optimization problem is solved using coordinate descent, by iteratively updating regression coefficients, taking into account both the LASSO and fused penalties.
[A] The multivariate Cox proportional hazards regression model from the survival package was implemented with the FLASSO penalty function applied using the hdnom and penalized packages.
[B] The method contains 3 hyperparameters:
[B.1] nfolds = fold numbers of cross-validation fixed at a value = 5
[B.2] lambda1 = LASSO penalty made to vary across a range of values equal to 0.001 to 5.000
[B.3] lambda2 = fused LASSO penalty made to vary across a range of values equal to 0.001 to 0.500
[C] The method selected 3 out of the 25 predictors for the final Cox Proportional Hazards model. No information about the selected predictors are available.
[D] Optimal model parameters were determined as:
[D.1] Lambda1 = 5.000
[D.2] Lambda2 = 0.001
[E] Internally validated Time-Dependent AUROC metrics using bootstrap resampling of the train data showed good performance overall except for the last few time periods and are summarized as follows:
[E.1] 12 months = 0.72181
[E.2] 24 months = 0.71095
[E.3] 36 months = 0.62796
[E.4] 48 months = 0.65079
[E.5] 60 months = 0.63001
[E.6] 72 months = 0.65456
[E.7] 84 months = 0.71094
[E.8] 96 months = 0.57652
[E.9] 108 months = 0.76628
[E.10] 120 months = 0.79072
[F] Externally validated Time-Dependent AUROC metrics using the test data showed good performance overall except for the last few time periods and are summarized as follows:
[F.1] 12 months = 0.73644
[F.2] 24 months = 0.70272
[F.3] 36 months = 0.58654
[F.4] 48 months = 0.61131
[F.5] 60 months = 0.59972
[F.6] 72 months = 0.63533
[F.7] 84 months = 0.68976
[F.8] 96 months = 0.86677
[F.9] 108 months = 0.46055
[F.10] 120 months = 0.62500
[G] Model predictions at time = 60 months were reasonably calibrated for both the train and test data. Kaplan-Meier survival curves for the three risk groups from the calibration data were differentially profiled with p-value < 0.05.
[H] Model predictions at time = 120 months were reasonably calibrated for both the train and test data. Kaplan-Meier survival curves for the three risk groups from the calibration data were differentially profiled with p-value < 0.05.
Code Chunk | Output
##################################
# Loading the preprocessed data
##################################
PMA_PreModelling_FLASSO <- peakVO2.Source[,!names(peakVO2.Source) %in% rownames(UF_CoxPHModelSummary_NonSig)]
##################################
# Setting the appropriate variable types
##################################
PMA_PreModelling_FLASSO$died <- as.factor(PMA_PreModelling_FLASSO$died)
##################################
# Creating a local object
# for the train and test sets
##################################
set.seed(88888888)
TrainIndex <- createDataPartition(PMA_PreModelling_FLASSO$died,
p = 0.8,
list = FALSE,
times = 1)
PMA_PreModelling_FLASSO_Train <- PMA_PreModelling_FLASSO[ TrainIndex,]
PMA_PreModelling_FLASSO_Test <- PMA_PreModelling_FLASSO[-TrainIndex,]
PMA_PreModelling_FLASSO_Train$died <- as.numeric(PMA_PreModelling_FLASSO_Train$died)
PMA_PreModelling_FLASSO_Test$died <- as.numeric(PMA_PreModelling_FLASSO_Test$died)
##################################
# Formulating the model components
# for the train data
##################################
FLASSO_Train_Predictors <- as.matrix(
PMA_PreModelling_FLASSO_Train[,!names(PMA_PreModelling_FLASSO_Train)
%in% c("ttodead","died")])
FLASSO_Train_Time <- PMA_PreModelling_FLASSO_Train$ttodead
FLASSO_Train_Status <- PMA_PreModelling_FLASSO_Train$died
FLASSO_Train_Response <- survival::Surv(FLASSO_Train_Time, FLASSO_Train_Status)
##################################
# Fitting a FLASSO model
##################################
FLASSO_Train_CoxPH_Fit <- fit_flasso(FLASSO_Train_Predictors,
FLASSO_Train_Response,
nfolds = 5,
lambda1 = c(0.001, 0.05, 0.5, 1, 5),
lambda2 = c(0.001, 0.01, 0.5),
seed = 88888888,
trace = FALSE)## 123451234512345123451234512345123451234512345123451234512345123451234512345##################################
# Extracting the model objects and
# optimal hyperparameters
# from the FLASSO model
##################################
(FLASSO_Train_CoxPH_Model <- FLASSO_Train_CoxPH_Fit$model)## Penalized cox regression object
## 25 regression coefficients of which 3 are non-zero
##
## Loglikelihood = -4589.738
## L1 penalty = 0.01464715 at lambda1 = 5
## L2 penalty = 5.550388e-06 at lambda2 = 0.001(FLASSO_Train_CoxPH_Lambda1 <- FLASSO_Train_CoxPH_Fit$lambda1)## lambda1
## 5(FLASSO_Train_CoxPH_Lambda2 <- FLASSO_Train_CoxPH_Fit$lambda2)## lambda2
## 0.001##################################
# Conducting an internal validation
# of the FLASSO model
# using a 50-cycle bootstrap
##################################
FLASSO_Train_CoxPH_Model_InternalValidation <- validate(FLASSO_Train_Predictors,
FLASSO_Train_Time,
FLASSO_Train_Status,
model.type = "flasso",
lambda1 = FLASSO_Train_CoxPH_Fit$lambda1,
lambda2 = FLASSO_Train_CoxPH_Fit$lambda2,
method = "bootstrap",
boot.times = 50,
tauc.type = "UNO",
tauc.time = seq(12, 120, 12),
seed = 88888888)## Start bootstrap sample 1
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## Start bootstrap sample 50print(FLASSO_Train_CoxPH_Model_InternalValidation)## High-Dimensional Cox Model Validation Object
## Random seed: 88888888
## Validation method: bootstrap
## Bootstrap samples: 50
## Model type: flasso
## Fused lasso model lambda1: 5
## Fused lasso model lambda2: 0.001
## Time-dependent AUC type: UNO
## Evaluation time points for tAUC: 12 24 36 48 60 72 84 96 108 120summary(FLASSO_Train_CoxPH_Model_InternalValidation)## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7252512 0.7150066 0.6300159 0.6521802 0.6302462 0.6551807 0.7124809
## Min 0.7181147 0.7065274 0.6072147 0.6447300 0.6128590 0.6412017 0.6889170
## 0.25 Qt. 0.7216526 0.7099664 0.6208374 0.6483071 0.6263984 0.6534210 0.7061755
## Median 0.7218108 0.7109526 0.6279633 0.6507986 0.6300183 0.6545639 0.7109444
## 0.75 Qt. 0.7221247 0.7128365 0.6368961 0.6536377 0.6333300 0.6569550 0.7190238
## Max 0.7537228 0.7505965 0.6808277 0.6796688 0.6557119 0.6677206 0.7405627
## 96 108 120
## Mean 0.5745624 0.7586374 0.7797808
## Min 0.5248061 0.6524812 0.5966152
## 0.25 Qt. 0.5686012 0.7445487 0.7854935
## Median 0.5765256 0.7662841 0.7907220
## 0.75 Qt. 0.5816979 0.7818164 0.7928887
## Max 0.5963204 0.7986991 0.8276318FLASSO_Train_IV_Summary <- data.frame(matrix(ncol=4, nrow=10))
names(FLASSO_Train_IV_Summary) <- c("Model","Validation","MedianTDAUROC","Time")
for (i in 1:10){
FLASSO_Train_IV_Summary[i,1] <- "FLASSO"
FLASSO_Train_IV_Summary[i,2] <- "Cross-Validation"
FLASSO_Train_IV_Summary[i,3] <- summary(FLASSO_Train_CoxPH_Model_InternalValidation)[[4,i]]
FLASSO_Train_IV_Summary[i,4] <- i*12
}## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7252512 0.7150066 0.6300159 0.6521802 0.6302462 0.6551807 0.7124809
## Min 0.7181147 0.7065274 0.6072147 0.6447300 0.6128590 0.6412017 0.6889170
## 0.25 Qt. 0.7216526 0.7099664 0.6208374 0.6483071 0.6263984 0.6534210 0.7061755
## Median 0.7218108 0.7109526 0.6279633 0.6507986 0.6300183 0.6545639 0.7109444
## 0.75 Qt. 0.7221247 0.7128365 0.6368961 0.6536377 0.6333300 0.6569550 0.7190238
## Max 0.7537228 0.7505965 0.6808277 0.6796688 0.6557119 0.6677206 0.7405627
## 96 108 120
## Mean 0.5745624 0.7586374 0.7797808
## Min 0.5248061 0.6524812 0.5966152
## 0.25 Qt. 0.5686012 0.7445487 0.7854935
## Median 0.5765256 0.7662841 0.7907220
## 0.75 Qt. 0.5816979 0.7818164 0.7928887
## Max 0.5963204 0.7986991 0.8276318
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7252512 0.7150066 0.6300159 0.6521802 0.6302462 0.6551807 0.7124809
## Min 0.7181147 0.7065274 0.6072147 0.6447300 0.6128590 0.6412017 0.6889170
## 0.25 Qt. 0.7216526 0.7099664 0.6208374 0.6483071 0.6263984 0.6534210 0.7061755
## Median 0.7218108 0.7109526 0.6279633 0.6507986 0.6300183 0.6545639 0.7109444
## 0.75 Qt. 0.7221247 0.7128365 0.6368961 0.6536377 0.6333300 0.6569550 0.7190238
## Max 0.7537228 0.7505965 0.6808277 0.6796688 0.6557119 0.6677206 0.7405627
## 96 108 120
## Mean 0.5745624 0.7586374 0.7797808
## Min 0.5248061 0.6524812 0.5966152
## 0.25 Qt. 0.5686012 0.7445487 0.7854935
## Median 0.5765256 0.7662841 0.7907220
## 0.75 Qt. 0.5816979 0.7818164 0.7928887
## Max 0.5963204 0.7986991 0.8276318
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7252512 0.7150066 0.6300159 0.6521802 0.6302462 0.6551807 0.7124809
## Min 0.7181147 0.7065274 0.6072147 0.6447300 0.6128590 0.6412017 0.6889170
## 0.25 Qt. 0.7216526 0.7099664 0.6208374 0.6483071 0.6263984 0.6534210 0.7061755
## Median 0.7218108 0.7109526 0.6279633 0.6507986 0.6300183 0.6545639 0.7109444
## 0.75 Qt. 0.7221247 0.7128365 0.6368961 0.6536377 0.6333300 0.6569550 0.7190238
## Max 0.7537228 0.7505965 0.6808277 0.6796688 0.6557119 0.6677206 0.7405627
## 96 108 120
## Mean 0.5745624 0.7586374 0.7797808
## Min 0.5248061 0.6524812 0.5966152
## 0.25 Qt. 0.5686012 0.7445487 0.7854935
## Median 0.5765256 0.7662841 0.7907220
## 0.75 Qt. 0.5816979 0.7818164 0.7928887
## Max 0.5963204 0.7986991 0.8276318
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7252512 0.7150066 0.6300159 0.6521802 0.6302462 0.6551807 0.7124809
## Min 0.7181147 0.7065274 0.6072147 0.6447300 0.6128590 0.6412017 0.6889170
## 0.25 Qt. 0.7216526 0.7099664 0.6208374 0.6483071 0.6263984 0.6534210 0.7061755
## Median 0.7218108 0.7109526 0.6279633 0.6507986 0.6300183 0.6545639 0.7109444
## 0.75 Qt. 0.7221247 0.7128365 0.6368961 0.6536377 0.6333300 0.6569550 0.7190238
## Max 0.7537228 0.7505965 0.6808277 0.6796688 0.6557119 0.6677206 0.7405627
## 96 108 120
## Mean 0.5745624 0.7586374 0.7797808
## Min 0.5248061 0.6524812 0.5966152
## 0.25 Qt. 0.5686012 0.7445487 0.7854935
## Median 0.5765256 0.7662841 0.7907220
## 0.75 Qt. 0.5816979 0.7818164 0.7928887
## Max 0.5963204 0.7986991 0.8276318
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7252512 0.7150066 0.6300159 0.6521802 0.6302462 0.6551807 0.7124809
## Min 0.7181147 0.7065274 0.6072147 0.6447300 0.6128590 0.6412017 0.6889170
## 0.25 Qt. 0.7216526 0.7099664 0.6208374 0.6483071 0.6263984 0.6534210 0.7061755
## Median 0.7218108 0.7109526 0.6279633 0.6507986 0.6300183 0.6545639 0.7109444
## 0.75 Qt. 0.7221247 0.7128365 0.6368961 0.6536377 0.6333300 0.6569550 0.7190238
## Max 0.7537228 0.7505965 0.6808277 0.6796688 0.6557119 0.6677206 0.7405627
## 96 108 120
## Mean 0.5745624 0.7586374 0.7797808
## Min 0.5248061 0.6524812 0.5966152
## 0.25 Qt. 0.5686012 0.7445487 0.7854935
## Median 0.5765256 0.7662841 0.7907220
## 0.75 Qt. 0.5816979 0.7818164 0.7928887
## Max 0.5963204 0.7986991 0.8276318
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7252512 0.7150066 0.6300159 0.6521802 0.6302462 0.6551807 0.7124809
## Min 0.7181147 0.7065274 0.6072147 0.6447300 0.6128590 0.6412017 0.6889170
## 0.25 Qt. 0.7216526 0.7099664 0.6208374 0.6483071 0.6263984 0.6534210 0.7061755
## Median 0.7218108 0.7109526 0.6279633 0.6507986 0.6300183 0.6545639 0.7109444
## 0.75 Qt. 0.7221247 0.7128365 0.6368961 0.6536377 0.6333300 0.6569550 0.7190238
## Max 0.7537228 0.7505965 0.6808277 0.6796688 0.6557119 0.6677206 0.7405627
## 96 108 120
## Mean 0.5745624 0.7586374 0.7797808
## Min 0.5248061 0.6524812 0.5966152
## 0.25 Qt. 0.5686012 0.7445487 0.7854935
## Median 0.5765256 0.7662841 0.7907220
## 0.75 Qt. 0.5816979 0.7818164 0.7928887
## Max 0.5963204 0.7986991 0.8276318
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7252512 0.7150066 0.6300159 0.6521802 0.6302462 0.6551807 0.7124809
## Min 0.7181147 0.7065274 0.6072147 0.6447300 0.6128590 0.6412017 0.6889170
## 0.25 Qt. 0.7216526 0.7099664 0.6208374 0.6483071 0.6263984 0.6534210 0.7061755
## Median 0.7218108 0.7109526 0.6279633 0.6507986 0.6300183 0.6545639 0.7109444
## 0.75 Qt. 0.7221247 0.7128365 0.6368961 0.6536377 0.6333300 0.6569550 0.7190238
## Max 0.7537228 0.7505965 0.6808277 0.6796688 0.6557119 0.6677206 0.7405627
## 96 108 120
## Mean 0.5745624 0.7586374 0.7797808
## Min 0.5248061 0.6524812 0.5966152
## 0.25 Qt. 0.5686012 0.7445487 0.7854935
## Median 0.5765256 0.7662841 0.7907220
## 0.75 Qt. 0.5816979 0.7818164 0.7928887
## Max 0.5963204 0.7986991 0.8276318
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7252512 0.7150066 0.6300159 0.6521802 0.6302462 0.6551807 0.7124809
## Min 0.7181147 0.7065274 0.6072147 0.6447300 0.6128590 0.6412017 0.6889170
## 0.25 Qt. 0.7216526 0.7099664 0.6208374 0.6483071 0.6263984 0.6534210 0.7061755
## Median 0.7218108 0.7109526 0.6279633 0.6507986 0.6300183 0.6545639 0.7109444
## 0.75 Qt. 0.7221247 0.7128365 0.6368961 0.6536377 0.6333300 0.6569550 0.7190238
## Max 0.7537228 0.7505965 0.6808277 0.6796688 0.6557119 0.6677206 0.7405627
## 96 108 120
## Mean 0.5745624 0.7586374 0.7797808
## Min 0.5248061 0.6524812 0.5966152
## 0.25 Qt. 0.5686012 0.7445487 0.7854935
## Median 0.5765256 0.7662841 0.7907220
## 0.75 Qt. 0.5816979 0.7818164 0.7928887
## Max 0.5963204 0.7986991 0.8276318
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7252512 0.7150066 0.6300159 0.6521802 0.6302462 0.6551807 0.7124809
## Min 0.7181147 0.7065274 0.6072147 0.6447300 0.6128590 0.6412017 0.6889170
## 0.25 Qt. 0.7216526 0.7099664 0.6208374 0.6483071 0.6263984 0.6534210 0.7061755
## Median 0.7218108 0.7109526 0.6279633 0.6507986 0.6300183 0.6545639 0.7109444
## 0.75 Qt. 0.7221247 0.7128365 0.6368961 0.6536377 0.6333300 0.6569550 0.7190238
## Max 0.7537228 0.7505965 0.6808277 0.6796688 0.6557119 0.6677206 0.7405627
## 96 108 120
## Mean 0.5745624 0.7586374 0.7797808
## Min 0.5248061 0.6524812 0.5966152
## 0.25 Qt. 0.5686012 0.7445487 0.7854935
## Median 0.5765256 0.7662841 0.7907220
## 0.75 Qt. 0.5816979 0.7818164 0.7928887
## Max 0.5963204 0.7986991 0.8276318
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## Mean 0.7252512 0.7150066 0.6300159 0.6521802 0.6302462 0.6551807 0.7124809
## Min 0.7181147 0.7065274 0.6072147 0.6447300 0.6128590 0.6412017 0.6889170
## 0.25 Qt. 0.7216526 0.7099664 0.6208374 0.6483071 0.6263984 0.6534210 0.7061755
## Median 0.7218108 0.7109526 0.6279633 0.6507986 0.6300183 0.6545639 0.7109444
## 0.75 Qt. 0.7221247 0.7128365 0.6368961 0.6536377 0.6333300 0.6569550 0.7190238
## Max 0.7537228 0.7505965 0.6808277 0.6796688 0.6557119 0.6677206 0.7405627
## 96 108 120
## Mean 0.5745624 0.7586374 0.7797808
## Min 0.5248061 0.6524812 0.5966152
## 0.25 Qt. 0.5686012 0.7445487 0.7854935
## Median 0.5765256 0.7662841 0.7907220
## 0.75 Qt. 0.5816979 0.7818164 0.7928887
## Max 0.5963204 0.7986991 0.8276318FLASSO_Train_IV_Summary## Model Validation MedianTDAUROC Time
## 1 FLASSO Cross-Validation 0.7218108 12
## 2 FLASSO Cross-Validation 0.7109526 24
## 3 FLASSO Cross-Validation 0.6279633 36
## 4 FLASSO Cross-Validation 0.6507986 48
## 5 FLASSO Cross-Validation 0.6300183 60
## 6 FLASSO Cross-Validation 0.6545639 72
## 7 FLASSO Cross-Validation 0.7109444 84
## 8 FLASSO Cross-Validation 0.5765256 96
## 9 FLASSO Cross-Validation 0.7662841 108
## 10 FLASSO Cross-Validation 0.7907220 120plot(FLASSO_Train_CoxPH_Model_InternalValidation,
ylim = c(0, 1))## 12 24 36 48 60 72 84
## Mean 0.7252512 0.7150066 0.6300159 0.6521802 0.6302462 0.6551807 0.7124809
## Min 0.7181147 0.7065274 0.6072147 0.6447300 0.6128590 0.6412017 0.6889170
## 0.25 Qt. 0.7216526 0.7099664 0.6208374 0.6483071 0.6263984 0.6534210 0.7061755
## Median 0.7218108 0.7109526 0.6279633 0.6507986 0.6300183 0.6545639 0.7109444
## 0.75 Qt. 0.7221247 0.7128365 0.6368961 0.6536377 0.6333300 0.6569550 0.7190238
## Max 0.7537228 0.7505965 0.6808277 0.6796688 0.6557119 0.6677206 0.7405627
## 96 108 120
## Mean 0.5745624 0.7586374 0.7797808
## Min 0.5248061 0.6524812 0.5966152
## 0.25 Qt. 0.5686012 0.7445487 0.7854935
## Median 0.5765256 0.7662841 0.7907220
## 0.75 Qt. 0.5816979 0.7818164 0.7928887
## Max 0.5963204 0.7986991 0.8276318
##################################
# Conducting an external validation
# of the FLASSO model
# using the test data
##################################
FLASSO_Test_Predictors <- as.matrix(
PMA_PreModelling_FLASSO_Test[,!names(PMA_PreModelling_FLASSO_Test)
%in% c("ttodead","died")])
FLASSO_Test_Time <- PMA_PreModelling_FLASSO_Test$ttodead
FLASSO_Test_Status <- PMA_PreModelling_FLASSO_Test$died
FLASSO_Test_Response <- survival::Surv(FLASSO_Test_Time, FLASSO_Test_Status)
FLASSO_Train_CoxPH_Model_ExternalValidation <- validate_external(FLASSO_Train_CoxPH_Fit,
FLASSO_Train_Predictors,
FLASSO_Train_Time,
FLASSO_Train_Status,
FLASSO_Test_Predictors,
FLASSO_Test_Time,
FLASSO_Test_Status,
tauc.type = "UNO",
tauc.time = seq(12, 120, 12))
print(FLASSO_Train_CoxPH_Model_ExternalValidation)## High-Dimensional Cox Model External Validation Object
## Model type: flasso
## Time-dependent AUC type: UNO
## Evaluation time points for tAUC: 12 24 36 48 60 72 84 96 108 120summary(FLASSO_Train_CoxPH_Model_ExternalValidation)## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7364484 0.7027265 0.586547 0.6113137 0.5997273 0.6353383 0.689768
## 96 108 120
## AUC 0.8667712 0.4605588 0.625FLASSO_Train_EV_Summary <- data.frame(matrix(ncol=4, nrow=10))
names(FLASSO_Train_EV_Summary) <- c("Model","Validation","MedianTDAUROC","Time")
for (i in 1:10){
FLASSO_Train_EV_Summary[i,1] <- "FLASSO"
FLASSO_Train_EV_Summary[i,2] <- "Test"
FLASSO_Train_EV_Summary[i,3] <- summary(FLASSO_Train_CoxPH_Model_ExternalValidation)[[1,i]]
FLASSO_Train_EV_Summary[i,4] <- i*12
}## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7364484 0.7027265 0.586547 0.6113137 0.5997273 0.6353383 0.689768
## 96 108 120
## AUC 0.8667712 0.4605588 0.625
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7364484 0.7027265 0.586547 0.6113137 0.5997273 0.6353383 0.689768
## 96 108 120
## AUC 0.8667712 0.4605588 0.625
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7364484 0.7027265 0.586547 0.6113137 0.5997273 0.6353383 0.689768
## 96 108 120
## AUC 0.8667712 0.4605588 0.625
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7364484 0.7027265 0.586547 0.6113137 0.5997273 0.6353383 0.689768
## 96 108 120
## AUC 0.8667712 0.4605588 0.625
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7364484 0.7027265 0.586547 0.6113137 0.5997273 0.6353383 0.689768
## 96 108 120
## AUC 0.8667712 0.4605588 0.625
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7364484 0.7027265 0.586547 0.6113137 0.5997273 0.6353383 0.689768
## 96 108 120
## AUC 0.8667712 0.4605588 0.625
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7364484 0.7027265 0.586547 0.6113137 0.5997273 0.6353383 0.689768
## 96 108 120
## AUC 0.8667712 0.4605588 0.625
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7364484 0.7027265 0.586547 0.6113137 0.5997273 0.6353383 0.689768
## 96 108 120
## AUC 0.8667712 0.4605588 0.625
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7364484 0.7027265 0.586547 0.6113137 0.5997273 0.6353383 0.689768
## 96 108 120
## AUC 0.8667712 0.4605588 0.625
## Time-Dependent AUC Summary at Evaluation Time Points
## 12 24 36 48 60 72 84
## AUC 0.7364484 0.7027265 0.586547 0.6113137 0.5997273 0.6353383 0.689768
## 96 108 120
## AUC 0.8667712 0.4605588 0.625FLASSO_Train_EV_Summary## Model Validation MedianTDAUROC Time
## 1 FLASSO Test 0.7364484 12
## 2 FLASSO Test 0.7027265 24
## 3 FLASSO Test 0.5865470 36
## 4 FLASSO Test 0.6113137 48
## 5 FLASSO Test 0.5997273 60
## 6 FLASSO Test 0.6353383 72
## 7 FLASSO Test 0.6897680 84
## 8 FLASSO Test 0.8667712 96
## 9 FLASSO Test 0.4605588 108
## 10 FLASSO Test 0.6250000 120plot(FLASSO_Train_CoxPH_Model_ExternalValidation,
ylim = c(0, 1))## 12 24 36 48 60 72 84
## AUC 0.7364484 0.7027265 0.586547 0.6113137 0.5997273 0.6353383 0.689768
## 96 108 120
## AUC 0.8667712 0.4605588 0.625
##################################
# Conducting an internal calibration
# of the FLASSO model
# using a 50-cycle bootstrap
# for Year 5 (Time = 60)
##################################
FLASSO_Train_CoxPH_Model_InternalCalibration <- calibrate(FLASSO_Train_Predictors,
FLASSO_Train_Time,
FLASSO_Train_Status,
model.type = "flasso",
lambda1 = FLASSO_Train_CoxPH_Fit$lambda1,
lambda2 = FLASSO_Train_CoxPH_Fit$lambda2,
method = "bootstrap",
boot.times = 50,
pred.at = 60,
ngroup = 3,
seed = 88888888)## Start bootstrap sample 1
##
## Start bootstrap sample 2
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## Start bootstrap sample 50print(FLASSO_Train_CoxPH_Model_InternalCalibration)## High-Dimensional Cox Model Calibration Object
## Random seed: 88888888
## Calibration method: bootstrap
## Bootstrap samples: 50
## Model type: flasso
## Fused lasso model lambda1: 5
## Fused lasso model lambda2: 0.001
## Calibration time point: 60
## Number of groups formed for calibration: 3summary(FLASSO_Train_CoxPH_Model_InternalCalibration)## Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.5920323 0.5658604 0.5238011 0.6112970
## 2 0.7305319 0.7473826 0.7111420 0.7854702
## 3 0.8437515 0.8534281 0.8236513 0.8842813plot(FLASSO_Train_CoxPH_Model_InternalCalibration)
##################################
# Estimating survival curves
# of the FLASSO model
# for the risk groups
# using the train data
##################################
FLASSO_Train_CoxPH_Model_KMPlot <- kmplot( FLASSO_Train_CoxPH_Model_InternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
##################################
# Conducting an internal calibration
# of the FLASSO model
# using a 50-cycle bootstrap
# for Year 10 (Time = 120)
##################################
FLASSO_Train_CoxPH_Model_InternalCalibration <- calibrate(FLASSO_Train_Predictors,
FLASSO_Train_Time,
FLASSO_Train_Status,
model.type = "flasso",
lambda1 = FLASSO_Train_CoxPH_Fit$lambda1,
lambda2 = FLASSO_Train_CoxPH_Fit$lambda2,
method = "bootstrap",
boot.times = 50,
pred.at = 120,
ngroup = 3,
seed = 88888888)## Start bootstrap sample 1
##
## Start bootstrap sample 2
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## Start bootstrap sample 50print(FLASSO_Train_CoxPH_Model_InternalCalibration)## High-Dimensional Cox Model Calibration Object
## Random seed: 88888888
## Calibration method: bootstrap
## Bootstrap samples: 50
## Model type: flasso
## Fused lasso model lambda1: 5
## Fused lasso model lambda2: 0.001
## Calibration time point: 120
## Number of groups formed for calibration: 3summary(FLASSO_Train_CoxPH_Model_InternalCalibration)## Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.3763233 0.3832509 0.3310179 0.4437260
## 2 0.5559173 0.5312785 0.4734111 0.5962192
## 3 0.7280414 0.7478355 0.7051934 0.7930561plot(FLASSO_Train_CoxPH_Model_InternalCalibration)
##################################
# Estimating survival curves
# of the FLASSO model
# for the risk groups
# using the train data
##################################
FLASSO_Train_CoxPH_Model_KMPlot <- kmplot( FLASSO_Train_CoxPH_Model_InternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
##################################
# Conducting an external calibration
# of the FLASSO model
# using the test data
# for Year 5 (Time = 60)
##################################
FLASSO_Train_CoxPH_Model_ExternalCalibration <- calibrate_external(FLASSO_Train_CoxPH_Fit,
FLASSO_Train_Predictors,
FLASSO_Train_Time,
FLASSO_Train_Status,
FLASSO_Test_Predictors,
FLASSO_Test_Time,
FLASSO_Test_Status,
pred.at = 60,
ngroup = 3)
print(FLASSO_Train_CoxPH_Model_ExternalCalibration)## High-Dimensional Cox Model External Calibration Object
## Model type: flasso
## Calibration time point: 60
## Number of groups formed for calibration: 3summary(FLASSO_Train_CoxPH_Model_ExternalCalibration)## External Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.5932746 0.6108646 0.5337453 0.6991265
## 2 0.7357357 0.6646663 0.5871397 0.7524294
## 3 0.8516221 0.8459862 0.7872762 0.9090744plot(FLASSO_Train_CoxPH_Model_ExternalCalibration)
##################################
# Estimating survival curves
# of the FLASSO model
# for the risk groups
# using the test data
##################################
FLASSO_Test_CoxPH_Model_KMPlot <- kmplot( FLASSO_Train_CoxPH_Model_ExternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
##################################
# Conducting an external calibration
# of the FLASSO model
# using the test data
# for Year 10 (Time = 120)
##################################
FLASSO_Train_CoxPH_Model_ExternalCalibration <- calibrate_external(FLASSO_Train_CoxPH_Fit,
FLASSO_Train_Predictors,
FLASSO_Train_Time,
FLASSO_Train_Status,
FLASSO_Test_Predictors,
FLASSO_Test_Time,
FLASSO_Test_Status,
pred.at = 120,
ngroup = 3)
print(FLASSO_Train_CoxPH_Model_ExternalCalibration)## High-Dimensional Cox Model External Calibration Object
## Model type: flasso
## Calibration time point: 120
## Number of groups formed for calibration: 3summary(FLASSO_Train_CoxPH_Model_ExternalCalibration)## External Calibration Summary Table
## Predicted Observed Lower 95% Upper 95%
## 1 0.3700157 0.4684327 0.3765699 0.5827051
## 2 0.5574473 0.5055909 0.4054662 0.6304402
## 3 0.7364988 0.8090626 0.7401885 0.8843453plot(FLASSO_Train_CoxPH_Model_ExternalCalibration)
##################################
# Estimating survival curves
# of the FLASSO model
# for the risk groups
# using the test data
##################################
FLASSO_Test_CoxPH_Model_KMPlot <- kmplot( FLASSO_Train_CoxPH_Model_ExternalCalibration,
group.name = c("High-Risk", "Medium-Risk", "Low-Risk"),
time.at = 1:10 * 12)
1.6 Penalized Model Comparison Summary
Model performance comparison:
[A] All models had considerably high time-dependent AUROC overall although most reported lower values (and wider confidence intervals) at the 108th and 120th time periods potentially due to lesser case numbers for these survival times.
[B] The method which demonstrated consistent time-dependent AUROC across all the time periods was FLASSO: Fused Least Absolute Shrinkage and Selection Operator (survival,hdnom and penalized packages) with the most optimal number of retained predictors at 3.
[C] Lesser number of retained predictors were noted for the LASSO: Least Absolute Shrinkage and Selection Operator and ENET: Elastic Net (survival, hdnom, penalized and glmnet packages) methods with 5 and 7, respectively.
[D] Time-dependent AUROC for the MCP: Minimax Concave Penalty and SCAD: Smoothly Clipped Absolute Deviation (survival, hdnom, penalized and ncvreg packages) methods were comparable all across the time periods with retained number of predictors equal to 13 and 15, respectively.
Code Chunk | Output
##################################
# Consolidating all results
##################################
(TDAUROC_Summary <- rbind(LASSO_Train_IV_Summary,
LASSO_Train_EV_Summary,
ENET_Train_IV_Summary,
ENET_Train_EV_Summary,
MCP_Train_IV_Summary,
MCP_Train_EV_Summary,
SCAD_Train_IV_Summary,
SCAD_Train_EV_Summary,
FLASSO_Train_IV_Summary,
FLASSO_Train_EV_Summary))## Model Validation MedianTDAUROC Time
## 1 LASSO Cross-Validation 0.7593593 12
## 2 LASSO Cross-Validation 0.7646676 24
## 3 LASSO Cross-Validation 0.6581791 36
## 4 LASSO Cross-Validation 0.6661259 48
## 5 LASSO Cross-Validation 0.6567368 60
## 6 LASSO Cross-Validation 0.6713395 72
## 7 LASSO Cross-Validation 0.7160384 84
## 8 LASSO Cross-Validation 0.5971917 96
## 9 LASSO Cross-Validation 0.6706664 108
## 10 LASSO Cross-Validation 0.5622711 120
## 11 LASSO Test 0.7552909 12
## 12 LASSO Test 0.7416851 24
## 13 LASSO Test 0.6366259 36
## 14 LASSO Test 0.6214336 48
## 15 LASSO Test 0.6549471 60
## 16 LASSO Test 0.6722079 72
## 17 LASSO Test 0.6516368 84
## 18 LASSO Test 0.7317083 96
## 19 LASSO Test 0.3407045 108
## 20 LASSO Test 0.3250000 120
## 21 ENET Cross-Validation 0.7656844 12
## 22 ENET Cross-Validation 0.7754405 24
## 23 ENET Cross-Validation 0.6698848 36
## 24 ENET Cross-Validation 0.6747182 48
## 25 ENET Cross-Validation 0.6626752 60
## 26 ENET Cross-Validation 0.6795469 72
## 27 ENET Cross-Validation 0.7187430 84
## 28 ENET Cross-Validation 0.6060480 96
## 29 ENET Cross-Validation 0.6610317 108
## 30 ENET Cross-Validation 0.4654877 120
## 31 ENET Test 0.7726240 12
## 32 ENET Test 0.7457409 24
## 33 ENET Test 0.6343841 36
## 34 ENET Test 0.6183024 48
## 35 ENET Test 0.6556561 60
## 36 ENET Test 0.6644110 72
## 37 ENET Test 0.6299208 84
## 38 ENET Test 0.6980839 96
## 39 ENET Test 0.4185411 108
## 40 ENET Test 0.3500000 120
## 41 MCP Cross-Validation 0.7661746 12
## 42 MCP Cross-Validation 0.7781832 24
## 43 MCP Cross-Validation 0.6853087 36
## 44 MCP Cross-Validation 0.6799203 48
## 45 MCP Cross-Validation 0.6614552 60
## 46 MCP Cross-Validation 0.6763311 72
## 47 MCP Cross-Validation 0.6875798 84
## 48 MCP Cross-Validation 0.6151269 96
## 49 MCP Cross-Validation 0.6257171 108
## 50 MCP Cross-Validation 0.2666043 120
## 51 MCP Test 0.8036043 12
## 52 MCP Test 0.7463290 24
## 53 MCP Test 0.6444782 36
## 54 MCP Test 0.6019245 48
## 55 MCP Test 0.6482782 60
## 56 MCP Test 0.6010785 72
## 57 MCP Test 0.5660976 84
## 58 MCP Test 0.6525144 96
## 59 MCP Test 0.4698016 108
## 60 MCP Test 0.2750000 120
## 61 SCAD Cross-Validation 0.7664699 12
## 62 SCAD Cross-Validation 0.7787000 24
## 63 SCAD Cross-Validation 0.6853263 36
## 64 SCAD Cross-Validation 0.6800789 48
## 65 SCAD Cross-Validation 0.6619458 60
## 66 SCAD Cross-Validation 0.6770086 72
## 67 SCAD Cross-Validation 0.6874433 84
## 68 SCAD Cross-Validation 0.6159158 96
## 69 SCAD Cross-Validation 0.6252510 108
## 70 SCAD Cross-Validation 0.2666043 120
## 71 SCAD Test 0.8029733 12
## 72 SCAD Test 0.7451414 24
## 73 SCAD Test 0.6442855 36
## 74 SCAD Test 0.6007730 48
## 75 SCAD Test 0.6489130 60
## 76 SCAD Test 0.6047650 72
## 77 SCAD Test 0.5663698 84
## 78 SCAD Test 0.6516541 96
## 79 SCAD Test 0.4566631 108
## 80 SCAD Test 0.2750000 120
## 81 FLASSO Cross-Validation 0.7218108 12
## 82 FLASSO Cross-Validation 0.7109526 24
## 83 FLASSO Cross-Validation 0.6279633 36
## 84 FLASSO Cross-Validation 0.6507986 48
## 85 FLASSO Cross-Validation 0.6300183 60
## 86 FLASSO Cross-Validation 0.6545639 72
## 87 FLASSO Cross-Validation 0.7109444 84
## 88 FLASSO Cross-Validation 0.5765256 96
## 89 FLASSO Cross-Validation 0.7662841 108
## 90 FLASSO Cross-Validation 0.7907220 120
## 91 FLASSO Test 0.7364484 12
## 92 FLASSO Test 0.7027265 24
## 93 FLASSO Test 0.5865470 36
## 94 FLASSO Test 0.6113137 48
## 95 FLASSO Test 0.5997273 60
## 96 FLASSO Test 0.6353383 72
## 97 FLASSO Test 0.6897680 84
## 98 FLASSO Test 0.8667712 96
## 99 FLASSO Test 0.4605588 108
## 100 FLASSO Test 0.6250000 120TDAUROC_Summary$Model <- factor(TDAUROC_Summary$Model,
levels = c("LASSO",
"ENET",
"MCP",
"SCAD",
"FLASSO"))
TDAUROC_Summary$Time <- as.factor(TDAUROC_Summary$Time)
TDAUROC_Summary$Time <- factor(TDAUROC_Summary$Time,
levels= c("12","24","36","48","60",
"72","84","96","108","120"))
(TDAUROC_Plot <- dotplot(Model ~ MedianTDAUROC | Time,
data = TDAUROC_Summary,
groups = Validation,
main = "Penalized Model Performance Comparison",
ylab = "Model",
xlab = "Time-Dependent AUROC",
auto.key = list(adj = 1),
type=c("p", "h"),
origin = 0,
alpha = 0.45,
pch = 16,
cex = 2,
layout = c(5,2)))
2. References
[Book] Clinical Prediction Models by Ewout Steyerberg
[Book] Survival Analysis: A Self-Learning Text by David Kleinbaum and Mitchel Klein
[Book] Applied Survival Analysis Using R by Dirk Moore
[Book] Regression Modeling Strategies by Frank Harrell
[Book] R for Health Data Science by Ewen Harrison and Riinu Pius
[Book] Supervised Machine Learning by Michael Foley
[Book] Data Science for Biological, Medical and Health Research by Thomas Love
[R Package] survival by Terry Therneau
[R Package] survminer by Alboukadel Kassambara
[R Package] mice by Gerko Vink and Stef van Buuren
[R Package] foreign by R Core Team
[R Package] rms by Frank Harrell
[R Package] Hmisc by Frank Harrell and Charles Dupont
[R Package] VIM by Matthias Templ
[R Package] gridExtra by Baptiste Auguie
[R Package] finalfit by Ewen Harrison
[R Package] knitr by Yihui Xie
[R Package] dplyr by Hadley Wickham
[R Package] gtsummary by Daniel Sjoberg
[R Package] tidyr by Hadley Wickham
[R Package] purrr by Lionel Henry
[R Package] moments by Lukasz Komsta and Frederick Novomestky
[R Package] skimr by Elin Waring
[R Package] caret by Max Kuhn
[R Package] corrplot by Taiyun Wei
[R Package] SurvMetrics by Hanpu Zhou
[R Package] randomForestSRC by Hemant Ishwaran and Udaya Kogalur
[R Package] pec by Thomas Gerds
[R Package] hdnom by Nan Xiao
[R Package] ncvreg by Patrick Breheny
[Article] An Introduction to hdnom by Nan Xiao
[Article] Penalized Cox Models by Scikit Survival Team
[Article] Regularized Cox Regression by Kenneth Tay, Noah Simon, Jerome Friedman, Trevor Hastie, Rob Tibshirani and Balasubramanian Narasimhan
[Article] An Introduction to hdnom by Nan Xiao
[Article] An Introduction to hdnom by Nan Xiao
[Article] An Introduction to hdnom by Nan Xiao
[Article] Predictive Evaluation Metrics in Survival Analysis by Zhou Hanpu
[Article] Survival Analysis by Lisa Sullivan
[Article] Survival Analysis in R by Emily Zabor
[Article] Assessment of Discrimination in Survival Analysis by R-Studio Team
[Article] Cox Proportional-Hazards Regression by MedCalc Team
[Article] Cox Proportional-Hazards Model by R Studio Team
[Article] Exploring Time To Event / Survival Data by Thomas Love
[Article] Survival Analysis by Alboukadel Kassambara
[Article] Survival Analysis with R by Joseph Rickert
[Article] Cox Model Assumptions by Alboukadel Kassambara
[Article] Understanding Predictions in Survival Analysis by Scikit Team
[Article] Regularization in R Tutorial: Ridge, Lasso and Elastic Net by Datacamp Team
[Article] Penalized Regression Essentials: Ridge, Lasso & Elastic Net by STHDA Team
[Article] Lasso vs Ridge vs Elastic Net | ML by Geeks for Geeks Team
[Article] Lasso Regression Explained, Step by Step by Boris Giba
[Article] How to Develop LASSO Regression Models in Python by Jason Brownlee
[Article] Introduction to Lasso Regression by Statology Team
[Article] Elastic Net Regression Explained, Step by Step by Boris Giba
[Article] How to Develop Elastic Net Regression Models in Python by Jason Brownlee
[Article] Adaptive Lasso: What It Is and How to Implement in R by Ricardo Carvalho
[Article] L1 Penalization: The Lasso and Adaptive Lasso by Justin Petrovich
[Article] Adaptive Lasso, MCP, and SCAD by Patrick Breheny
[Article] The Smoothly Clipped Absolute Deviation (SCAD) penalty by Andrew Jones
[Article] Fused Lasso by Patrick Breheny
[Publication] Survival Analysis Part I: Basic Concepts and First Analyses by Taane Clark (British Journal of Cancer)
[Publication] Survival Analysis Part II: Multivariate Data Analysis – An Introduction to Concepts and Methods by Mike Bradburn (British Journal of Cancer)
[Publication] Survival Analysis Part III: Multivariate Data Analysis – Choosing a Model and Assessing its Adequacy and Fit by Mike Bradburn (British Journal of Cancer)
[Publication] Survival Analysis Part IV: Further Concepts and Methods in Survival Analysis by Taane Clark (British Journal of Cancer)
[Publication] Regression Models and Life-Tables by David Cox (Royal Statistical Society)
[Publication] Regression Shrinkage and Selection Via the Lasso by Rob Tibshirani (Journal of the Royal Statistical Society)
[Publication] The Adaptive Lasso and Its Oracle Properties by Hui Zou (Journal of the American Statistical Association)
[Publication] Regularization and Variable Selection via the Elastic Net by Hui Zou and Trevor Hastie (Journal of the Royal Statistical Society)
[Publication] On The Adaptive Elastic-Net With a Diverging Number of Parameters by Hui Zou and Hao Helen Zhang (Annals of Statistics)
[Publication] Adaptive Elastic Net Method for Cox Model by Chunhong Li, Xinxing Wei and Hongshuai Dai (arXiv e-prints)
[Publication] Coordinate Descent Algorithms for Non-Convex Penalized Regression with Applications to Biological Feature Selection by Patrick Breheny and Jian Huang (The Annals of Applied Statistics)
[Publication] Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent by Noah Simon, Jerome Friedman, Trevor Hastie and Rob Tibshirani (Journal of Statistical Software)
[Publication] Nearly Unbiased Variable Selection Under Minimax Concave Penalty by Cunhui Zhang (The Annals of Statistics)
[Publication] Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties by Jianqing Fan and Runze Li (Journal of the American Statistical Association )
[Publication] Sparsity and Smoothness via the Fused Lasso by Robert Tibshirani, Michael Saunders, Saharon Rosset, Ji Zhu and Keith Knight (Journal of the Royal Statistical Society)
[Tutorial] Survival Analysis / Time-To-Event Analysis in R by Heidi Seibold (Datacamp)