##Load original data
library(mstate)
## Loading required package: survival
crisk<- read.csv("/Users/phaptran/Documents/LEADING PROGRAM/N-DATA/ANALYSIS DTA/STATA ANALYSIS/R/crisk1784.csv",header=TRUE,)
#crisk<- read.csv("F:/LEADING PROGRAM/STATA ANALYSIS/SURVIVAL ANALYSIS/R/crisk1784.csv",header=TRUE)
tmat<- trans.illdeath(names=c("Normal", "IADL decline", "Death"))
tmat
## to
## from Normal IADL decline Death
## Normal NA 1 2
## IADL decline NA NA 3
## Death NA NA NA
#dataframe crisk
#crisk<-data.frame(crisk)
#Create covariates list to preserve
#covs<-c("base_hyp", "base_tch", "base_tg", "base_hdl", "event", "deceased", "prediabetes", "diabetes", "id", "age_1990", "sex", "smokea", "drinka", "diagrp", "bmijp", "agegrp", "bminew","illtime","deathtime","illstt","deathstt")
covs<-c("diagrp","agegrp","sex","smokea","drinka","base_hyp", "base_tch", "base_tg", "base_hdl","bmijp")
#Create long data for Multistate-model using msprep
msbmt <- msprep(time = c(NA, "illtime", "deathtime"), status = c(NA, "illstt", "deathstt"), data = data.frame(crisk), trans = tmat, keep = covs)
msbmt$agegrp<-factor(msbmt$agegrp)
msbmt$sex<-factor(msbmt$sex)
msbmt$diagrp<-factor(msbmt$diagrp)
msbmt$smokea<-factor(msbmt$smokea)
msbmt$drinka<-factor(msbmt$drinka)
msbmt$bmijp<-factor(msbmt$bmijp)
msbmt$base_hyp<-factor(msbmt$base_hyp)
msbmt$base_hdl<-factor(msbmt$base_hdl)
msbmt$base_tch<-factor(msbmt$base_tch)
msbmt$base_tg<-factor(msbmt$base_tg)
expcovs<-expand.covs(msbmt,covs[1:2],append=FALSE)
head(expcovs)
## diagrp2.1 diagrp2.2 diagrp2.3 diagrp3.1 diagrp3.2 diagrp3.3 agegrp2.1
## 1 0 0 0 0 0 0 1
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## agegrp2.2 agegrp2.3 agegrp3.1 agegrp3.2 agegrp3.3
## 1 0 0 0 0 0
## 2 1 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 1 0 0
## 6 0 0 0 1 0
head(msbmt)
## An object of class 'msdata'
##
## Data:
## id from to trans Tstart Tstop time status diagrp agegrp sex smokea drinka
## 1 1 1 2 1 0 20 20 0 1 2 2 1 1
## 2 1 1 3 2 0 20 20 0 1 2 2 1 1
## 3 2 1 2 1 0 20 20 0 1 1 2 1 1
## 4 2 1 3 2 0 20 20 0 1 1 2 1 1
## 5 3 1 2 1 0 20 20 0 1 3 2 1 3
## 6 3 1 3 2 0 20 20 0 1 3 2 1 3
## base_hyp base_tch base_tg base_hdl bmijp
## 1 0 1 0 0 2
## 2 0 1 0 0 2
## 3 0 0 1 0 3
## 4 0 0 1 0 3
## 5 0 0 0 0 2
## 6 0 0 0 0 2
events(msbmt)
## $Frequencies
## to
## from Normal IADL decline Death no event total entering
## Normal 0 374 360 1050 1784
## IADL decline 0 0 141 233 374
## Death 0 0 0 501 501
##
## $Proportions
## to
## from Normal IADL decline Death no event
## Normal 0.0000000 0.2096413 0.2017937 0.5885650
## IADL decline 0.0000000 0.0000000 0.3770053 0.6229947
## Death 0.0000000 0.0000000 0.0000000 1.0000000
msbmt <- expand.covs(msbmt, covs, append = TRUE, longnames = FALSE)
head(msbmt)
## An object of class 'msdata'
##
## Data:
## id from to trans Tstart Tstop time status diagrp agegrp sex smokea drinka
## 1 1 1 2 1 0 20 20 0 1 2 2 1 1
## 2 1 1 3 2 0 20 20 0 1 2 2 1 1
## 3 2 1 2 1 0 20 20 0 1 1 2 1 1
## 4 2 1 3 2 0 20 20 0 1 1 2 1 1
## 5 3 1 2 1 0 20 20 0 1 3 2 1 3
## 6 3 1 3 2 0 20 20 0 1 3 2 1 3
## base_hyp base_tch base_tg base_hdl bmijp diagrp1.1 diagrp1.2 diagrp1.3
## 1 0 1 0 0 2 0 0 0
## 2 0 1 0 0 2 0 0 0
## 3 0 0 1 0 3 0 0 0
## 4 0 0 1 0 3 0 0 0
## 5 0 0 0 0 2 0 0 0
## 6 0 0 0 0 2 0 0 0
## diagrp2.1 diagrp2.2 diagrp2.3 agegrp1.1 agegrp1.2 agegrp1.3 agegrp2.1
## 1 0 0 0 1 0 0 0
## 2 0 0 0 0 1 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 1
## 6 0 0 0 0 0 0 0
## agegrp2.2 agegrp2.3 sex.1 sex.2 sex.3 smokea1.1 smokea1.2 smokea1.3 smokea2.1
## 1 0 0 1 0 0 0 0 0 0
## 2 0 0 0 1 0 0 0 0 0
## 3 0 0 1 0 0 0 0 0 0
## 4 0 0 0 1 0 0 0 0 0
## 5 0 0 1 0 0 0 0 0 0
## 6 1 0 0 1 0 0 0 0 0
## smokea2.2 smokea2.3 drinka1.1 drinka1.2 drinka1.3 drinka2.1 drinka2.2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 1 0
## 6 0 0 0 0 0 0 1
## drinka2.3 base_hyp.1 base_hyp.2 base_hyp.3 base_tch.1 base_tch.2 base_tch.3
## 1 0 0 0 0 1 0 0
## 2 0 0 0 0 0 1 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## base_tg.1 base_tg.2 base_tg.3 base_hdl.1 base_hdl.2 base_hdl.3 bmijp1.1
## 1 0 0 0 0 0 0 1
## 2 0 0 0 0 0 0 0
## 3 1 0 0 0 0 0 0
## 4 0 1 0 0 0 0 0
## 5 0 0 0 0 0 0 1
## 6 0 0 0 0 0 0 0
## bmijp1.2 bmijp1.3 bmijp2.1 bmijp2.2 bmijp2.3
## 1 0 0 0 0 0
## 2 1 0 0 0 0
## 3 0 0 1 0 0
## 4 0 0 0 1 0
## 5 0 0 0 0 0
## 6 1 0 0 0 0
##Estimation WITHOUT ANY PROPORTIONAL ASSUMPTION
##PREDICTION
c5 <- coxph(Surv(Tstart, Tstop, status) ~diagrp1.1 + diagrp1.2 + diagrp1.3 + diagrp2.1 + diagrp2.2 + diagrp2.3
+ sex.1 + sex.2 + sex.3
+ agegrp1.1 + agegrp1.2 + agegrp1.3 + agegrp2.1 + agegrp2.2 + agegrp2.3
+ smokea1.1 + smokea1.2 + smokea1.3 + smokea2.1 + smokea2.2 + smokea2.3
+ drinka1.1 + drinka1.2 + drinka1.3 + drinka2.1 + drinka2.2 + drinka2.3
+ bmijp1.1 + bmijp1.2 + bmijp1.3 + bmijp2.1 + bmijp2.2 + bmijp2.3
+ base_hyp.1 + base_hyp.2 + base_hyp.3
+ base_tg.1 + base_tg.2 + base_tg.3
+ base_tch.1 + base_tch.2 + base_tch.3
+ base_hdl.1 + base_hdl.2 + base_hdl.3
+ strata(trans), data = msbmt, method = "breslow")
c5
## Call:
## coxph(formula = Surv(Tstart, Tstop, status) ~ diagrp1.1 + diagrp1.2 +
## diagrp1.3 + diagrp2.1 + diagrp2.2 + diagrp2.3 + sex.1 + sex.2 +
## sex.3 + agegrp1.1 + agegrp1.2 + agegrp1.3 + agegrp2.1 + agegrp2.2 +
## agegrp2.3 + smokea1.1 + smokea1.2 + smokea1.3 + smokea2.1 +
## smokea2.2 + smokea2.3 + drinka1.1 + drinka1.2 + drinka1.3 +
## drinka2.1 + drinka2.2 + drinka2.3 + bmijp1.1 + bmijp1.2 +
## bmijp1.3 + bmijp2.1 + bmijp2.2 + bmijp2.3 + base_hyp.1 +
## base_hyp.2 + base_hyp.3 + base_tg.1 + base_tg.2 + base_tg.3 +
## base_tch.1 + base_tch.2 + base_tch.3 + base_hdl.1 + base_hdl.2 +
## base_hdl.3 + strata(trans), data = msbmt, method = "breslow")
##
## coef exp(coef) se(coef) z p
## diagrp1.1 0.207896 1.231085 0.114691 1.813 0.06988
## diagrp1.2 0.288908 1.334969 0.118124 2.446 0.01445
## diagrp1.3 0.069372 1.071835 0.193001 0.359 0.71927
## diagrp2.1 0.334784 1.397639 0.156816 2.135 0.03277
## diagrp2.2 0.418033 1.518971 0.156142 2.677 0.00742
## diagrp2.3 -0.034882 0.965720 0.254402 -0.137 0.89094
## sex.1 0.207727 1.230877 0.176970 1.174 0.24048
## sex.2 -0.305795 0.736538 0.173309 -1.764 0.07766
## sex.3 -0.518535 0.595392 0.280534 -1.848 0.06455
## agegrp1.1 0.888472 2.431412 0.183661 4.838 1.31e-06
## agegrp1.2 1.019092 2.770677 0.188129 5.417 6.06e-08
## agegrp1.3 0.512495 1.669451 0.409342 1.252 0.21057
## agegrp2.1 1.528958 4.613367 0.174315 8.771 < 2e-16
## agegrp2.2 1.716383 5.564367 0.179843 9.544 < 2e-16
## agegrp2.3 1.098032 2.998261 0.390919 2.809 0.00497
## smokea1.1 -0.253131 0.776366 0.210797 -1.201 0.22982
## smokea1.2 0.341207 1.406644 0.200447 1.702 0.08871
## smokea1.3 0.016966 1.017110 0.370867 0.046 0.96351
## smokea2.1 0.266420 1.305283 0.154957 1.719 0.08556
## smokea2.2 1.122301 3.071914 0.155741 7.206 5.75e-13
## smokea2.3 0.535124 1.707660 0.258321 2.072 0.03831
## drinka1.1 0.358626 1.431361 0.332739 1.078 0.28112
## drinka1.2 0.489843 1.632060 0.262092 1.869 0.06163
## drinka1.3 -0.141018 0.868474 0.488689 -0.289 0.77292
## drinka2.1 0.432991 1.541862 0.161122 2.687 0.00720
## drinka2.2 -0.039507 0.961263 0.144978 -0.273 0.78524
## drinka2.3 -0.220789 0.801886 0.252254 -0.875 0.38143
## bmijp1.1 -0.021598 0.978633 0.259822 -0.083 0.93375
## bmijp1.2 -0.490286 0.612451 0.214007 -2.291 0.02196
## bmijp1.3 -0.486371 0.614854 0.378508 -1.285 0.19880
## bmijp2.1 0.103745 1.109317 0.272292 0.381 0.70320
## bmijp2.2 -0.323268 0.723780 0.233244 -1.386 0.16576
## bmijp2.3 -0.274660 0.759830 0.399815 -0.687 0.49210
## base_hyp.1 0.186630 1.205181 0.104760 1.781 0.07483
## base_hyp.2 0.129354 1.138093 0.107785 1.200 0.23010
## base_hyp.3 0.018712 1.018888 0.181004 0.103 0.91766
## base_tg.1 0.037876 1.038602 0.117287 0.323 0.74675
## base_tg.2 0.132737 1.141949 0.121106 1.096 0.27306
## base_tg.3 0.257026 1.293078 0.192325 1.336 0.18141
## base_tch.1 0.008622 1.008659 0.126974 0.068 0.94586
## base_tch.2 -0.176953 0.837819 0.141181 -1.253 0.21007
## base_tch.3 -0.027865 0.972520 0.234001 -0.119 0.90521
## base_hdl.1 -0.092496 0.911653 0.163530 -0.566 0.57165
## base_hdl.2 -0.365018 0.694184 0.166377 -2.194 0.02824
## base_hdl.3 0.596356 1.815491 0.243177 2.452 0.01419
##
## Likelihood ratio test=484.9 on 45 df, p=< 2.2e-16
## n= 3940, number of events= 875
## (2 observations deleted due to missingness)
##Estimation with proportioanlly assumption
msbmt$pr<-0
msbmt$pr[msbmt$trans==3]<-1
c6 <- coxph(Surv(Tstart, Tstop, status) ~diagrp1.1 + diagrp1.2 + diagrp1.3 + diagrp2.1 + diagrp2.2 + diagrp2.3
+ sex.1 + sex.2 + sex.3
+ agegrp1.1 + agegrp1.2 + agegrp1.3 + agegrp2.1 + agegrp2.2 + agegrp2.3
+ smokea1.1 + smokea1.2 + smokea1.3 + smokea2.1 + smokea2.2 + smokea2.3
+ drinka1.1 + drinka1.2 + drinka1.3 + drinka2.1 + drinka2.2 + drinka2.3
+ bmijp1.1 + bmijp1.2 + bmijp1.3 + bmijp2.1 + bmijp2.2 + bmijp2.3
+ base_hyp.1 + base_hyp.2 + base_hyp.3
+ base_tg.1 + base_tg.2 + base_tg.3
+ base_tch.1 + base_tch.2 + base_tch.3
+ base_hdl.1 + base_hdl.2 + base_hdl.3
+ pr + strata(to), data = msbmt, method = "breslow")
c6
## Call:
## coxph(formula = Surv(Tstart, Tstop, status) ~ diagrp1.1 + diagrp1.2 +
## diagrp1.3 + diagrp2.1 + diagrp2.2 + diagrp2.3 + sex.1 + sex.2 +
## sex.3 + agegrp1.1 + agegrp1.2 + agegrp1.3 + agegrp2.1 + agegrp2.2 +
## agegrp2.3 + smokea1.1 + smokea1.2 + smokea1.3 + smokea2.1 +
## smokea2.2 + smokea2.3 + drinka1.1 + drinka1.2 + drinka1.3 +
## drinka2.1 + drinka2.2 + drinka2.3 + bmijp1.1 + bmijp1.2 +
## bmijp1.3 + bmijp2.1 + bmijp2.2 + bmijp2.3 + base_hyp.1 +
## base_hyp.2 + base_hyp.3 + base_tg.1 + base_tg.2 + base_tg.3 +
## base_tch.1 + base_tch.2 + base_tch.3 + base_hdl.1 + base_hdl.2 +
## base_hdl.3 + pr + strata(to), data = msbmt, method = "breslow")
##
## coef exp(coef) se(coef) z p
## diagrp1.1 0.207896 1.231085 0.114691 1.813 0.069884
## diagrp1.2 0.273163 1.314114 0.118068 2.314 0.020689
## diagrp1.3 0.011291 1.011355 0.194192 0.058 0.953635
## diagrp2.1 0.334784 1.397639 0.156816 2.135 0.032770
## diagrp2.2 0.401659 1.494301 0.156234 2.571 0.010144
## diagrp2.3 0.041205 1.042066 0.256413 0.161 0.872330
## sex.1 0.207727 1.230877 0.176970 1.174 0.240475
## sex.2 -0.304042 0.737830 0.173470 -1.753 0.079652
## sex.3 -0.589437 0.554639 0.285300 -2.066 0.038826
## agegrp1.1 0.888472 2.431412 0.183661 4.838 1.31e-06
## agegrp1.2 0.998237 2.713494 0.188076 5.308 1.11e-07
## agegrp1.3 0.583976 1.793154 0.408456 1.430 0.152798
## agegrp2.1 1.528958 4.613367 0.174315 8.771 < 2e-16
## agegrp2.2 1.685275 5.393935 0.179685 9.379 < 2e-16
## agegrp2.3 1.342000 3.826687 0.384505 3.490 0.000483
## smokea1.1 -0.253131 0.776366 0.210797 -1.201 0.229818
## smokea1.2 0.342373 1.408286 0.200566 1.707 0.087816
## smokea1.3 0.040733 1.041574 0.378270 0.108 0.914248
## smokea2.1 0.266420 1.305283 0.154957 1.719 0.085557
## smokea2.2 1.097572 2.996881 0.155984 7.036 1.97e-12
## smokea2.3 0.644481 1.904998 0.261684 2.463 0.013785
## drinka1.1 0.358626 1.431361 0.332739 1.078 0.281123
## drinka1.2 0.469052 1.598479 0.261961 1.791 0.073367
## drinka1.3 -0.142818 0.866912 0.489288 -0.292 0.770371
## drinka2.1 0.432991 1.541862 0.161122 2.687 0.007202
## drinka2.2 -0.043135 0.957782 0.144936 -0.298 0.765996
## drinka2.3 -0.258878 0.771917 0.257582 -1.005 0.314882
## bmijp1.1 -0.021598 0.978633 0.259822 -0.083 0.933750
## bmijp1.2 -0.487066 0.614426 0.213906 -2.277 0.022785
## bmijp1.3 -0.597584 0.550139 0.376901 -1.586 0.112848
## bmijp2.1 0.103745 1.109317 0.272292 0.381 0.703200
## bmijp2.2 -0.326997 0.721086 0.233056 -1.403 0.160592
## bmijp2.3 -0.413777 0.661148 0.397477 -1.041 0.297871
## base_hyp.1 0.186630 1.205181 0.104760 1.781 0.074833
## base_hyp.2 0.125894 1.134162 0.107758 1.168 0.242684
## base_hyp.3 0.072219 1.074891 0.179847 0.402 0.688008
## base_tg.1 0.037876 1.038602 0.117287 0.323 0.746747
## base_tg.2 0.126980 1.135394 0.121060 1.049 0.294224
## base_tg.3 0.281151 1.324653 0.193503 1.453 0.146238
## base_tch.1 0.008622 1.008659 0.126974 0.068 0.945865
## base_tch.2 -0.179801 0.835436 0.141069 -1.275 0.202464
## base_tch.3 -0.065445 0.936651 0.233095 -0.281 0.778892
## base_hdl.1 -0.092496 0.911653 0.163530 -0.566 0.571651
## base_hdl.2 -0.347804 0.706237 0.165993 -2.095 0.036144
## base_hdl.3 0.580167 1.786337 0.242388 2.394 0.016686
## pr 1.539848 4.663883 0.654032 2.354 0.018553
##
## Likelihood ratio test=576.7 on 46 df, p=< 2.2e-16
## n= 3940, number of events= 875
## (2 observations deleted due to missingness)
cox.zph(c6)
## chisq df p
## diagrp1.1 2.81e+00 1 0.09350
## diagrp1.2 6.92e-01 1 0.40554
## diagrp1.3 1.10e+01 1 0.00089
## diagrp2.1 3.85e+00 1 0.04971
## diagrp2.2 1.10e+00 1 0.29372
## diagrp2.3 5.48e+00 1 0.01925
## sex.1 2.01e+00 1 0.15631
## sex.2 3.52e+00 1 0.06081
## sex.3 2.51e+00 1 0.11325
## agegrp1.1 4.47e+00 1 0.03452
## agegrp1.2 1.10e+01 1 0.00092
## agegrp1.3 1.52e+00 1 0.21752
## agegrp2.1 1.39e+01 1 0.00020
## agegrp2.2 2.72e-01 1 0.60189
## agegrp2.3 2.35e+01 1 1.2e-06
## smokea1.1 4.91e-01 1 0.48326
## smokea1.2 3.75e-01 1 0.54035
## smokea1.3 7.06e+00 1 0.00789
## smokea2.1 2.54e+00 1 0.11076
## smokea2.2 1.54e+00 1 0.21479
## smokea2.3 2.37e+01 1 1.1e-06
## drinka1.1 5.06e-01 1 0.47669
## drinka1.2 3.88e-04 1 0.98429
## drinka1.3 6.13e+00 1 0.01328
## drinka2.1 7.30e-01 1 0.39299
## drinka2.2 1.26e+00 1 0.26219
## drinka2.3 1.16e+01 1 0.00067
## bmijp1.1 7.75e-03 1 0.92983
## bmijp1.2 5.08e+00 1 0.02425
## bmijp1.3 2.00e+01 1 7.9e-06
## bmijp2.1 2.44e-01 1 0.62099
## bmijp2.2 8.55e+00 1 0.00346
## bmijp2.3 2.90e+00 1 0.08837
## base_hyp.1 3.14e-02 1 0.85936
## base_hyp.2 4.15e+00 1 0.04170
## base_hyp.3 6.99e+00 1 0.00818
## base_tg.1 1.85e-01 1 0.66693
## base_tg.2 7.65e-01 1 0.38185
## base_tg.3 1.94e+01 1 1.1e-05
## base_tch.1 6.79e-01 1 0.41009
## base_tch.2 3.07e-01 1 0.57976
## base_tch.3 4.31e+00 1 0.03789
## base_hdl.1 5.30e-01 1 0.46666
## base_hdl.2 5.30e+00 1 0.02138
## base_hdl.3 1.27e+01 1 0.00037
## pr 2.70e+01 1 2.0e-07
## GLOBAL 9.73e+01 46 1.5e-05
newd<-data.frame(sex=rep(0,3),agegrp=rep(0,3),diagrp=rep(0,3), smokea=rep(0,3),
drinka=rep(0,3),
bmijp=rep(0,3),
base_hyp=rep(0,3),base_tg=rep(0,3),base_hdl=rep(0,3),base_tch=rep(0,3),
trans=1:3)
newd$agegrp<-factor(newd$agegrp,levels = 0:2,labels = levels(factor(crisk$agegrp)))
newd$diagrp<-factor(newd$diagrp,levels = 0:2,labels = levels(factor(crisk$diagrp)))
newd$smokea<-factor(newd$smokea,levels = 0:2,labels = levels(factor(crisk$smokea)))
newd$drinka<-factor(newd$drinka,levels = 0:2,labels = levels(factor(crisk$drinka)))
newd$bmijp<-factor(newd$bmijp,levels = 0:2,labels = levels(factor(crisk$bmijp)))
newd$sex<-factor(newd$sex,levels=0:1,labels = levels(factor(crisk$sex)))
newd$base_hyp<-factor(newd$base_hyp,levels = 0:1,labels = levels(factor(crisk$base_hyp)))
newd$base_hdl<-factor(newd$base_hdl,levels = 0:1,labels = levels(factor(crisk$base_hdl)))
newd$base_tch<-factor(newd$base_tch,levels = 0:1,labels = levels(factor(crisk$base_tch)))
newd$base_tg<-factor(newd$base_tg,levels = 0:1,labels = levels(factor(crisk$base_tg)))
attr(newd,"trans")<-tmat
class(newd)<-c("msdata","data.frame")
newd<-expand.covs(newd,covs,longnames=FALSE)
newd$strata=1:3
newd
## An object of class 'msdata'
##
## Data:
## sex agegrp diagrp smokea drinka bmijp base_hyp base_tg base_hdl base_tch
## 1 1 1 1 1 1 1 0 0 0 0
## 2 1 1 1 1 1 1 0 0 0 0
## 3 1 1 1 1 1 1 0 0 0 0
## trans diagrp1.1 diagrp1.2 diagrp1.3 diagrp2.1 diagrp2.2 diagrp2.3 agegrp1.1
## 1 1 0 0 0 0 0 0 0
## 2 2 0 0 0 0 0 0 0
## 3 3 0 0 0 0 0 0 0
## agegrp1.2 agegrp1.3 agegrp2.1 agegrp2.2 agegrp2.3 sex.1 sex.2 sex.3 smokea1.1
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## smokea1.2 smokea1.3 smokea2.1 smokea2.2 smokea2.3 drinka1.1 drinka1.2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## drinka1.3 drinka2.1 drinka2.2 drinka2.3 base_hyp.1 base_hyp.2 base_hyp.3
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## base_tch.1 base_tch.2 base_tch.3 base_tg.1 base_tg.2 base_tg.3 base_hdl.1
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## base_hdl.2 base_hdl.3 bmijp1.1 bmijp1.2 bmijp1.3 bmijp2.1 bmijp2.2 bmijp2.3
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## strata
## 1 1
## 2 2
## 3 3
msf1<-msfit(c5,newdata=newd,trans=tmat) #MSF1
summary(msf1)
##
## Transition 1 (head and tail):
## time Haz seHaz lower upper
## 1 5.000000 0.005608302 0.00206968 0.002720853 0.01155999
## 2 5.117043 0.005608302 0.00206968 0.002720853 0.01155999
## 3 5.221081 0.005608302 0.00206968 0.002720853 0.01155999
## 4 5.303217 0.005608302 0.00206968 0.002720853 0.01155999
## 5 5.322382 0.005608302 0.00206968 0.002720853 0.01155999
## 6 5.423682 0.005608302 0.00206968 0.002720853 0.01155999
##
## ...
## time Haz seHaz lower upper
## 483 24.52293 0.05376569 0.01845611 0.02743556 0.105365
## 484 24.55852 0.05376569 0.01845611 0.02743556 0.105365
## 485 24.57495 0.05376569 0.01845611 0.02743556 0.105365
## 486 24.59685 0.05376569 0.01845611 0.02743556 0.105365
## 487 24.61875 0.05376569 0.01845611 0.02743556 0.105365
## 488 24.99931 0.05376569 0.01845611 0.02743556 0.105365
##
## Transition 2 (head and tail):
## time Haz seHaz lower upper
## 489 5.000000 0.0000000000 0.0000000000 0.000000e+00 0.000000000
## 490 5.117043 0.0000000000 0.0000000000 0.000000e+00 0.000000000
## 491 5.221081 0.0001494317 0.0001562989 1.923639e-05 0.001160811
## 492 5.303217 0.0002989488 0.0002304084 6.600114e-05 0.001354073
## 493 5.322382 0.0004486624 0.0002932950 1.245893e-04 0.001615691
## 494 5.423682 0.0004486624 0.0002932950 1.245893e-04 0.001615691
##
## ...
## time Haz seHaz lower upper
## 971 24.52293 0.09525081 0.02922248 0.05220646 0.1737853
## 972 24.55852 0.09567047 0.02934824 0.05243969 0.1745403
## 973 24.57495 0.09609089 0.02947420 0.05267337 0.1752966
## 974 24.59685 0.09651144 0.02960020 0.05290711 0.1760531
## 975 24.61875 0.09693258 0.02972637 0.05314119 0.1768106
## 976 24.99931 0.09693258 0.02972637 0.05314119 0.1768106
##
## Transition 3 (head and tail):
## time Haz seHaz lower upper
## 977 5.000000 0.000000000 0.000000000 0.0000000000 0.00000000
## 978 5.117043 0.007691603 0.008925108 0.0007912395 0.07476973
## 979 5.221081 0.007691603 0.008925108 0.0007912395 0.07476973
## 980 5.303217 0.007691603 0.008925108 0.0007912395 0.07476973
## 981 5.322382 0.007691603 0.008925108 0.0007912395 0.07476973
## 982 5.423682 0.015465741 0.014230286 0.0025478043 0.09388051
##
## ...
## time Haz seHaz lower upper
## 1459 24.52293 0.5711449 0.3287777 0.1848225 1.764972
## 1460 24.55852 0.5711449 0.3287777 0.1848225 1.764972
## 1461 24.57495 0.5711449 0.3287777 0.1848225 1.764972
## 1462 24.59685 0.5711449 0.3287777 0.1848225 1.764972
## 1463 24.61875 0.5711449 0.3287777 0.1848225 1.764972
## 1464 24.99931 0.5711449 0.3287777 0.1848225 1.764972
##variance
vH1<-msf1$varHaz
head(vH1[vH1$trans1==1&vH1$trans2==1,])
## time varHaz trans1 trans2
## 1 5.000000 4.283575e-06 1 1
## 2 5.117043 4.283575e-06 1 1
## 3 5.221081 4.283575e-06 1 1
## 4 5.303217 4.283575e-06 1 1
## 5 5.322382 4.283575e-06 1 1
## 6 5.423682 4.283575e-06 1 1
tail(vH1[vH1$trans1==1&vH1$trans2==1,])
## time varHaz trans1 trans2
## 483 24.52293 0.000340628 1 1
## 484 24.55852 0.000340628 1 1
## 485 24.57495 0.000340628 1 1
## 486 24.59685 0.000340628 1 1
## 487 24.61875 0.000340628 1 1
## 488 24.99931 0.000340628 1 1
tail(vH1[vH1$trans1==1&vH1$trans2==2,])
## time varHaz trans1 trans2
## 971 24.52293 0 1 2
## 972 24.55852 0 1 2
## 973 24.57495 0 1 2
## 974 24.59685 0 1 2
## 975 24.61875 0 1 2
## 976 24.99931 0 1 2
tail(vH1[vH1$trans1==1&vH1$trans2==3,])
## time varHaz trans1 trans2
## 1459 24.52293 0 1 3
## 1460 24.55852 0 1 3
## 1461 24.57495 0 1 3
## 1462 24.59685 0 1 3
## 1463 24.61875 0 1 3
## 1464 24.99931 0 1 3
tail(vH1[vH1$trans1==2&vH1$trans2==2,])
## time varHaz trans1 trans2
## 1947 24.52293 0.0008539534 2 2
## 1948 24.55852 0.0008613190 2 2
## 1949 24.57495 0.0008687283 2 2
## 1950 24.59685 0.0008761719 2 2
## 1951 24.61875 0.0008836571 2 2
## 1952 24.99931 0.0008836571 2 2
cox.zph(c6)
## chisq df p
## diagrp1.1 2.81e+00 1 0.09350
## diagrp1.2 6.92e-01 1 0.40554
## diagrp1.3 1.10e+01 1 0.00089
## diagrp2.1 3.85e+00 1 0.04971
## diagrp2.2 1.10e+00 1 0.29372
## diagrp2.3 5.48e+00 1 0.01925
## sex.1 2.01e+00 1 0.15631
## sex.2 3.52e+00 1 0.06081
## sex.3 2.51e+00 1 0.11325
## agegrp1.1 4.47e+00 1 0.03452
## agegrp1.2 1.10e+01 1 0.00092
## agegrp1.3 1.52e+00 1 0.21752
## agegrp2.1 1.39e+01 1 0.00020
## agegrp2.2 2.72e-01 1 0.60189
## agegrp2.3 2.35e+01 1 1.2e-06
## smokea1.1 4.91e-01 1 0.48326
## smokea1.2 3.75e-01 1 0.54035
## smokea1.3 7.06e+00 1 0.00789
## smokea2.1 2.54e+00 1 0.11076
## smokea2.2 1.54e+00 1 0.21479
## smokea2.3 2.37e+01 1 1.1e-06
## drinka1.1 5.06e-01 1 0.47669
## drinka1.2 3.88e-04 1 0.98429
## drinka1.3 6.13e+00 1 0.01328
## drinka2.1 7.30e-01 1 0.39299
## drinka2.2 1.26e+00 1 0.26219
## drinka2.3 1.16e+01 1 0.00067
## bmijp1.1 7.75e-03 1 0.92983
## bmijp1.2 5.08e+00 1 0.02425
## bmijp1.3 2.00e+01 1 7.9e-06
## bmijp2.1 2.44e-01 1 0.62099
## bmijp2.2 8.55e+00 1 0.00346
## bmijp2.3 2.90e+00 1 0.08837
## base_hyp.1 3.14e-02 1 0.85936
## base_hyp.2 4.15e+00 1 0.04170
## base_hyp.3 6.99e+00 1 0.00818
## base_tg.1 1.85e-01 1 0.66693
## base_tg.2 7.65e-01 1 0.38185
## base_tg.3 1.94e+01 1 1.1e-05
## base_tch.1 6.79e-01 1 0.41009
## base_tch.2 3.07e-01 1 0.57976
## base_tch.3 4.31e+00 1 0.03789
## base_hdl.1 5.30e-01 1 0.46666
## base_hdl.2 5.30e+00 1 0.02138
## base_hdl.3 1.27e+01 1 0.00037
## pr 2.70e+01 1 2.0e-07
## GLOBAL 9.73e+01 46 1.5e-05
newd$strata=c(1,2,2)
newd$pr<-c(0,0,1)
msf2<-msfit(c6,newdata = newd,trans=tmat)
summary(msf2)
##
## Transition 1 (head and tail):
## time Haz seHaz lower upper
## 1 5.000000 0.005608302 0.00206968 0.002720853 0.01155999
## 2 5.117043 0.005608302 0.00206968 0.002720853 0.01155999
## 3 5.221081 0.005608302 0.00206968 0.002720853 0.01155999
## 4 5.303217 0.005608302 0.00206968 0.002720853 0.01155999
## 5 5.322382 0.005608302 0.00206968 0.002720853 0.01155999
## 6 5.423682 0.005608302 0.00206968 0.002720853 0.01155999
##
## ...
## time Haz seHaz lower upper
## 483 24.52293 0.05376569 0.01845611 0.02743556 0.105365
## 484 24.55852 0.05376569 0.01845611 0.02743556 0.105365
## 485 24.57495 0.05376569 0.01845611 0.02743556 0.105365
## 486 24.59685 0.05376569 0.01845611 0.02743556 0.105365
## 487 24.61875 0.05376569 0.01845611 0.02743556 0.105365
## 488 24.99931 0.05376569 0.01845611 0.02743556 0.105365
##
## Transition 2 (head and tail):
## time Haz seHaz lower upper
## 489 5.000000 0.0000000000 0.0000000000 0.000000e+00 0.000000000
## 490 5.117043 0.0001407883 0.0001472330 1.813010e-05 0.001093283
## 491 5.221081 0.0002817106 0.0002170541 6.222492e-05 0.001275387
## 492 5.303217 0.0004227054 0.0002762120 1.174438e-04 0.001521408
## 493 5.322382 0.0005638657 0.0003305405 1.787301e-04 0.001778909
## 494 5.423682 0.0007053771 0.0003822502 2.438639e-04 0.002040305
##
## ...
## time Haz seHaz lower upper
## 971 24.52293 0.09635424 0.02956673 0.05280509 0.1758190
## 972 24.55852 0.09662427 0.02964837 0.05295440 0.1763074
## 973 24.57495 0.09689461 0.02973009 0.05310388 0.1767962
## 974 24.59685 0.09716500 0.02981183 0.05325338 0.1772852
## 975 24.61875 0.09743562 0.02989363 0.05340301 0.1777746
## 976 24.99931 0.09743562 0.02989363 0.05340301 0.1777746
##
## Transition 3 (head and tail):
## time Haz seHaz lower upper
## 977 5.000000 0.0000000000 0.0000000000 0.000000e+00 0.000000000
## 978 5.117043 0.0006566201 0.0007591123 6.811583e-05 0.006329658
## 979 5.221081 0.0013138654 0.0012017026 2.187838e-04 0.007890174
## 980 5.303217 0.0019714487 0.0016135627 3.963752e-04 0.009805382
## 981 5.322382 0.0026298036 0.0020140810 5.861594e-04 0.011798611
## 982 5.423682 0.0032897962 0.0024097666 7.828278e-04 0.013825211
##
## ...
## time Haz seHaz lower upper
## 1459 24.52293 0.4493849 0.2595940 0.1448469 1.394209
## 1460 24.55852 0.4506443 0.2603179 0.1452550 1.398095
## 1461 24.57495 0.4519051 0.2610427 0.1456637 1.401985
## 1462 24.59685 0.4531662 0.2617676 0.1460724 1.405876
## 1463 24.61875 0.4544283 0.2624932 0.1464814 1.409770
## 1464 24.99931 0.4544283 0.2624932 0.1464814 1.409770
##variance
vH2<-msf2$varHaz
head(vH2[vH2$trans1==1&vH2$trans2==1,])
## time varHaz trans1 trans2
## 1 5.000000 4.283575e-06 1 1
## 2 5.117043 4.283575e-06 1 1
## 3 5.221081 4.283575e-06 1 1
## 4 5.303217 4.283575e-06 1 1
## 5 5.322382 4.283575e-06 1 1
## 6 5.423682 4.283575e-06 1 1
tail(vH2[vH2$trans1==1&vH2$trans2==1,])
## time varHaz trans1 trans2
## 483 24.52293 0.000340628 1 1
## 484 24.55852 0.000340628 1 1
## 485 24.57495 0.000340628 1 1
## 486 24.59685 0.000340628 1 1
## 487 24.61875 0.000340628 1 1
## 488 24.99931 0.000340628 1 1
tail(vH2[vH2$trans1==1&vH2$trans2==2,])
## time varHaz trans1 trans2
## 971 24.52293 0 1 2
## 972 24.55852 0 1 2
## 973 24.57495 0 1 2
## 974 24.59685 0 1 2
## 975 24.61875 0 1 2
## 976 24.99931 0 1 2
tail(vH2[vH2$trans1==1&vH2$trans2==3,])
## time varHaz trans1 trans2
## 1459 24.52293 0 1 3
## 1460 24.55852 0 1 3
## 1461 24.57495 0 1 3
## 1462 24.59685 0 1 3
## 1463 24.61875 0 1 3
## 1464 24.99931 0 1 3
tail(vH2[vH2$trans1==2&vH2$trans2==2,])
## time varHaz trans1 trans2
## 1947 24.52293 0.0008741917 2 2
## 1948 24.55852 0.0008790257 2 2
## 1949 24.57495 0.0008838780 2 2
## 1950 24.59685 0.0008887449 2 2
## 1951 24.61875 0.0008936292 2 2
## 1952 24.99931 0.0008936292 2 2
##PLOT Stratified baseline hazards
par(mfrow=c(1,2))
plot(msf1, cols = rep(1, 3), lwd = 2, lty = 1:3, xlab = "Years since entry",ylab = "Stratified baseline hazards", legend.pos = c(5, 1.2),ylim = c(0,1))
##PLOT Proportional baseline hazards
plot(msf2, cols = rep(1, 3), lwd = 2, lty = 1:3, xlab = "Time since entry (years) ",ylab = "Proportional baseline hazards", legend.pos = c(5,1),ylim = c(0,1),xlim = c(0,25))
par(mfrow = c(1, 1))
##PRobabilities transitions
pt<-probtrans(msf2,predt=0)
head(pt[[3]])
## time pstate1 pstate2 pstate3 se1 se2 se3
## 1 0.000000 0 0 1 0 0 0
## 2 5.000000 0 0 1 0 0 0
## 3 5.117043 0 0 1 0 0 0
## 4 5.221081 0 0 1 0 0 0
## 5 5.303217 0 0 1 0 0 0
## 6 5.322382 0 0 1 0 0 0
tail(pt[[3]])
## time pstate1 pstate2 pstate3 se1 se2 se3
## 484 24.52293 0 0 1 0 0 0
## 485 24.55852 0 0 1 0 0 0
## 486 24.57495 0 0 1 0 0 0
## 487 24.59685 0 0 1 0 0 0
## 488 24.61875 0 0 1 0 0 0
## 489 24.99931 0 0 1 0 0 0
summary(pt,from=2)
##
## Prediction from state 2 :
## time pstate1 pstate2 pstate3 se1 se2 se3
## 1 0.000000 0 1.0000000 0.0000000000 0 0.0000000000 0.0000000000
## 2 5.000000 0 1.0000000 0.0000000000 0 0.0000000000 0.0000000000
## 3 5.117043 0 0.9993434 0.0006566201 0 0.0007586138 0.0007586138
## 4 5.221081 0 0.9986866 0.0013134338 0 0.0012001242 0.0012001242
## 5 5.303217 0 0.9980298 0.0019701535 0 0.0016103837 0.0016103837
## 6 5.322382 0 0.9973728 0.0026272113 0 0.0020087896 0.0020087896
## 7 5.423682 0 0.9967145 0.0032854700 0 0.0024018494 0.0024018494
## 8 5.741273 0 0.9960555 0.0039445452 0 0.0027916674 0.0027916674
## 9 5.776865 0 0.9953953 0.0046046629 0 0.0031796202 0.0031796202
## 10 5.957563 0 0.9940714 0.0059285557 0 0.0039527166 0.0039527166
## 11 6.056126 0 0.9934070 0.0065930003 0 0.0043392757 0.0043392757
## 12 6.067077 0 0.9927428 0.0072572335 0 0.0047247806 0.0047247806
## 13 6.231349 0 0.9920782 0.0079218288 0 0.0051097525 0.0051097525
## 14 6.247776 0 0.9914097 0.0085902740 0 0.0054965231 0.0054965231
## 15 6.266941 0 0.9907402 0.0092597663 0 0.0058833349 0.0058833349
## 16 6.433949 0 0.9900669 0.0099331092 0 0.0062717368 0.0062717368
## 17 6.442163 0 0.9893911 0.0106089196 0 0.0066608014 0.0066608014
## 18 6.499658 0 0.9887155 0.0112844707 0 0.0070491883 0.0070491883
## 19 6.620123 0 0.9880401 0.0119599461 0 0.0074370527 0.0074370527
## 20 6.729637 0 0.9873648 0.0126352343 0 0.0078243575 0.0078243575
## 21 7.006160 0 0.9866885 0.0133114858 0 0.0082118303 0.0082118303
## 22 7.019849 0 0.9860124 0.0139876485 0 0.0085988457 0.0085988457
## 23 7.082820 0 0.9853363 0.0146636913 0 0.0089854025 0.0089854025
## 24 7.301848 0 0.9846604 0.0153395898 0 0.0093715014 0.0093715014
## 25 7.414100 0 0.9839845 0.0160155288 0 0.0097572668 0.0097572668
## 26 7.605750 0 0.9833074 0.0166925502 0 0.0101433057 0.0101433057
## 27 7.739904 0 0.9826306 0.0173694204 0 0.0105289102 0.0105289102
## 28 7.775496 0 0.9819526 0.0180474134 0 0.0109148464 0.0109148464
## 29 8.032854 0 0.9812740 0.0187260218 0 0.0113008163 0.0113008163
## 30 8.158795 0 0.9805936 0.0194063511 0 0.0116875395 0.0116875395
## 31 8.249999 0 0.9805936 0.0194063511 0 0.0116875395 0.0116875395
## 32 8.287475 0 0.9799126 0.0200873615 0 0.0120743929 0.0120743929
## 33 8.459959 0 0.9792310 0.0207690125 0 0.0124612455 0.0124612455
## 34 8.511978 0 0.9785482 0.0214518313 0 0.0128484636 0.0128484636
## 35 8.731007 0 0.9778643 0.0221356668 0 0.0132359540 0.0132359540
## 36 8.799453 0 0.9771806 0.0228194402 0 0.0136230874 0.0136230874
## 37 9.045859 0 0.9764951 0.0235049433 0 0.0140109155 0.0140109155
## 38 9.078713 0 0.9758087 0.0241912709 0 0.0143989073 0.0143989073
## 39 9.352498 0 0.9751223 0.0248777343 0 0.0147866624 0.0147866624
## 40 9.409993 0 0.9744343 0.0255656836 0 0.0151749669 0.0151749669
## 41 9.481177 0 0.9737433 0.0262566970 0 0.0155647301 0.0155647301
## 42 9.530458 0 0.9730524 0.0269475668 0 0.0159540915 0.0159540915
## 43 9.799999 0 0.9730524 0.0269475668 0 0.0159540915 0.0159540915
## 44 9.853525 0 0.9716680 0.0283319671 0 0.0167313808 0.0167313808
## 45 9.900000 0 0.9716680 0.0283319671 0 0.0167313808 0.0167313808
## 46 9.927447 0 0.9709717 0.0290283328 0 0.0171229635 0.0171229635
## 47 9.930184 0 0.9702750 0.0297249843 0 0.0175143903 0.0175143903
## 48 10.000000 0 0.9702750 0.0297249843 0 0.0175143903 0.0175143903
## 49 10.045175 0 0.9696168 0.0303832060 0 0.0178827569 0.0178827569
## 50 10.206708 0 0.9689585 0.0310415085 0 0.0182509266 0.0182509266
## 51 10.294319 0 0.9682996 0.0317003670 0 0.0186191313 0.0186191313
## 52 10.305270 0 0.9669803 0.0330196562 0 0.0193554099 0.0193554099
## 53 10.310746 0 0.9663200 0.0336799672 0 0.0197236793 0.0197236793
## 54 10.368241 0 0.9656598 0.0343401624 0 0.0200916261 0.0200916261
## 55 10.433949 0 0.9649974 0.0350026160 0 0.0204608289 0.0204608289
## 56 10.477755 0 0.9643340 0.0356660241 0 0.0208303244 0.0208303244
## 57 10.699521 0 0.9636703 0.0363296817 0 0.0211997078 0.0211997078
## 58 10.713210 0 0.9630047 0.0369952593 0 0.0215699095 0.0215699095
## 59 10.765229 0 0.9623392 0.0376607781 0 0.0219398117 0.0219398117
## 60 10.863791 0 0.9616715 0.0383285165 0 0.0223107333 0.0223107333
## 61 11.006160 0 0.9610030 0.0389969727 0 0.0226817911 0.0226817911
## 62 11.071869 0 0.9603334 0.0396665707 0 0.0230532219 0.0230532219
## 63 11.080082 0 0.9596636 0.0403363696 0 0.0234244941 0.0234244941
## 64 11.093771 0 0.9589937 0.0410063048 0 0.0237955707 0.0237955707
## 65 11.096509 0 0.9583228 0.0416772223 0 0.0241669382 0.0241669382
## 66 11.312799 0 0.9576518 0.0423481520 0 0.0245380385 0.0245380385
## 67 11.337440 0 0.9569801 0.0430198725 0 0.0249093083 0.0249093083
## 68 11.353868 0 0.9563071 0.0436929385 0 0.0252810559 0.0252810559
## 69 11.373033 0 0.9556317 0.0443682743 0 0.0256538243 0.0256538243
## 70 11.378508 0 0.9549552 0.0450448396 0 0.0260269998 0.0260269998
## 71 11.460644 0 0.9542752 0.0457247887 0 0.0264018012 0.0264018012
## 72 11.561944 0 0.9535953 0.0464047254 0 0.0267763104 0.0267763104
## 73 11.655031 0 0.9529146 0.0470853598 0 0.0271509238 0.0271509238
## 74 11.824778 0 0.9522338 0.0477662214 0 0.0275253698 0.0275253698
## 75 11.838467 0 0.9515523 0.0484477241 0 0.0278998865 0.0278998865
## 76 11.852156 0 0.9508708 0.0491292272 0 0.0282741162 0.0282741162
## 77 11.868583 0 0.9501895 0.0498105324 0 0.0286479482 0.0286479482
## 78 11.893224 0 0.9495073 0.0504926594 0 0.0290219489 0.0290219489
## 79 11.934292 0 0.9488249 0.0511751497 0 0.0293958627 0.0293958627
## 80 11.947981 0 0.9481420 0.0518579887 0 0.0297696805 0.0297696805
## 81 11.956194 0 0.9474580 0.0525419742 0 0.0301438132 0.0301438132
## 82 12.002738 0 0.9467742 0.0532258470 0 0.0305175928 0.0305175928
## 83 12.095825 0 0.9460893 0.0539107185 0 0.0308916331 0.0308916331
## 84 12.134154 0 0.9454043 0.0545956974 0 0.0312654566 0.0312654566
## 85 12.161533 0 0.9447172 0.0552828353 0 0.0316401763 0.0316401763
## 86 12.232718 0 0.9440288 0.0559712007 0 0.0320152769 0.0320152769
## 87 12.238193 0 0.9433408 0.0566591968 0 0.0323898778 0.0323898778
## 88 12.276523 0 0.9426508 0.0573491978 0 0.0327652376 0.0327652376
## 89 12.372348 0 0.9419608 0.0580391825 0 0.0331402907 0.0331402907
## 90 12.501027 0 0.9412712 0.0587288132 0 0.0335148511 0.0335148511
## 91 12.555783 0 0.9405809 0.0594191206 0 0.0338894847 0.0338894847
## 92 12.572211 0 0.9398897 0.0601103119 0 0.0342643040 0.0342643040
## 93 12.596851 0 0.9391986 0.0608014465 0 0.0346387929 0.0346387929
## 94 12.629705 0 0.9385075 0.0614924838 0 0.0350129291 0.0350129291
## 95 12.706366 0 0.9378151 0.0621848817 0 0.0353879114 0.0353879114
## 96 12.714579 0 0.9371230 0.0628769591 0 0.0357624171 0.0357624171
## 97 12.717317 0 0.9364296 0.0635703534 0 0.0361373403 0.0361373403
## 98 12.867899 0 0.9357362 0.0642637930 0 0.0365119866 0.0365119866
## 99 12.917180 0 0.9350419 0.0649580695 0 0.0368867870 0.0368867870
## 100 12.993840 0 0.9343476 0.0656523594 0 0.0372612916 0.0372612916
## 101 13.070499 0 0.9336523 0.0663477051 0 0.0376360667 0.0376360667
## 102 13.089664 0 0.9329553 0.0670446602 0 0.0380113988 0.0380113988
## 103 13.136209 0 0.9322586 0.0677413678 0 0.0383862899 0.0383862899
## 104 13.341547 0 0.9315620 0.0684380174 0 0.0387608433 0.0387608433
## 105 13.349760 0 0.9308655 0.0691345322 0 0.0391350171 0.0391350171
## 106 13.415469 0 0.9301693 0.0698306635 0 0.0395086764 0.0395086764
## 107 13.448323 0 0.9294735 0.0705264789 0 0.0398818585 0.0398818585
## 108 13.467488 0 0.9287761 0.0712238839 0 0.0402555257 0.0402555257
## 109 13.500342 0 0.9280782 0.0719218337 0 0.0406291800 0.0406291800
## 110 13.620808 0 0.9266799 0.0733201354 0 0.0413765483 0.0413765483
## 111 13.637235 0 0.9259790 0.0740209957 0 0.0417508429 0.0417508429
## 112 13.686516 0 0.9252779 0.0747220509 0 0.0421249306 0.0421249306
## 113 13.856263 0 0.9245769 0.0754230725 0 0.0424986881 0.0424986881
## 114 13.872690 0 0.9238762 0.0761238411 0 0.0428719973 0.0428719973
## 115 13.941136 0 0.9231751 0.0768248581 0 0.0432451190 0.0432451190
## 116 13.963039 0 0.9224705 0.0775295328 0 0.0436209270 0.0436209270
## 117 13.965776 0 0.9217659 0.0782341104 0 0.0439963670 0.0439963670
## 118 13.993155 0 0.9210603 0.0789396997 0 0.0443719409 0.0443719409
## 119 13.995893 0 0.9203536 0.0796464415 0 0.0447478157 0.0447478157
## 120 14.006845 0 0.9196457 0.0803542921 0 0.0451239662 0.0451239662
## 121 14.064340 0 0.9189369 0.0810630594 0 0.0455002879 0.0455002879
## 122 14.083505 0 0.9182278 0.0817722335 0 0.0458765067 0.0458765067
## 123 14.130048 0 0.9175183 0.0824817140 0 0.0462525454 0.0462525454
## 124 14.239562 0 0.9168089 0.0831910758 0 0.0466281986 0.0466281986
## 125 14.286105 0 0.9160982 0.0839017936 0 0.0470042542 0.0470042542
## 126 14.297057 0 0.9153856 0.0846143702 0 0.0473809767 0.0473809767
## 127 14.425735 0 0.9146718 0.0853281548 0 0.0477580167 0.0477580167
## 128 14.428473 0 0.9139574 0.0860425704 0 0.0481350660 0.0481350660
## 129 14.477755 0 0.9125256 0.0874744464 0 0.0488894887 0.0488894887
## 130 14.559891 0 0.9118095 0.0881904711 0 0.0492664037 0.0492664037
## 131 14.595483 0 0.9110935 0.0889065106 0 0.0496429965 0.0496429965
## 132 14.694045 0 0.9103772 0.0896228480 0 0.0500194166 0.0500194166
## 133 14.784394 0 0.9096602 0.0903397861 0 0.0503958238 0.0503958238
## 134 14.798083 0 0.9089403 0.0910596949 0 0.0507734468 0.0507734468
## 135 14.841889 0 0.9082196 0.0917803532 0 0.0511511313 0.0511511313
## 136 14.855578 0 0.9074976 0.0925024162 0 0.0515292204 0.0515292204
## 137 14.929501 0 0.9067754 0.0932246271 0 0.0519070502 0.0519070502
## 138 14.956879 0 0.9060529 0.0939471448 0 0.0522847040 0.0522847040
## 139 14.984258 0 0.9053303 0.0946697134 0 0.0526620467 0.0526620467
## 140 15.000000 0 0.9053303 0.0946697134 0 0.0526620467 0.0526620467
## 141 15.093771 0 0.9046494 0.0953505673 0 0.0530153020 0.0530153020
## 142 15.175907 0 0.9039669 0.0960331486 0 0.0533691588 0.0533691588
## 143 15.192334 0 0.9032845 0.0967154585 0 0.0537225776 0.0537225776
## 144 15.227926 0 0.9025975 0.0974025434 0 0.0540781145 0.0540781145
## 145 15.271731 0 0.9019092 0.0980907905 0 0.0544339511 0.0544339511
## 146 15.277207 0 0.9012201 0.0987799390 0 0.0547899509 0.0547899509
## 147 15.279945 0 0.9005305 0.0994695343 0 0.0551458782 0.0551458782
## 148 15.293634 0 0.8998410 0.1001589610 0 0.0555014132 0.0555014132
## 149 15.373033 0 0.8991516 0.1008484337 0 0.0558566672 0.0558566672
## 150 15.457906 0 0.8984610 0.1015390425 0 0.0562121408 0.0562121408
## 151 15.479809 0 0.8977705 0.1022294512 0 0.0565672045 0.0565672045
## 152 15.523614 0 0.8970800 0.1029199968 0 0.0569220313 0.0569220313
## 153 15.578371 0 0.8963878 0.1036122302 0 0.0572773739 0.0572773739
## 154 15.594798 0 0.8956956 0.1043044414 0 0.0576323962 0.0576323962
## 155 15.696098 0 0.8950028 0.1049972427 0 0.0579874115 0.0579874115
## 156 15.726215 0 0.8943101 0.1056898820 0 0.0583420335 0.0583420335
## 157 15.748117 0 0.8936178 0.1063821826 0 0.0586961711 0.0586961711
## 158 15.791924 0 0.8929257 0.1070742662 0 0.0590498872 0.0590498872
## 159 15.822040 0 0.8922327 0.1077673160 0 0.0594037863 0.0594037863
## 160 15.854894 0 0.8915390 0.1084609743 0 0.0597576848 0.0597576848
## 161 15.879535 0 0.8908457 0.1091542802 0 0.0601110908 0.0601110908
## 162 15.917865 0 0.8901518 0.1098481923 0 0.0604647665 0.0604647665
## 163 15.947981 0 0.8894576 0.1105424070 0 0.0608182606 0.0608182606
## 164 15.950719 0 0.8887632 0.1112367677 0 0.0611715061 0.0611715061
## 165 15.994524 0 0.8880682 0.1119318416 0 0.0615248004 0.0615248004
## 166 16.087612 0 0.8873722 0.1126278071 0 0.0618785401 0.0618785401
## 167 16.114990 0 0.8866748 0.1133251997 0 0.0622326892 0.0622326892
## 168 16.134155 0 0.8859770 0.1140229903 0 0.0625867224 0.0625867224
## 169 16.167009 0 0.8852794 0.1147206072 0 0.0629403488 0.0629403488
## 170 16.180698 0 0.8845819 0.1154181294 0 0.0632936234 0.0632936234
## 171 16.205339 0 0.8838848 0.1161151627 0 0.0636463309 0.0636463309
## 172 16.229979 0 0.8831877 0.1168122935 0 0.0639987243 0.0639987243
## 173 16.336756 0 0.8824897 0.1175102570 0 0.0643511496 0.0643511496
## 174 16.342232 0 0.8817923 0.1182077118 0 0.0647029973 0.0647029973
## 175 16.377823 0 0.8810950 0.1189050425 0 0.0650544624 0.0650544624
## 176 16.405203 0 0.8803974 0.1196025854 0 0.0654058630 0.0654058630
## 177 16.410677 0 0.8796987 0.1203012576 0 0.0657575119 0.0657575119
## 178 16.440794 0 0.8789982 0.1210018259 0 0.0661097129 0.0661097129
## 179 16.484600 0 0.8782968 0.1217032386 0 0.0664619395 0.0664619395
## 180 16.487337 0 0.8775952 0.1224048022 0 0.0668139071 0.0668139071
## 181 16.501026 0 0.8768940 0.1231059730 0 0.0671653518 0.0671653518
## 182 16.643394 0 0.8761910 0.1238090017 0 0.0675171963 0.0675171963
## 183 16.676249 0 0.8754882 0.1245117622 0 0.0678685790 0.0678685790
## 184 16.752909 0 0.8740836 0.1259163658 0 0.0685695769 0.0685695769
## 185 16.777550 0 0.8733805 0.1266195238 0 0.0689201371 0.0689201371
## 186 16.788502 0 0.8726764 0.1273235561 0 0.0692708033 0.0692708033
## 187 16.793976 0 0.8719725 0.1280274649 0 0.0696210781 0.0696210781
## 188 16.796715 0 0.8712673 0.1287327202 0 0.0699716325 0.0699716325
## 189 16.813141 0 0.8705625 0.1294375262 0 0.0703216319 0.0703216319
## 190 16.898016 0 0.8698562 0.1301438264 0 0.0706720405 0.0706720405
## 191 17.026693 0 0.8691504 0.1308496093 0 0.0710218592 0.0710218592
## 192 17.054073 0 0.8684443 0.1315556844 0 0.0713714984 0.0713714984
## 193 17.133471 0 0.8677374 0.1322626022 0 0.0717210787 0.0717210787
## 194 17.136208 0 0.8670306 0.1329693943 0 0.0720702621 0.0720702621
## 195 17.144423 0 0.8663230 0.1336769581 0 0.0724194911 0.0724194911
## 196 17.196442 0 0.8656158 0.1343841970 0 0.0727682239 0.0727682239
## 197 17.237509 0 0.8649082 0.1350917621 0 0.0731167809 0.0731167809
## 198 17.245722 0 0.8642011 0.1357988934 0 0.0734647880 0.0734647880
## 199 17.371664 0 0.8634933 0.1365067191 0 0.0738127992 0.0738127992
## 200 17.399042 0 0.8627857 0.1372142533 0 0.0741603302 0.0741603302
## 201 17.401779 0 0.8620781 0.1379218686 0 0.0745075789 0.0745075789
## 202 17.481176 0 0.8613709 0.1386290565 0 0.0748542807 0.0748542807
## 203 17.486652 0 0.8606640 0.1393359814 0 0.0752005165 0.0752005165
## 204 17.508556 0 0.8599568 0.1400432115 0 0.0755465500 0.0755465500
## 205 17.533195 0 0.8592498 0.1407501686 0 0.0758921121 0.0758921121
## 206 17.579741 0 0.8585428 0.1414572217 0 0.0762373832 0.0762373832
## 207 17.590691 0 0.8578354 0.1421646441 0 0.0765824965 0.0765824965
## 208 17.601643 0 0.8571274 0.1428725723 0 0.0769273654 0.0769273654
## 209 17.645449 0 0.8557088 0.1442912367 0 0.0776171078 0.0776171078
## 210 17.697468 0 0.8549992 0.1450008465 0 0.0779617706 0.0779617706
## 211 17.738535 0 0.8542883 0.1457116599 0 0.0783066734 0.0783066734
## 212 17.746748 0 0.8535776 0.1464224337 0 0.0786512136 0.0786512136
## 213 17.771389 0 0.8521549 0.1478451395 0 0.0793394782 0.0793394782
## 214 17.787817 0 0.8514426 0.1485574218 0 0.0796837116 0.0796837116
## 215 17.831623 0 0.8507293 0.1492707067 0 0.0800280815 0.0800280815
## 216 17.949350 0 0.8500156 0.1499843719 0 0.0803722874 0.0803722874
## 217 17.957563 0 0.8493023 0.1506977385 0 0.0807160013 0.0807160013
## 218 17.993155 0 0.8485884 0.1514116088 0 0.0810595520 0.0810595520
## 219 18.023272 0 0.8478746 0.1521254357 0 0.0814027328 0.0814027328
## 220 18.026011 0 0.8471608 0.1528391768 0 0.0817455569 0.0817455569
## 221 18.119097 0 0.8464466 0.1535533610 0 0.0820882432 0.0820882432
## 222 18.121834 0 0.8457326 0.1542674135 0 0.0824305161 0.0824305161
## 223 18.124573 0 0.8450182 0.1549818128 0 0.0827725762 0.0827725762
## 224 18.135523 0 0.8443036 0.1556964225 0 0.0831143854 0.0831143854
## 225 18.168377 0 0.8435895 0.1564105216 0 0.0834555993 0.0834555993
## 226 18.171116 0 0.8428738 0.1571261818 0 0.0837972045 0.0837972045
## 227 18.228611 0 0.8421573 0.1578427046 0 0.0841388670 0.0841388670
## 228 18.242300 0 0.8414399 0.1585601364 0 0.0844806067 0.0844806067
## 229 18.277891 0 0.8407228 0.1592771942 0 0.0848218132 0.0848218132
## 230 18.351814 0 0.8400058 0.1599941887 0 0.0851626342 0.0851626342
## 231 18.406570 0 0.8392891 0.1607108540 0 0.0855029433 0.0855029433
## 232 18.433949 0 0.8385723 0.1614276614 0 0.0858429642 0.0858429642
## 233 18.439425 0 0.8378546 0.1621453546 0 0.0861830478 0.0861830478
## 234 18.455853 0 0.8371372 0.1628627522 0 0.0865226345 0.0865226345
## 235 18.466805 0 0.8364198 0.1635801936 0 0.0868618846 0.0868618846
## 236 18.469542 0 0.8357025 0.1642974538 0 0.0872006916 0.0872006916
## 237 18.477755 0 0.8349858 0.1650141654 0 0.0875388823 0.0875388823
## 238 18.483231 0 0.8342685 0.1657314751 0 0.0878768465 0.0878768465
## 239 18.491444 0 0.8335507 0.1664493360 0 0.0882147110 0.0882147110
## 240 18.499659 0 0.8328331 0.1671668643 0 0.0885520602 0.0885520602
## 241 18.516085 0 0.8321148 0.1678851502 0 0.0888893384 0.0888893384
## 242 18.579056 0 0.8313951 0.1686049299 0 0.0892269538 0.0892269538
## 243 18.595482 0 0.8306757 0.1693242914 0 0.0895640115 0.0895640115
## 244 18.633812 0 0.8299568 0.1700432341 0 0.0899005117 0.0899005117
## 245 18.655716 0 0.8292378 0.1707621931 0 0.0902366576 0.0902366576
## 246 18.674881 0 0.8285192 0.1714808364 0 0.0905722944 0.0905722944
## 247 18.724161 0 0.8278007 0.1721992644 0 0.0909074688 0.0909074688
## 248 18.765230 0 0.8270826 0.1729174170 0 0.0912421532 0.0912421532
## 249 18.833675 0 0.8263650 0.1736350176 0 0.0915762188 0.0915762188
## 250 18.836414 0 0.8256480 0.1743520361 0 0.0919096524 0.0919096524
## 251 18.866529 0 0.8249310 0.1750689614 0 0.0922426810 0.0922426810
## 252 18.940453 0 0.8242141 0.1757858856 0 0.0925753473 0.0925753473
## 253 18.981520 0 0.8227805 0.1772194953 0 0.0932391239 0.0932391239
## 254 19.008898 0 0.8220638 0.1779362341 0 0.0935706165 0.0935706165
## 255 19.011637 0 0.8213473 0.1786527326 0 0.0939016357 0.0939016357
## 256 19.025326 0 0.8206312 0.1793688257 0 0.0942321052 0.0942321052
## 257 19.033539 0 0.8199153 0.1800847486 0 0.0945621336 0.0945621336
## 258 19.044491 0 0.8191987 0.1808012713 0 0.0948920732 0.0948920732
## 259 19.047228 0 0.8184825 0.1815175136 0 0.0952215205 0.0952215205
## 260 19.082821 0 0.8170487 0.1829513069 0 0.0958795955 0.0958795955
## 261 19.107460 0 0.8163302 0.1836697705 0 0.0962089995 0.0962089995
## 262 19.110199 0 0.8156122 0.1843878359 0 0.0965378545 0.0965378545
## 263 19.132101 0 0.8148939 0.1851060716 0 0.0968664081 0.0968664081
## 264 19.134840 0 0.8141756 0.1858243937 0 0.0971945903 0.0971945903
## 265 19.159479 0 0.8134558 0.1865441940 0 0.0975230327 0.0975230327
## 266 19.173168 0 0.8127361 0.1872638550 0 0.0978510420 0.0978510420
## 267 19.184120 0 0.8120166 0.1879833676 0 0.0981786135 0.0981786135
## 268 19.192333 0 0.8112974 0.1887026480 0 0.0985057095 0.0985057095
## 269 19.219713 0 0.8105785 0.1894215119 0 0.0988322466 0.0988322466
## 270 19.238878 0 0.8098589 0.1901410673 0 0.0991586578 0.0991586578
## 271 19.263517 0 0.8077000 0.1923000473 0 0.1001347010 0.1001347010
## 272 19.268993 0 0.8069796 0.1930204164 0 0.1004601295 0.1004601295
## 273 19.299110 0 0.8062590 0.1937410148 0 0.1007855645 0.1007855645
## 274 19.378508 0 0.8055383 0.1944617048 0 0.1011106662 0.1011106662
## 275 19.397673 0 0.8048178 0.1951821538 0 0.1014352850 0.1014352850
## 276 19.457905 0 0.8040976 0.1959024022 0 0.1017594393 0.1017594393
## 277 19.471594 0 0.8033758 0.1966241744 0 0.1020839430 0.1020839430
## 278 19.488022 0 0.8026516 0.1973484356 0 0.1024091805 0.1024091805
## 279 19.509924 0 0.8012037 0.1987963230 0 0.1030578568 0.1030578568
## 280 19.515400 0 0.8004793 0.1995207480 0 0.1033820300 0.1033820300
## 281 19.523615 0 0.7997552 0.2002448326 0 0.1037056709 0.1037056709
## 282 19.559206 0 0.7990316 0.2009684356 0 0.1040287166 0.1040287166
## 283 19.564682 0 0.7983077 0.2016923198 0 0.1043514883 0.1043514883
## 284 19.627653 0 0.7975843 0.2024156645 0 0.1046736392 0.1046736392
## 285 19.638603 0 0.7968609 0.2031391193 0 0.1049954754 0.1049954754
## 286 19.682409 0 0.7961377 0.2038622767 0 0.1053168039 0.1053168039
## 287 19.704311 0 0.7954145 0.2045855425 0 0.1056377986 0.1056377986
## 288 19.750856 0 0.7946906 0.2053093981 0 0.1059586230 0.1059586230
## 289 19.791924 0 0.7939670 0.2060330453 0 0.1062790595 0.1062790595
## 290 19.811089 0 0.7932439 0.2067561498 0 0.1065988736 0.1065988736
## 291 19.813826 0 0.7925205 0.2074794604 0 0.1069183955 0.1069183955
## 292 19.885010 0 0.7917975 0.2082024721 0 0.1072374025 0.1072374025
## 293 19.887749 0 0.7910730 0.2089269595 0 0.1075566715 0.1075566715
## 294 19.893225 0 0.7903485 0.2096514593 0 0.1078755498 0.1078755498
## 295 19.900000 0 0.7903485 0.2096514593 0 0.1078755498 0.1078755498
## 296 19.909651 0 0.7896310 0.2103690103 0 0.1081909399 0.1081909399
## 297 19.964409 0 0.7889130 0.2110869524 0 0.1085061229 0.1085061229
## 298 19.978098 0 0.7881949 0.2118050716 0 0.1088210039 0.1088210039
## 299 20.000000 0 0.7881949 0.2118050716 0 0.1088210039 0.1088210039
## 300 20.008213 0 0.7874458 0.2125541906 0 0.1091470700 0.1091470700
## 301 20.016428 0 0.7866956 0.2133043875 0 0.1094730889 0.1094730889
## 302 20.095825 0 0.7859445 0.2140555429 0 0.1097991046 0.1097991046
## 303 20.109514 0 0.7851932 0.2148068158 0 0.1101247549 0.1101247549
## 304 20.128679 0 0.7836910 0.2163089573 0 0.1107742247 0.1107742247
## 305 20.156057 0 0.7829407 0.2170592674 0 0.1110981954 0.1110981954
## 306 20.164270 0 0.7821900 0.2178099583 0 0.1114219117 0.1114219117
## 307 20.243670 0 0.7814395 0.2185605435 0 0.1117451663 0.1117451663
## 308 20.271048 0 0.7806865 0.2193134518 0 0.1120692173 0.1120692173
## 309 20.281998 0 0.7799334 0.2200666132 0 0.1123929552 0.1123929552
## 310 20.298426 0 0.7791789 0.2208211358 0 0.1127168499 0.1127168499
## 311 20.312115 0 0.7784240 0.2215759728 0 0.1130404549 0.1130404549
## 312 20.342232 0 0.7776688 0.2223312230 0 0.1133638093 0.1133638093
## 313 20.364134 0 0.7769140 0.2230859943 0 0.1136865361 0.1136865361
## 314 20.383299 0 0.7761593 0.2238406829 0 0.1140088035 0.1140088035
## 315 20.391512 0 0.7754049 0.2245951331 0 0.1143305454 0.1143305454
## 316 20.394251 0 0.7746505 0.2253495303 0 0.1146518397 0.1146518397
## 317 20.396988 0 0.7738964 0.2261036086 0 0.1149725745 0.1149725745
## 318 20.427105 0 0.7731425 0.2268574656 0 0.1152927909 0.1152927909
## 319 20.449007 0 0.7723878 0.2276122017 0 0.1156129502 0.1156129502
## 320 20.465435 0 0.7716335 0.2283664562 0 0.1159324806 0.1159324806
## 321 20.514715 0 0.7708781 0.2291219143 0 0.1162520117 0.1162520117
## 322 20.561260 0 0.7701232 0.2298768160 0 0.1165708816 0.1165708816
## 323 20.632444 0 0.7693683 0.2306317058 0 0.1168893182 0.1168893182
## 324 20.646133 0 0.7686128 0.2313872434 0 0.1172075526 0.1172075526
## 325 20.711842 0 0.7678575 0.2321425148 0 0.1175252463 0.1175252463
## 326 20.714579 0 0.7671017 0.2328983085 0 0.1178427256 0.1178427256
## 327 20.741957 0 0.7663463 0.2336536505 0 0.1181595862 0.1181595862
## 328 20.744696 0 0.7655916 0.2344083551 0 0.1184757516 0.1184757516
## 329 20.758385 0 0.7648373 0.2351626708 0 0.1187913252 0.1187913252
## 330 20.780287 0 0.7640829 0.2359171465 0 0.1191065341 0.1191065341
## 331 20.793976 0 0.7633283 0.2366716996 0 0.1194213432 0.1194213432
## 332 20.807667 0 0.7625733 0.2374267487 0 0.1197359245 0.1197359245
## 333 20.813141 0 0.7618183 0.2381817146 0 0.1200500385 0.1200500385
## 334 20.824093 0 0.7610633 0.2389367114 0 0.1203637317 0.1203637317
## 335 20.862423 0 0.7603081 0.2396919094 0 0.1206770737 0.1206770737
## 336 20.870636 0 0.7595506 0.2404494405 0 0.1209909129 0.1209909129
## 337 20.911705 0 0.7587915 0.2412085186 0 0.1213049457 0.1213049457
## 338 20.917179 0 0.7580324 0.2419676415 0 0.1216185570 0.1216185570
## 339 20.944559 0 0.7572739 0.2427261334 0 0.1219314705 0.1219314705
## 340 20.960985 0 0.7565151 0.2434849043 0 0.1222440573 0.1222440573
## 341 20.971937 0 0.7557562 0.2442438000 0 0.1225562539 0.1225562539
## 342 21.021219 0 0.7549976 0.2450023692 0 0.1228678764 0.1228678764
## 343 21.034908 0 0.7542390 0.2457609844 0 0.1231791540 0.1231791540
## 344 21.100616 0 0.7534810 0.2465190170 0 0.1234897536 0.1234897536
## 345 21.119781 0 0.7527234 0.2472766142 0 0.1237997355 0.1237997355
## 346 21.188227 0 0.7519663 0.2480336854 0 0.1241090632 0.1241090632
## 347 21.245722 0 0.7512091 0.2487908859 0 0.1244180016 0.1244180016
## 348 21.251198 0 0.7504497 0.2495503300 0 0.1247274009 0.1247274009
## 349 21.256674 0 0.7496892 0.2503107849 0 0.1250367608 0.1250367608
## 350 21.278576 0 0.7489282 0.2510718102 0 0.1253459024 0.1253459024
## 351 21.327858 0 0.7481675 0.2518324505 0 0.1256544419 0.1256544419
## 352 21.333334 0 0.7474067 0.2525932572 0 0.1259625999 0.1259625999
## 353 21.336071 0 0.7466466 0.2533534336 0 0.1262700574 0.1262700574
## 354 21.377138 0 0.7458868 0.2541131753 0 0.1265768932 0.1265768932
## 355 21.409992 0 0.7451274 0.2548726399 0 0.1268831704 0.1268831704
## 356 21.415468 0 0.7443677 0.2556323021 0 0.1271890739 0.1271890739
## 357 21.475702 0 0.7436084 0.2563915658 0 0.1274943701 0.1274943701
## 358 21.481176 0 0.7428495 0.2571504845 0 0.1277991947 0.1277991947
## 359 21.483915 0 0.7413323 0.2586676641 0 0.1284067949 0.1284067949
## 360 21.494867 0 0.7405720 0.2594280182 0 0.1287109204 0.1287109204
## 361 21.500341 0 0.7398123 0.2601877057 0 0.1290143303 0.1290143303
## 362 21.549623 0 0.7390527 0.2609472971 0 0.1293172502 0.1293172502
## 363 21.555099 0 0.7382930 0.2617070174 0 0.1296197685 0.1296197685
## 364 21.609856 0 0.7375320 0.2624680114 0 0.1299223336 0.1299223336
## 365 21.623545 0 0.7367713 0.2632286723 0 0.1302243131 0.1302243131
## 366 21.661875 0 0.7360114 0.2639886249 0 0.1305255600 0.1305255600
## 367 21.683779 0 0.7352501 0.2647498983 0 0.1308268682 0.1308268682
## 368 21.686516 0 0.7344876 0.2655123915 0 0.1311281955 0.1311281955
## 369 21.702944 0 0.7337253 0.2662747118 0 0.1314289967 0.1314289967
## 370 21.752224 0 0.7329625 0.2670375333 0 0.1317294558 0.1317294558
## 371 21.842573 0 0.7321996 0.2678003554 0 0.1320294554 0.1320294554
## 372 21.867214 0 0.7314370 0.2685629923 0 0.1323289226 0.1323289226
## 373 21.875427 0 0.7306746 0.2693254038 0 0.1326278419 0.1326278419
## 374 21.889116 0 0.7299111 0.2700888616 0 0.1329267044 0.1329267044
## 375 21.984941 0 0.7283839 0.2716161227 0 0.1335227194 0.1335227194
## 376 22.012320 0 0.7276206 0.2723794393 0 0.1338200847 0.1338200847
## 377 22.050650 0 0.7268569 0.2731430929 0 0.1341171967 0.1341171967
## 378 22.075291 0 0.7260922 0.2739078016 0 0.1344142480 0.1344142480
## 379 22.088980 0 0.7253277 0.2746722671 0 0.1347107396 0.1347107396
## 380 22.119097 0 0.7245591 0.2754408779 0 0.1350092620 0.1350092620
## 381 22.132786 0 0.7237907 0.2762093123 0 0.1353072441 0.1353072441
## 382 22.154688 0 0.7230215 0.2769785171 0 0.1356050042 0.1356050042
## 383 22.195757 0 0.7222508 0.2777491553 0 0.1359028372 0.1359028372
## 384 22.206707 0 0.7214791 0.2785208754 0 0.1362006780 0.1362006780
## 385 22.220396 0 0.7207041 0.2792959265 0 0.1364990966 0.1364990966
## 386 22.225872 0 0.7199274 0.2800726159 0 0.1367982989 0.1367982989
## 387 22.228611 0 0.7191513 0.2808486772 0 0.1370967759 0.1370967759
## 388 22.234087 0 0.7183751 0.2816248790 0 0.1373948423 0.1373948423
## 389 22.242300 0 0.7175993 0.2824007115 0 0.1376922822 0.1376922822
## 390 22.247776 0 0.7168238 0.2831761988 0 0.1379890987 0.1379890987
## 391 22.316221 0 0.7160466 0.2839533627 0 0.1382858718 0.1382858718
## 392 22.373716 0 0.7152701 0.2847299359 0 0.1385819328 0.1385819328
## 393 22.398357 0 0.7144937 0.2855062720 0 0.1388774156 0.1388774156
## 394 22.420259 0 0.7137175 0.2862825002 0 0.1391723686 0.1391723686
## 395 22.433949 0 0.7129412 0.2870587712 0 0.1394668159 0.1394668159
## 396 22.480494 0 0.7121652 0.2878347771 0 0.1397606738 0.1397606738
## 397 22.527037 0 0.7113875 0.2886125256 0 0.1400551805 0.1400551805
## 398 22.652977 0 0.7106096 0.2893904167 0 0.1403492693 0.1403492693
## 399 22.732374 0 0.7098312 0.2901688199 0 0.1406430559 0.1406430559
## 400 22.737850 0 0.7090530 0.2909469796 0 0.1409362405 0.1409362405
## 401 22.754278 0 0.7082752 0.2917247725 0 0.1412288007 0.1412288007
## 402 22.863792 0 0.7074974 0.2925026188 0 0.1415208416 0.1415208416
## 403 22.882957 0 0.7067201 0.2932799468 0 0.1418121940 0.1418121940
## 404 22.907597 0 0.7059427 0.2940572755 0 0.1421030511 0.1421030511
## 405 22.910336 0 0.7051646 0.2948353782 0 0.1423936985 0.1423936985
## 406 22.967831 0 0.7043854 0.2956146102 0 0.1426842652 0.1426842652
## 407 22.976044 0 0.7036067 0.2963932794 0 0.1429741244 0.1429741244
## 408 23.025326 0 0.7028258 0.2971742039 0 0.1432641137 0.1432641137
## 409 23.039015 0 0.7020439 0.2979561081 0 0.1435539589 0.1435539589
## 410 23.074606 0 0.7012589 0.2987410848 0 0.1438446376 0.1438446376
## 411 23.091034 0 0.7004742 0.2995257761 0 0.1441347021 0.1441347021
## 412 23.115675 0 0.6996893 0.3003107335 0 0.1444243543 0.1444243543
## 413 23.118412 0 0.6989038 0.3010961837 0 0.1447136350 0.1447136350
## 414 23.121149 0 0.6981189 0.3018811001 0 0.1450022093 0.1450022093
## 415 23.156742 0 0.6973346 0.3026653654 0 0.1452900349 0.1452900349
## 416 23.203285 0 0.6965495 0.3034505488 0 0.1455776822 0.1455776822
## 417 23.214237 0 0.6957640 0.3042360167 0 0.1458649191 0.1458649191
## 418 23.219713 0 0.6941918 0.3058081619 0 0.1464376903 0.1464376903
## 419 23.263517 0 0.6934049 0.3065951007 0 0.1467242712 0.1467242712
## 420 23.274469 0 0.6910455 0.3089544937 0 0.1475788595 0.1475788595
## 421 23.326488 0 0.6902582 0.3097417944 0 0.1478635760 0.1478635760
## 422 23.329227 0 0.6894708 0.3105291561 0 0.1481477938 0.1481477938
## 423 23.331964 0 0.6886833 0.3113166687 0 0.1484315665 0.1484315665
## 424 23.351130 0 0.6878964 0.3121036157 0 0.1487146152 0.1487146152
## 425 23.364819 0 0.6871100 0.3128899789 0 0.1489969338 0.1489969338
## 426 23.367556 0 0.6863243 0.3136756558 0 0.1492784865 0.1492784865
## 427 23.370295 0 0.6855385 0.3144614836 0 0.1495595935 0.1495595935
## 428 23.373032 0 0.6847524 0.3152475931 0 0.1498402779 0.1498402779
## 429 23.389460 0 0.6839664 0.3160336430 0 0.1501204179 0.1501204179
## 430 23.419575 0 0.6831810 0.3168189987 0 0.1503997889 0.1503997889
## 431 23.427790 0 0.6823935 0.3176064615 0 0.1506793129 0.1506793129
## 432 23.449692 0 0.6816054 0.3183946373 0 0.1509585602 0.1509585602
## 433 23.466120 0 0.6808160 0.3191840120 0 0.1512376991 0.1512376991
## 434 23.490759 0 0.6800264 0.3199735522 0 0.1515163754 0.1515163754
## 435 23.493498 0 0.6792370 0.3207630135 0 0.1517944687 0.1517944687
## 436 23.509924 0 0.6784483 0.3215516909 0 0.1520717564 0.1520717564
## 437 23.518139 0 0.6776599 0.3223400545 0 0.1523484099 0.1523484099
## 438 23.561943 0 0.6768716 0.3231284350 0 0.1526245212 0.1526245212
## 439 23.600273 0 0.6760834 0.3239166398 0 0.1529000391 0.1529000391
## 440 23.704311 0 0.6752955 0.3247045421 0 0.1531749194 0.1531749194
## 441 23.720739 0 0.6745054 0.3254946182 0 0.1534498970 0.1534498970
## 442 23.753593 0 0.6737154 0.3262845643 0 0.1537242931 0.1537242931
## 443 23.767282 0 0.6729251 0.3270748841 0 0.1539983051 0.1539983051
## 444 23.770021 0 0.6721356 0.3278643825 0 0.1542714970 0.1542714970
## 445 23.772758 0 0.6713462 0.3286537769 0 0.1545441155 0.1545441155
## 446 23.791924 0 0.6705553 0.3294447275 0 0.1548165957 0.1548165957
## 447 23.811089 0 0.6697631 0.3302369053 0 0.1550889522 0.1550889522
## 448 23.830254 0 0.6689711 0.3310289037 0 0.1553607043 0.1553607043
## 449 23.832991 0 0.6681769 0.3318230907 0 0.1556325860 0.1556325860
## 450 23.838467 0 0.6673823 0.3326177014 0 0.1559040711 0.1559040711
## 451 23.843943 0 0.6665880 0.3334120411 0 0.1561749158 0.1561749158
## 452 23.874060 0 0.6657912 0.3342088317 0 0.1564457715 0.1564457715
## 453 23.904175 0 0.6649944 0.3350056258 0 0.1567162843 0.1567162843
## 454 23.912388 0 0.6641978 0.3358021505 0 0.1569861528 0.1569861528
## 455 23.917864 0 0.6634017 0.3365982951 0 0.1572553401 0.1572553401
## 456 23.945244 0 0.6618095 0.3381905051 0 0.1577914787 0.1577914787
## 457 23.975359 0 0.6610135 0.3389864633 0 0.1580589469 0.1580589469
## 458 23.980835 0 0.6602183 0.3397816904 0 0.1583256164 0.1583256164
## 459 24.008213 0 0.6594231 0.3405769424 0 0.1585917385 0.1585917385
## 460 24.041067 0 0.6586276 0.3413723757 0 0.1588573420 0.1588573420
## 461 24.071184 0 0.6578326 0.3421674113 0 0.1591223652 0.1591223652
## 462 24.076660 0 0.6570376 0.3429624476 0 0.1593868314 0.1593868314
## 463 24.109514 0 0.6562429 0.3437571453 0 0.1596506283 0.1596506283
## 464 24.117727 0 0.6554484 0.3445516180 0 0.1599137931 0.1599137931
## 465 24.131416 0 0.6546536 0.3453463935 0 0.1601765189 0.1601765189
## 466 24.142368 0 0.6530648 0.3469352026 0 0.1606994978 0.1606994978
## 467 24.180698 0 0.6522694 0.3477306185 0 0.1609606476 0.1609606476
## 468 24.188911 0 0.6514744 0.3485256413 0 0.1612211075 0.1612211075
## 469 24.202600 0 0.6506769 0.3493230587 0 0.1614817777 0.1614817777
## 470 24.216290 0 0.6498801 0.3501198838 0 0.1617416900 0.1617416900
## 471 24.246407 0 0.6490835 0.3509164729 0 0.1620009597 0.1620009597
## 472 24.257359 0 0.6482847 0.3517153440 0 0.1622603232 0.1622603232
## 473 24.271048 0 0.6474851 0.3525148860 0 0.1625193304 0.1625193304
## 474 24.276524 0 0.6466854 0.3533146404 0 0.1627778326 0.1627778326
## 475 24.287474 0 0.6458863 0.3541136870 0 0.1630355354 0.1630355354
## 476 24.303902 0 0.6450878 0.3549121867 0 0.1632924908 0.1632924908
## 477 24.402464 0 0.6442895 0.3557104903 0 0.1635488109 0.1635488109
## 478 24.418892 0 0.6426922 0.3573078050 0 0.1640593867 0.1640593867
## 479 24.438057 0 0.6418917 0.3581082532 0 0.1643146607 0.1643146607
## 480 24.481861 0 0.6410891 0.3589108857 0 0.1645700419 0.1645700419
## 481 24.503765 0 0.6402844 0.3597156296 0 0.1648255148 0.1648255148
## 482 24.511978 0 0.6394801 0.3605198927 0 0.1650802506 0.1650802506
## 483 24.517454 0 0.6386760 0.3613239543 0 0.1653343367 0.1653343367
## 484 24.522930 0 0.6378722 0.3621277947 0 0.1655877667 0.1655877667
## 485 24.558521 0 0.6370689 0.3629311323 0 0.1658404528 0.1658404528
## 486 24.574949 0 0.6362656 0.3637343571 0 0.1660925164 0.1660925164
## 487 24.596851 0 0.6354633 0.3645367189 0 0.1663437249 0.1663437249
## 488 24.618753 0 0.6346612 0.3653387658 0 0.1665942485 0.1665942485
## 489 24.999315 0 0.6346612 0.3653387658 0 0.1665942485 0.1665942485
## lower1 lower2 lower3 upper1 upper2 upper3
## 1 0 1.0000000 0.000000e+00 0 1 0.000000000
## 2 0 1.0000000 0.000000e+00 0 1 0.000000000
## 3 0 0.9978576 6.821725e-05 0 1 0.006320248
## 4 0 0.9963371 2.190986e-04 0 1 0.007873663
## 5 0 0.9948785 3.969506e-04 0 1 0.009778307
## 6 0 0.9934434 5.870277e-04 0 1 0.011757945
## 7 0 0.9920181 7.840160e-04 0 1 0.013767976
## 8 0 0.9905989 9.853200e-04 0 1 0.015791253
## 9 0 0.9891829 1.189665e-03 0 1 0.017822601
## 10 0 0.9863544 1.604839e-03 0 1 0.021901119
## 11 0 0.9849385 1.814901e-03 0 1 0.023950431
## 12 0 0.9835254 2.025821e-03 0 1 0.025998065
## 13 0 0.9821136 2.237597e-03 0 1 0.028045877
## 14 0 0.9806951 2.451106e-03 0 1 0.030105921
## 15 0 0.9792760 2.665484e-03 0 1 0.032167997
## 16 0 0.9778505 2.881635e-03 0 1 0.034239817
## 17 0 0.9764219 3.099167e-03 0 1 0.036315944
## 18 0 0.9749955 3.317052e-03 0 1 0.038389297
## 19 0 0.9735707 3.535303e-03 0 1 0.040460555
## 20 0 0.9721478 3.753855e-03 0 1 0.042529384
## 21 0 0.9707242 3.973031e-03 0 1 0.044599619
## 22 0 0.9693021 4.192492e-03 0 1 0.046667779
## 23 0 0.9678817 4.412213e-03 0 1 0.048733786
## 24 0 0.9664629 4.632169e-03 0 1 0.050797585
## 25 0 0.9650452 4.852405e-03 0 1 0.052859799
## 26 0 0.9636266 5.073248e-03 0 1 0.054923634
## 27 0 0.9622095 5.294295e-03 0 1 0.056985257
## 28 0 0.9607912 5.515934e-03 0 1 0.059048769
## 29 0 0.9593729 5.738003e-03 0 1 0.061112535
## 30 0 0.9579520 5.960805e-03 0 1 0.063180469
## 31 0 0.9579520 5.960805e-03 0 1 0.063180469
## 32 0 0.9565307 6.184018e-03 0 1 0.065249179
## 33 0 0.9551095 6.407691e-03 0 1 0.067317834
## 34 0 0.9536869 6.631955e-03 0 1 0.069388443
## 35 0 0.9522634 6.856765e-03 0 1 0.071460485
## 36 0 0.9508413 7.081774e-03 0 1 0.073530563
## 37 0 0.9494167 7.307550e-03 0 1 0.075604326
## 38 0 0.9479916 7.533803e-03 0 1 0.077678904
## 39 0 0.9465674 7.760314e-03 0 1 0.079752139
## 40 0 0.9451413 7.987513e-03 0 1 0.081828247
## 41 0 0.9437099 8.215908e-03 0 1 0.083912100
## 42 0 0.9422801 8.444472e-03 0 1 0.085993698
## 43 0 0.9422801 8.444472e-03 0 1 0.085993698
## 44 0 0.9394223 8.904336e-03 0 1 0.090147135
## 45 0 0.9394223 8.904336e-03 0 1 0.090147135
## 46 0 0.9379846 9.135309e-03 0 1 0.092240354
## 47 0 0.9365476 9.366587e-03 0 1 0.094332617
## 48 0 0.9365476 9.366587e-03 0 1 0.094332617
## 49 0 0.9351932 9.586029e-03 0 1 0.096300483
## 50 0 0.9338396 9.805662e-03 0 1 0.098267234
## 51 0 0.9324859 1.002567e-02 0 1 0.100234075
## 52 0 0.9297789 1.046686e-02 0 1 0.104166608
## 53 0 0.9284254 1.068786e-02 0 1 0.106133561
## 54 0 0.9270731 1.090898e-02 0 1 0.108098698
## 55 0 0.9257167 1.113088e-02 0 1 0.110070661
## 56 0 0.9243595 1.135325e-02 0 1 0.112044104
## 57 0 0.9230027 1.157588e-02 0 1 0.114016854
## 58 0 0.9216430 1.179932e-02 0 1 0.115993880
## 59 0 0.9202846 1.202292e-02 0 1 0.117969197
## 60 0 0.9189225 1.224741e-02 0 1 0.119949889
## 61 0 0.9175601 1.247231e-02 0 1 0.121931201
## 62 0 0.9161964 1.269777e-02 0 1 0.123914398
## 63 0 0.9148334 1.292348e-02 0 1 0.125896632
## 64 0 0.9134711 1.314942e-02 0 1 0.127877706
## 65 0 0.9121080 1.337585e-02 0 1 0.129860230
## 66 0 0.9107459 1.360247e-02 0 1 0.131841208
## 67 0 0.9093832 1.382953e-02 0 1 0.133822976
## 68 0 0.9080189 1.405723e-02 0 1 0.135807180
## 69 0 0.9066510 1.428585e-02 0 1 0.137796745
## 70 0 0.9052817 1.451507e-02 0 1 0.139788365
## 71 0 0.9039066 1.474559e-02 0 1 0.141788568
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## 389 0 0.4926679 1.086023e-01 0 1 0.734332341
## 390 0 0.4915362 1.089618e-01 0 1 0.735934656
## 391 0 0.4904037 1.093226e-01 0 1 0.737537181
## 392 0 0.4892735 1.096836e-01 0 1 0.739136246
## 393 0 0.4881450 1.100449e-01 0 1 0.740732577
## 394 0 0.4870181 1.104065e-01 0 1 0.742326438
## 395 0 0.4858925 1.107686e-01 0 1 0.743917972
## 396 0 0.4847687 1.111309e-01 0 1 0.745506718
## 397 0 0.4836431 1.114941e-01 0 1 0.747099214
## 398 0 0.4825187 1.118578e-01 0 1 0.748689846
## 399 0 0.4813949 1.122221e-01 0 1 0.750279251
## 400 0 0.4802729 1.125868e-01 0 1 0.751865813
## 401 0 0.4791528 1.129516e-01 0 1 0.753449405
## 402 0 0.4780340 1.133170e-01 0 1 0.755030617
## 403 0 0.4769174 1.136825e-01 0 1 0.756608519
## 404 0 0.4758022 1.140484e-01 0 1 0.758184159
## 405 0 0.4746873 1.144151e-01 0 1 0.759759089
## 406 0 0.4735722 1.147827e-01 0 1 0.761334012
## 407 0 0.4724592 1.151505e-01 0 1 0.762905531
## 408 0 0.4713447 1.155200e-01 0 1 0.764478272
## 409 0 0.4702302 1.158903e-01 0 1 0.766050675
## 410 0 0.4691125 1.162624e-01 0 1 0.767627971
## 411 0 0.4679966 1.166347e-01 0 1 0.769202385
## 412 0 0.4668817 1.170076e-01 0 1 0.770775017
## 413 0 0.4657676 1.173812e-01 0 1 0.772346107
## 414 0 0.4646557 1.177550e-01 0 1 0.773913823
## 415 0 0.4635461 1.181289e-01 0 1 0.775477936
## 416 0 0.4624366 1.185036e-01 0 1 0.777041549
## 417 0 0.4613282 1.188789e-01 0 1 0.778603406
## 418 0 0.4591146 1.196319e-01 0 1 0.781719549
## 419 0 0.4580078 1.200090e-01 0 1 0.783279128
## 420 0 0.4547001 1.211433e-01 0 1 0.787933414
## 421 0 0.4535984 1.215223e-01 0 1 0.789484776
## 422 0 0.4524981 1.219017e-01 0 1 0.791033919
## 423 0 0.4513989 1.222816e-01 0 1 0.792581131
## 424 0 0.4503020 1.226616e-01 0 1 0.794124901
## 425 0 0.4492073 1.230419e-01 0 1 0.795665198
## 426 0 0.4481149 1.234222e-01 0 1 0.797201827
## 427 0 0.4470238 1.238031e-01 0 1 0.798736530
## 428 0 0.4459336 1.241845e-01 0 1 0.800269444
## 429 0 0.4448450 1.245663e-01 0 1 0.801799908
## 430 0 0.4437587 1.249483e-01 0 1 0.803326697
## 431 0 0.4426710 1.253317e-01 0 1 0.804854888
## 432 0 0.4415838 1.257160e-01 0 1 0.806382105
## 433 0 0.4404963 1.261013e-01 0 1 0.807909273
## 434 0 0.4394100 1.264872e-01 0 1 0.809434452
## 435 0 0.4383253 1.268734e-01 0 1 0.810957002
## 436 0 0.4372431 1.272598e-01 0 1 0.812475695
## 437 0 0.4361628 1.276464e-01 0 1 0.813991466
## 438 0 0.4350838 1.280336e-01 0 1 0.815504837
## 439 0 0.4340065 1.284210e-01 0 1 0.817015518
## 440 0 0.4329311 1.288088e-01 0 1 0.818523270
## 441 0 0.4318542 1.291983e-01 0 1 0.820032199
## 442 0 0.4307790 1.295881e-01 0 1 0.821538515
## 443 0 0.4297046 1.299786e-01 0 1 0.823043293
## 444 0 0.4286328 1.303691e-01 0 1 0.824544152
## 445 0 0.4275626 1.307600e-01 0 1 0.826042451
## 446 0 0.4264918 1.311523e-01 0 1 0.827540663
## 447 0 0.4254208 1.315456e-01 0 1 0.829038800
## 448 0 0.4243515 1.319393e-01 0 1 0.830534219
## 449 0 0.4232808 1.323347e-01 0 1 0.832031008
## 450 0 0.4222109 1.327307e-01 0 1 0.833526230
## 451 0 0.4211428 1.331271e-01 0 1 0.835018548
## 452 0 0.4200733 1.335254e-01 0 1 0.836511722
## 453 0 0.4190049 1.339240e-01 0 1 0.838003515
## 454 0 0.4179383 1.343229e-01 0 1 0.839492395
## 455 0 0.4168736 1.347222e-01 0 1 0.840978159
## 456 0 0.4147495 1.355225e-01 0 1 0.843939635
## 457 0 0.4136894 1.359231e-01 0 1 0.845417859
## 458 0 0.4126318 1.363238e-01 0 1 0.846892328
## 459 0 0.4115755 1.367250e-01 0 1 0.848364433
## 460 0 0.4105205 1.371268e-01 0 1 0.849834352
## 461 0 0.4094673 1.375288e-01 0 1 0.851301660
## 462 0 0.4084156 1.379313e-01 0 1 0.852766560
## 463 0 0.4073657 1.383342e-01 0 1 0.854228431
## 464 0 0.4063176 1.387374e-01 0 1 0.855687484
## 465 0 0.4052704 1.391412e-01 0 1 0.857144778
## 466 0 0.4031822 1.399504e-01 0 1 0.860048108
## 467 0 0.4021386 1.403561e-01 0 1 0.861498824
## 468 0 0.4010971 1.407621e-01 0 1 0.862946415
## 469 0 0.4000538 1.411698e-01 0 1 0.864395897
## 470 0 0.3990128 1.415777e-01 0 1 0.865841884
## 471 0 0.3979735 1.419860e-01 0 1 0.867285021
## 472 0 0.3969328 1.423961e-01 0 1 0.868729469
## 473 0 0.3958927 1.428070e-01 0 1 0.870172675
## 474 0 0.3948537 1.432185e-01 0 1 0.871613814
## 475 0 0.3938171 1.436302e-01 0 1 0.873051243
## 476 0 0.3927827 1.440421e-01 0 1 0.874485256
## 477 0 0.3917499 1.444544e-01 0 1 0.875916479
## 478 0 0.3896886 1.452813e-01 0 1 0.878770125
## 479 0 0.3886574 1.456962e-01 0 1 0.880197818
## 480 0 0.3876249 1.461129e-01 0 1 0.881626901
## 481 0 0.3865912 1.465311e-01 0 1 0.883057287
## 482 0 0.3855595 1.469496e-01 0 1 0.884484345
## 483 0 0.3845296 1.473685e-01 0 1 0.885908566
## 484 0 0.3835014 1.477878e-01 0 1 0.887329918
## 485 0 0.3824753 1.482074e-01 0 1 0.888747909
## 486 0 0.3814508 1.486274e-01 0 1 0.890163225
## 487 0 0.3804288 1.490476e-01 0 1 0.891574559
## 488 0 0.3794088 1.494681e-01 0 1 0.892982871
## 489 0 0.3794088 1.494681e-01 0 1 0.892982871
summary(pt,from=1)
##
## Prediction from state 1 (head and tail):
## time pstate1 pstate2 pstate3 se1 se2
## 1 0.000000 1.0000000 0.000000000 0.0000000000 0.000000000 0.000000000
## 2 5.000000 0.9943917 0.005608302 0.0000000000 0.002058073 0.002058073
## 3 5.117043 0.9942517 0.005604619 0.0001436812 0.002062983 0.002056726
## 4 5.221081 0.9941116 0.005600936 0.0002874771 0.002068776 0.002055380
## 5 5.303217 0.9939714 0.005597252 0.0004313248 0.002075442 0.002054038
## 6 5.322382 0.9938311 0.005593567 0.0005753190 0.002082979 0.002052697
## se3 lower1 lower2 lower3 upper1 upper2
## 1 0.0000000000 1.0000000 0.000000000 0.000000e+00 1.0000000 0.00000000
## 2 0.0000000000 0.9903661 0.002731913 0.000000e+00 0.9984336 0.01151320
## 3 0.0001472089 0.9902165 0.002730115 1.928856e-05 0.9983033 0.01150565
## 4 0.0002169239 0.9900651 0.002728314 6.550963e-05 0.9981746 0.01149812
## 5 0.0002759379 0.9899119 0.002726512 1.230984e-04 0.9980475 0.01149059
## 6 0.0003300938 0.9897569 0.002724706 1.868638e-04 0.9979221 0.01148307
## upper3
## 1 0.000000000
## 2 0.000000000
## 3 0.001070287
## 4 0.001261541
## 5 0.001511320
## 6 0.001771300
##
## ...
## time pstate1 pstate2 pstate3 se1 se2 se3
## 484 24.52293 0.8602630 0.03744368 0.1022934 0.02998383 0.01555091 0.02710675
## 485 24.55852 0.8600307 0.03739653 0.1025728 0.03003532 0.01554827 0.02717286
## 486 24.57495 0.8597982 0.03734938 0.1028525 0.03008687 0.01554563 0.02723899
## 487 24.59685 0.8595657 0.03730228 0.1031320 0.03013845 0.01554300 0.02730507
## 488 24.61875 0.8593331 0.03725520 0.1034117 0.03019009 0.01554037 0.02737115
## 489 24.99931 0.8593331 0.03725520 0.1034117 0.03019009 0.01554037 0.02737115
## lower1 lower2 lower3 upper1 upper2 upper3
## 484 0.8034581 0.01659060 0.06085374 0.9210840 0.08450747 0.1719522
## 485 0.8031321 0.01655500 0.06102925 0.9209603 0.08447600 0.1723958
## 486 0.8028057 0.01651942 0.06120492 0.9208366 0.08444463 0.1728395
## 487 0.8024794 0.01648388 0.06138057 0.9207129 0.08441339 0.1732831
## 488 0.8021528 0.01644836 0.06155634 0.9205893 0.08438226 0.1737268
## 489 0.8021528 0.01644836 0.06155634 0.9205893 0.08438226 0.1737268
tmat2 <- transMat(x = list(c(2,4), c(3), c(),c()))
tmat2
## to
## from State 1 State 2 State 3 State 4
## State 1 NA 1 NA 2
## State 2 NA NA 3 NA
## State 3 NA NA NA NA
## State 4 NA NA NA NA
msf2$trans<-tmat2
pt<-probtrans(msf2, predt = 0)
summary(pt, from = 1)
##
## Prediction from state 1 (head and tail):
## time pstate1 pstate2 pstate3 pstate4 se1
## 1 0.000000 1.0000000 0.000000000 0.000000e+00 0.0000000000 0.000000000
## 2 5.000000 0.9943917 0.005608302 0.000000e+00 0.0000000000 0.002058073
## 3 5.117043 0.9942517 0.005604619 3.682523e-06 0.0001399987 0.002062983
## 4 5.221081 0.9941116 0.005600936 7.366133e-06 0.0002801110 0.002068776
## 5 5.303217 0.9939714 0.005597252 1.104922e-05 0.0004202755 0.002075442
## 6 5.322382 0.9938311 0.005593567 1.473419e-05 0.0005605848 0.002082979
## se2 se3 se4 lower1 lower2 lower3
## 1 0.000000000 0.000000e+00 0.0000000000 1.0000000 0.000000000 0.000000e+00
## 2 0.002058073 0.000000e+00 0.0000000000 0.9903661 0.002731913 0.000000e+00
## 3 0.002056726 4.463998e-06 0.0001463870 0.9902165 0.002730115 3.422229e-07
## 4 0.002055380 7.253188e-06 0.0002157768 0.9900651 0.002728314 1.069275e-06
## 5 0.002054038 9.899952e-06 0.0002745482 0.9899119 0.002726512 1.908385e-06
## 6 0.002052697 1.249624e-05 0.0003285034 0.9897569 0.002724706 2.795197e-06
## lower4 upper1 upper2 upper3 upper4
## 1 0.000000e+00 1.0000000 0.00000000 0.000000e+00 0.000000000
## 2 0.000000e+00 0.9984336 0.01151320 0.000000e+00 0.000000000
## 3 1.803355e-05 0.9983033 0.01150565 3.962616e-05 0.001086843
## 4 6.189099e-05 0.9981746 0.01149812 5.074458e-05 0.001267748
## 5 1.168101e-04 0.9980475 0.01149059 6.397302e-05 0.001512126
## 6 1.777608e-04 0.9979221 0.01148307 7.766767e-05 0.001767855
##
## ...
## time pstate1 pstate2 pstate3 pstate4 se1 se2
## 484 24.52293 0.8602630 0.03744368 0.01379236 0.08850100 0.02998383 0.01555091
## 485 24.55852 0.8600307 0.03739653 0.01383952 0.08873330 0.03003532 0.01554827
## 486 24.57495 0.8597982 0.03734938 0.01388667 0.08896580 0.03008687 0.01554563
## 487 24.59685 0.8595657 0.03730228 0.01393377 0.08919827 0.03013845 0.01554300
## 488 24.61875 0.8593331 0.03725520 0.01398085 0.08943089 0.03019009 0.01554037
## 489 24.99931 0.8593331 0.03725520 0.01398085 0.08943089 0.03019009 0.01554037
## se3 se4 lower1 lower2 lower3 lower4
## 484 0.009541280 0.02565829 0.8034581 0.01659060 0.003554632 0.05013800
## 485 0.009565555 0.02572118 0.8031321 0.01655500 0.003571004 0.05027456
## 486 0.009589778 0.02578411 0.8028057 0.01651942 0.003587403 0.05041125
## 487 0.009613926 0.02584701 0.8024794 0.01648388 0.003603815 0.05054794
## 488 0.009638016 0.02590994 0.8021528 0.01644836 0.003620250 0.05068474
## 489 0.009638016 0.02590994 0.8021528 0.01644836 0.003620250 0.05068474
## upper1 upper2 upper3 upper4
## 484 0.9210840 0.08450747 0.05351590 0.1562174
## 485 0.9209603 0.08447600 0.05363543 0.1566120
## 486 0.9208366 0.08444463 0.05375465 0.1570069
## 487 0.9207129 0.08441339 0.05387345 0.1574017
## 488 0.9205893 0.08438226 0.05399190 0.1577967
## 489 0.9205893 0.08438226 0.05399190 0.1577967
##Plot stacked plot
plot(pt, ord = c(2, 3, 4, 1), lwd = 2, xlab = "Time since entry (years)",
ylab = "Prediction probabilities", cex = 0.75, legend = c("Alive w/o IADl decline",
"Alive w/ IADL decline", "Death after IADl decline",
"Death w/o IADL decline"))
pt<-probtrans(msf2,predt=0.5)
## Warning in max(stackvarhaz$time[stackvarhaz$time <= predt]): no non-missing
## arguments to max; returning -Inf
summary(pt,from=1)
##
## Prediction from state 1 (head and tail):
## time pstate1 pstate2 pstate3 pstate4 se1
## 1 0.500000 1.0000000 0.000000000 0.000000e+00 0.0000000000 0.000000000
## 2 5.000000 0.9943917 0.005608302 0.000000e+00 0.0000000000 0.002058073
## 3 5.117043 0.9942517 0.005604619 3.682523e-06 0.0001399987 0.002062983
## 4 5.221081 0.9941116 0.005600936 7.366133e-06 0.0002801110 0.002068776
## 5 5.303217 0.9939714 0.005597252 1.104922e-05 0.0004202755 0.002075442
## 6 5.322382 0.9938311 0.005593567 1.473419e-05 0.0005605848 0.002082979
## se2 se3 se4 lower1 lower2 lower3
## 1 0.000000000 0.000000e+00 0.0000000000 1.0000000 0.000000000 0.000000e+00
## 2 0.002058073 0.000000e+00 0.0000000000 0.9903661 0.002731913 0.000000e+00
## 3 0.002056726 4.463998e-06 0.0001463870 0.9902165 0.002730115 3.422229e-07
## 4 0.002055380 7.253188e-06 0.0002157768 0.9900651 0.002728314 1.069275e-06
## 5 0.002054038 9.899952e-06 0.0002745482 0.9899119 0.002726512 1.908385e-06
## 6 0.002052697 1.249624e-05 0.0003285034 0.9897569 0.002724706 2.795197e-06
## lower4 upper1 upper2 upper3 upper4
## 1 0.000000e+00 1.0000000 0.00000000 0.000000e+00 0.000000000
## 2 0.000000e+00 0.9984336 0.01151320 0.000000e+00 0.000000000
## 3 1.803355e-05 0.9983033 0.01150565 3.962616e-05 0.001086843
## 4 6.189099e-05 0.9981746 0.01149812 5.074458e-05 0.001267748
## 5 1.168101e-04 0.9980475 0.01149059 6.397302e-05 0.001512126
## 6 1.777608e-04 0.9979221 0.01148307 7.766767e-05 0.001767855
##
## ...
## time pstate1 pstate2 pstate3 pstate4 se1 se2
## 484 24.52293 0.8602630 0.03744368 0.01379236 0.08850100 0.02998383 0.01555091
## 485 24.55852 0.8600307 0.03739653 0.01383952 0.08873330 0.03003532 0.01554827
## 486 24.57495 0.8597982 0.03734938 0.01388667 0.08896580 0.03008687 0.01554563
## 487 24.59685 0.8595657 0.03730228 0.01393377 0.08919827 0.03013845 0.01554300
## 488 24.61875 0.8593331 0.03725520 0.01398085 0.08943089 0.03019009 0.01554037
## 489 24.99931 0.8593331 0.03725520 0.01398085 0.08943089 0.03019009 0.01554037
## se3 se4 lower1 lower2 lower3 lower4
## 484 0.009541280 0.02565829 0.8034581 0.01659060 0.003554632 0.05013800
## 485 0.009565555 0.02572118 0.8031321 0.01655500 0.003571004 0.05027456
## 486 0.009589778 0.02578411 0.8028057 0.01651942 0.003587403 0.05041125
## 487 0.009613926 0.02584701 0.8024794 0.01648388 0.003603815 0.05054794
## 488 0.009638016 0.02590994 0.8021528 0.01644836 0.003620250 0.05068474
## 489 0.009638016 0.02590994 0.8021528 0.01644836 0.003620250 0.05068474
## upper1 upper2 upper3 upper4
## 484 0.9210840 0.08450747 0.05351590 0.1562174
## 485 0.9209603 0.08447600 0.05363543 0.1566120
## 486 0.9208366 0.08444463 0.05375465 0.1570069
## 487 0.9207129 0.08441339 0.05387345 0.1574017
## 488 0.9205893 0.08438226 0.05399190 0.1577967
## 489 0.9205893 0.08438226 0.05399190 0.1577967
plot(pt, ord = c(2, 3, 4, 1), lwd = 2, xlab = "Time since entry (years)",
ylab = "Prediction probabilities", cex = 0.75, legend = c("Alive w/o IADl decline",
"Alive w/ IADL decline", "Death after IADl decline",
"Death w/o IADL decline"))
##Age groups
msf2$trans <-tmat
msf.4554 <-msf2 # copy msfit result for reference (young) patient
newd <-newd[,1:11] # use the basic covariates of the reference patient
newd2 <-newd
newd2$agegrp <-1
newd2$agegrp <-factor(newd2$agegrp,levels = 0:2,labels = levels(factor(crisk$agegrp)))
attr(newd2, "trans") <- tmat
class(newd2) <- c("msdata","data.frame")
newd2 <- expand.covs(newd2,covs,longnames=FALSE)
newd2$strata=c(1,2,2)
newd2$pr <- c(0,0,1)
msf.5559 <- msfit(c6, newdata=newd2, trans=tmat)
newd3 <- newd
newd3$agegrp <- 2
newd3$agegrp <- factor(newd3$agegrp,levels = 0:2,labels = levels(factor(crisk$agegrp)))
attr(newd3, "trans") <- tmat
class(newd3) <- c("msdata","data.frame")
newd3 <- expand.covs(newd3,covs,longnames=FALSE)
newd3$strata=c(1,2,2)
newd3$pr <- c(0,0,1)
msf.60 <- msfit(c6, newdata=newd3, trans=tmat)
pt.4554 <- probtrans(msf.4554,predt=0) # 45-54
pt.45541 <- pt.4554[[1]];pt.45542 <- pt.4554[[2]]
pt.5559 <- probtrans(msf.5559,predt=0) # patient 55-59
pt.55591 <- pt.5559[[1]];pt.55592 <- pt.5559[[2]]
pt.60 <- probtrans(msf.60,predt=0) # patient > 60
pt.601 <- pt.60[[1]];pt.602 <- pt.60[[2]]
##Survival probility for age groups
plot(pt.45541$time, 1 - pt.45541$pstate3, ylim = c(0.1, 1), type = "s",lwd = 2, col = "red", xlab = "Time since entry (years)", ylab = "Survival probability",xlim=c(0,25))
lines(pt.55591$time,1 - pt.55591$pstate3, type = "s", lwd = 2,col = "blue")
lines(pt.601$time,1 - pt.601$pstate3, type = "s", lwd = 2, col = "green")
lines(pt.45542$time,1 - pt.45542$pstate3, type = "s", lwd = 2, col = "red",lty = 2)
lines(pt.55592$time,1 - pt.55592$pstate3, type = "s", lwd = 2,col = "blue", lty = 2) #ERROR because of ylim set at 0.425
lines(pt.602$time,1 - pt.602$pstate3, type = "s", lwd = 2, col = "green",lty = 2) #ERROR because of ylim set at 0.425
legend(5,0.5, c("No IADL decline", "IADL decline"), lwd = 2, lty = 1:2) #bty="n" NO BORDER AROUND LEGEND
legend(5,0.3, c("45-54", "55-59", ">=60"), lwd = 2, col = c("red","blue", "green"))
##Diabetes groups
msf2$trans <-tmat
msf.norm <-msf2 # copy msfit result for reference (young) patient
newd <-newd[,1:11] # use the basic covariates of the reference patient
newd4<-newd
newd4$diagrp <-1
newd4$diagrp <-factor(newd4$diagrp,levels = 0:2,labels = levels(factor(crisk$diagrp)))
attr(newd4, "trans") <- tmat
class(newd4) <- c("msdata","data.frame")
newd4 <- expand.covs(newd4,covs,longnames=FALSE)
newd4$strata=c(1,2,2)
newd4$pr <- c(0,0,1)
msf.pre <- msfit(c6, newdata=newd4, trans=tmat)
newd5 <- newd
newd5$diagrp <- 2
newd5$diagrp <- factor(newd5$diagrp,levels = 0:2,labels = levels(factor(crisk$diagrp)))
attr(newd5, "trans") <- tmat
class(newd5) <- c("msdata","data.frame")
newd5 <- expand.covs(newd5,covs,longnames=FALSE)
newd5$strata=c(1,2,2)
newd5$pr <- c(0,0,1)
msf.dia <- msfit(c6, newdata=newd5, trans=tmat)
pt.norm <- probtrans(msf.norm,predt=0) # original young (<= 20) patient
pt.norm1 <- pt.norm[[1]]; pt.norm2 <- pt.norm[[2]]
pt.pre <- probtrans(msf.pre,predt=0) # patient 20-40
pt.pre1 <- pt.pre[[1]]; pt.pre2 <- pt.pre[[2]]
pt.dia <- probtrans(msf.dia,predt=0) # patient > 40
pt.dia1 <- pt.dia[[1]]; pt.dia2 <- pt.dia[[2]]
##Survival probility for age groups
plot(pt.norm1$time, 1 - pt.norm1$pstate3, ylim = c(0.4, 1), type = "s",lwd = 2, col = "red", xlab = "Time since entry (years)", ylab = "Survival probability",xlim=c(0,25))
lines(pt.pre1$time, 1 - pt.pre1$pstate3, type = "s", lwd = 2,col = "blue")
lines(pt.dia1$time, 1 - pt.dia1$pstate3, type = "s", lwd = 2, col = "green")
lines(pt.norm2$time, 1 - pt.norm2$pstate3, type = "s", lwd = 2, col = "red",lty = 2)
lines(pt.pre2$time, 1 - pt.pre2$pstate3, type = "s", lwd = 2,col = "blue", lty = 2)
lines(pt.dia2$time, 1 - pt.dia2$pstate3, type = "s", lwd = 2, col = "green",lty = 2)
legend(5,0.8, c("No IADL decline", "IADL decline"), lwd = 2, lty = 1:2) #bty="n" NO BORDER AROUND LEGEND
legend(5,0.7, c("Normal glucose tolerance", "Prediabetes", "Diabetes"), lwd = 2, col = c("red","blue", "green"))
##BMI categories groups
msf2$trans <-tmat
msf.low <-msf2 # copy msfit result for reference (young) patient
newd <-newd[,1:11] # use the basic covariates of the reference patient
newd4<-newd
newd4$bmijp <-1
newd4$bmijp <-factor(newd4$bmijp,levels = 0:2,labels = levels(factor(crisk$bmijp)))
attr(newd4, "trans") <- tmat
class(newd4) <- c("msdata","data.frame")
newd4 <- expand.covs(newd4,covs,longnames=FALSE)
newd4$strata=c(1,2,2)
newd4$pr <- c(0,0,1)
msf.norm <- msfit(c6, newdata=newd4, trans=tmat)
newd5 <- newd
newd5$bmijp <- 2
newd5$bmijp <- factor(newd5$bmijp,levels = 0:2,labels = levels(factor(crisk$bmijp)))
attr(newd5, "trans") <- tmat
class(newd5) <- c("msdata","data.frame")
newd5 <- expand.covs(newd5,covs,longnames=FALSE)
newd5$strata=c(1,2,2)
newd5$pr <- c(0,0,1)
msf.over <- msfit(c6, newdata=newd5, trans=tmat)
pt.low <- probtrans(msf.low,predt=0) # original young (<= 20) patient
pt.low1 <- pt.low[[1]]; pt.low2 <- pt.low[[2]]
pt.norm <- probtrans(msf.norm,predt=0) # patient 20-40
pt.norm1 <- pt.norm[[1]]; pt.norm2 <- pt.norm[[2]]
pt.over <- probtrans(msf.over,predt=0) # patient > 40
pt.over1 <- pt.over[[1]]; pt.over2 <- pt.over[[2]]
##Survival probility for age groups
plot(pt.low1$time, 1 - pt.low1$pstate3, ylim = c(0.4, 1), type = "s",lwd = 2, col = "red", xlab = "Time since entry (years)", ylab = "Survival probability",xlim=c(0,25))
lines(pt.norm1$time, 1 - pt.norm1$pstate3, type = "s", lwd = 2,col = "blue")
lines(pt.over1$time, 1 - pt.over1$pstate3, type = "s", lwd = 2, col = "green")
lines(pt.low2$time, 1 - pt.low2$pstate3, type = "s", lwd = 2, col = "red",lty = 2)
lines(pt.norm2$time, 1 - pt.norm2$pstate3, type = "s", lwd = 2,col = "blue", lty = 2)
lines(pt.over2$time, 1 - pt.over2$pstate3, type = "s", lwd = 2, col = "green",lty = 2)
legend(5, 0.8, c("No IADL decline", "IADL decline"), lwd = 2, lty = 1:2) #bty="n" NO BORDER AROUND LEGEND #xjust = 1 Reverse X
legend(5,0.7, c("Underweight", "Normal weight", "Overweight"), lwd = 2, col = c("red","blue", "green"))
