TALLER 3 MODELOS DE REGRESIÓN’
Ricardo Enciso
2023-08-20
#PUNTO 1 #1. En la base de datos blocks, se dan datos de un experimento donde se pidi ́o a los ni ̃nos que construyeran torres con bloques c ́ubicos y cil ́ındricos como tan alto como pudieron, el n ́umero de bloques utilizados y el tiempo empleado es la variable de inter ́es. #(a) Ajuste el ”mejor” modelo de regresi ́on con el tiempo que tardaron los ni ̃nos como variable de #inter ́es. Muestre las medidas de bondad de ajuste. #(b) Realice una estimaci ́on con Child = A, Number = 10, T rial = 2, Shape = Cube y Age = 5 #(c) Existe datos at ́ıpicos o influyentes?. Muestre los residuales y las medidas de influencia.
remove(list=ls(all=TRUE))
library(GLMsData)
library(MASS)## Warning: package 'MASS' was built under R version 4.2.3library(AER)## Warning: package 'AER' was built under R version 4.2.3## Loading required package: car## Warning: package 'car' was built under R version 4.2.3## Loading required package: carData## Loading required package: lmtest## Loading required package: zoo## Warning: package 'zoo' was built under R version 4.2.3##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Loading required package: sandwich## Warning: package 'sandwich' was built under R version 4.2.3## Loading required package: survivallibrary(ISLR)## Warning: package 'ISLR' was built under R version 4.2.3library(car)
library(carData)
library(betareg)## Warning: package 'betareg' was built under R version 4.2.3library(carData)
data("blocks")
blockstable (blocks$Child)##
## A B C D E F G H I J K L M N O P Q R S T U V W X Y
## 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4table (blocks$Shape)##
## Cube Cylinder
## 50 50blocks$Child1 = ifelse(blocks$Child=="A", 1,0)
blocks$Child2 = ifelse(blocks$Child=="B", 1,0)
blocks$Child3 = ifelse(blocks$Child=="C", 1,0)
blocks$Child4 = ifelse(blocks$Child=="D", 1,0)
blocks$Child5 = ifelse(blocks$Child=="E", 1,0)
blocks$Child6 = ifelse(blocks$Child=="F", 1,0)
blocks$Child7 = ifelse(blocks$Child=="G", 1,0)
blocks$Child8 = ifelse(blocks$Child=="H", 1,0)
blocks$Child9 = ifelse(blocks$Child=="I", 1,0)
blocks$Child10 = ifelse(blocks$Child=="J", 1,0)
blocks$Child11 = ifelse(blocks$Child=="K", 1,0)
blocks$Child12 = ifelse(blocks$Child=="L", 1,0)
blocks$Child13 = ifelse(blocks$Child=="M", 1,0)
blocks$Child14 = ifelse(blocks$Child=="N", 1,0)
blocks$Child15 = ifelse(blocks$Child=="O", 1,0)
blocks$Child16 = ifelse(blocks$Child=="P", 1,0)
blocks$Child17 = ifelse(blocks$Child=="Q", 1,0)
blocks$Child18 = ifelse(blocks$Child=="R", 1,0)
blocks$Child19 = ifelse(blocks$Child=="S", 1,0)
blocks$Child20 = ifelse(blocks$Child=="T", 1,0)
blocks$Child21 = ifelse(blocks$Child=="U", 1,0)
blocks$Child22 = ifelse(blocks$Child=="V", 1,0)
blocks$Child23 = ifelse(blocks$Child=="W", 1,0)
blocks$Child24 = ifelse(blocks$Child=="X", 1,0)blocks$formaCu=ifelse(blocks$Shape== "Cube",1,0)
blocks$formaCy=ifelse(blocks$Shape== "Cylinder",1,0)
blocksblocks=blocks[, -(1)]
blocksblocks=blocks[, -(4)]
blocksmodelo1=glm(Time ~ Number + Trial + Age,data = blocks,family = poisson())## Warning in dpois(y, mu, log = TRUE): non-integer x = 18.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 20.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 39.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 81.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 17.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 52.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 18.800000## Warning in dpois(y, mu, log = TRUE): non-integer x = 20.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 24.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 54.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 52.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 22.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 16.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.800000## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 30.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 17.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 15.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 33.800000## Warning in dpois(y, mu, log = TRUE): non-integer x = 29.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 32.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 20.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 63.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 26.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 34.800000## Warning in dpois(y, mu, log = TRUE): non-integer x = 16.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 82.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 21.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 16.800000## Warning in dpois(y, mu, log = TRUE): non-integer x = 15.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 46.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 60.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 20.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 63.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 17.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.800000## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.400000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 44.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.800000## Warning in dpois(y, mu, log = TRUE): non-integer x = 19.800000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 19.800000## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 19.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 36.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 31.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.200000modelo1##
## Call: glm(formula = Time ~ Number + Trial + Age, family = poisson(),
## data = blocks)
##
## Coefficients:
## (Intercept) Number Trial Age
## 2.75208 0.21297 -0.02116 -0.22235
##
## Degrees of Freedom: 99 Total (i.e. Null); 96 Residual
## Null Deviance: 1608
## Residual Deviance: 910.5 AIC: Infdispersiontest(modelo1,trafo=1)##
## Overdispersion test
##
## data: modelo1
## z = 2.8366, p-value = 0.00228
## alternative hypothesis: true alpha is greater than 0
## sample estimates:
## alpha
## 10.98535#Rechace Ho.exisite evidencia estadística de que existe sobredisperción en el modelo del tiempo que tardan los niños en apilar los bloques por tanto se debe ajustar a un modelo binomial negativo
library(MASS)
modelo1.1=glm.nb(Time ~ Number + Trial + Age,data = blocks)## Warning in dpois(y, mu, log = TRUE): non-integer x = 18.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 20.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 39.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 81.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 17.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 52.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 18.800000## Warning in dpois(y, mu, log = TRUE): non-integer x = 20.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 24.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 54.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 52.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 22.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 16.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.800000## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 30.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 17.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 15.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 33.800000## Warning in dpois(y, mu, log = TRUE): non-integer x = 29.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 32.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 20.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 63.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 6.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 26.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 11.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 34.800000## Warning in dpois(y, mu, log = TRUE): non-integer x = 16.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 82.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 21.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 16.800000## Warning in dpois(y, mu, log = TRUE): non-integer x = 15.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 46.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 60.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 20.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 63.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 17.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 9.800000## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.400000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 14.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 44.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.800000## Warning in dpois(y, mu, log = TRUE): non-integer x = 19.800000
## Warning in dpois(y, mu, log = TRUE): non-integer x = 19.800000## Warning in dpois(y, mu, log = TRUE): non-integer x = 10.600000## Warning in dpois(y, mu, log = TRUE): non-integer x = 19.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 36.200000## Warning in dpois(y, mu, log = TRUE): non-integer x = 31.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 8.400000## Warning in dpois(y, mu, log = TRUE): non-integer x = 7.200000modelo1.1##
## Call: glm.nb(formula = Time ~ Number + Trial + Age, data = blocks,
## init.theta = 4.166060851, link = log)
##
## Coefficients:
## (Intercept) Number Trial Age
## 2.90886 0.22032 0.01421 -0.28781
##
## Degrees of Freedom: 99 Total (i.e. Null); 96 Residual
## Null Deviance: 183.3
## Residual Deviance: 100.4 AIC: 801.8#selección del “mejor modelo”
step(modelo1.1)## Start: AIC=799.84
## Time ~ Number + Trial + Age
##
## Df Deviance AIC
## - Trial 1 100.45 797.85
## <none> 100.43 799.84
## - Age 1 115.05 812.46
## - Number 1 183.24 880.64
##
## Step: AIC=797.85
## Time ~ Number + Age
##
## Df Deviance AIC
## <none> 100.44 797.85
## - Age 1 115.05 810.46
## - Number 1 183.23 878.64##
## Call: glm.nb(formula = Time ~ Number + Age, data = blocks, init.theta = 4.165736153,
## link = log)
##
## Coefficients:
## (Intercept) Number Age
## 2.9278 0.2203 -0.2873
##
## Degrees of Freedom: 99 Total (i.e. Null); 97 Residual
## Null Deviance: 183.3
## Residual Deviance: 100.4 AIC: 799.9modelofinal= glm.nb(formula = Time ~ Number + Age, data = blocks, init.theta = 4.165736153,
link = log)
modelofinal##
## Call: glm.nb(formula = Time ~ Number + Age, data = blocks, init.theta = 4.16573609,
## link = log)
##
## Coefficients:
## (Intercept) Number Age
## 2.9278 0.2203 -0.2873
##
## Degrees of Freedom: 99 Total (i.e. Null); 97 Residual
## Null Deviance: 183.3
## Residual Deviance: 100.4 AIC: 799.9round(exp(modelofinal $coefficients),2)## (Intercept) Number Age
## 18.69 1.25 0.751/0.75## [1] 1.333333#El tiempo que tarde un niño en aplilar los bloques con exito aumenta a razon de 1.25. #Por cada año cumplido por el niño el tiempo que tarda en apilar con exito los bloques disminuye a razón de 1.33.
library(DescTools)## Warning: package 'DescTools' was built under R version 4.2.3##
## Attaching package: 'DescTools'## The following object is masked from 'package:car':
##
## RecodePseudoR2(modelofinal,"Nagelkerke")## Nagelkerke
## 0.4601108summary(modelofinal)##
## Call:
## glm.nb(formula = Time ~ Number + Age, data = blocks, init.theta = 4.16573609,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5888 -0.7803 -0.3923 0.2797 3.8048
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.92779 0.30149 9.711 < 2e-16 ***
## Number 0.22035 0.02487 8.861 < 2e-16 ***
## Age -0.28726 0.07567 -3.796 0.000147 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(4.1657) family taken to be 1)
##
## Null deviance: 183.26 on 99 degrees of freedom
## Residual deviance: 100.44 on 97 degrees of freedom
## AIC: 799.85
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 4.166
## Std. Err.: 0.655
##
## 2 x log-likelihood: -791.8531-pchisq(183.26-100.4,2)## [1] 0#Rchace Ho.Existe evidencia estadística de que el modelo GLM con respuesta binomia negativa se ajusta alos datos #El tiempo que tardan los niños en apilar los bloques exitosamente es explicado de buena manera por el modelo binomial negativo. #Rechace Ho. Existe evidencia estadística de que el número de bloques que apila un niño exitosamente influye sobre el tiempo que tarda en apilarlos. #Rechace Ho.existe evidencia estadística de que la edad de los niños no influye sobre el tiempo que tarda en aplilar los bloques.
par(mfrow=c(1,2))
plot(abs(residuals(modelofinal)))
abline(h=2,col="red")
plot(abs(residuals(modelofinal,type="pearson")))
abline(h=2,col="red")
residuos=data.frame(abs(residuals(modelofinal)),abs(residuals(modelofinal,type="pearson")))
residuos[residuos[,1]>2&residuos[,2]>2,]#Los datos 24,47,69,76 en la base de datos de el núnero de bloques y el tiempo que tardan en apilarlos se pueden considerar atípicos
influence.measures(modelofinal)## Influence measures of
## glm.nb(formula = Time ~ Number + Age, data = blocks, init.theta = 4.16573609, link = log) :
##
## dfb.1_ dfb.Nmbr dfb.Age dffit cov.r cook.d hat inf
## 1 0.122600 -0.186295 -0.036108 -0.24219 1.044 1.40e-02 0.0473
## 2 0.115827 -0.048852 -0.103763 -0.16584 1.037 6.95e-03 0.0313
## 3 0.036748 -0.027429 -0.036195 -0.10371 1.023 2.77e-03 0.0143
## 4 0.041258 -0.079539 -0.012621 -0.13161 1.024 4.35e-03 0.0196
## 5 0.052991 -0.126460 0.002156 -0.16512 1.036 6.87e-03 0.0307
## 6 -0.304017 0.509547 0.048320 0.59456 1.021 2.04e-01 0.0923 *
## 7 0.016065 -0.031039 -0.002643 -0.04196 1.062 5.68e-04 0.0307
## 8 0.005119 0.002218 -0.007241 -0.00892 1.062 2.72e-05 0.0287
## 9 -0.096871 0.028711 0.058597 -0.14400 1.000 4.69e-03 0.0141
## 10 0.057151 -0.355557 0.119084 -0.38558 1.053 3.23e-02 0.0745
## 11 0.017228 0.032050 -0.043238 -0.06575 1.045 1.29e-03 0.0198
## 12 0.042894 0.139199 -0.100038 0.19471 1.016 1.88e-02 0.0268
## 13 0.071016 -0.009601 -0.078730 -0.11755 1.036 3.68e-03 0.0225
## 14 0.004145 0.000826 -0.000772 0.02187 1.041 1.79e-04 0.0102
## 15 0.004806 0.031658 -0.017500 0.04427 1.054 7.50e-04 0.0233
## 16 -0.003095 -0.020387 0.011270 -0.02851 1.055 2.67e-04 0.0233
## 17 0.064247 -0.000296 -0.072923 -0.09414 1.055 2.58e-03 0.0307
## 18 -0.010717 -0.002136 0.001996 -0.05653 1.032 9.19e-04 0.0102
## 19 -0.120184 -0.082580 0.153233 -0.17340 1.082 8.31e-03 0.0603
## 20 0.058750 -0.021092 -0.041401 0.06193 1.081 1.46e-03 0.0476
## 21 0.005527 0.004297 -0.007148 0.00866 1.076 2.65e-05 0.0411
## 22 -0.424391 0.113014 0.325730 -0.43735 1.000 3.57e-02 0.0598
## 23 0.014923 -0.058509 0.012847 -0.06694 1.081 1.42e-03 0.0485
## 24 0.055939 0.011148 -0.010419 0.29507 0.809 6.35e-02 0.0102 *
## 25 -0.133243 0.065149 0.075896 -0.16430 1.015 6.35e-03 0.0217
## 26 -0.047444 -0.073034 0.105622 0.15000 1.023 1.06e-02 0.0222
## 27 0.054179 0.155114 -0.156471 -0.20924 1.061 1.11e-02 0.0516
## 28 0.001355 0.019992 -0.015867 -0.02882 1.055 2.72e-04 0.0231
## 29 -0.010363 0.108093 -0.063423 -0.13271 1.046 4.74e-03 0.0310
## 30 0.005174 0.036378 -0.036613 -0.07668 1.034 1.65e-03 0.0147
## 31 -0.129627 0.017524 0.143706 0.21456 0.992 2.43e-02 0.0225
## 32 0.001531 0.022589 -0.017928 -0.03256 1.054 3.44e-04 0.0231
## 33 -0.047243 -0.048653 0.083915 0.10247 1.057 4.36e-03 0.0333
## 34 -0.057251 0.016968 0.034631 -0.08510 1.030 1.97e-03 0.0141
## 35 -0.052903 0.113736 -0.034083 -0.16997 0.997 6.33e-03 0.0172
## 36 0.012083 0.053934 -0.050616 -0.07910 1.053 1.85e-03 0.0270
## 37 -0.000903 -0.000299 0.000792 -0.00175 1.045 1.07e-06 0.0129
## 38 -0.002424 -0.005610 0.006346 0.00856 1.069 2.60e-05 0.0348
## 39 -0.013654 0.019355 -0.005262 -0.04887 1.037 7.14e-04 0.0117
## 40 -0.023548 0.031283 -0.001034 -0.05021 1.044 7.70e-04 0.0166
## 41 -0.035947 0.025142 0.008948 -0.07776 1.028 1.64e-03 0.0118
## 42 0.036887 0.060908 -0.078705 -0.09734 1.073 2.83e-03 0.0446
## 43 0.007449 0.001485 -0.001388 0.03929 1.037 6.10e-04 0.0102
## 44 0.043998 -0.006286 -0.036189 0.04656 1.078 8.11e-04 0.0443
## 45 0.186026 -0.066787 -0.131091 0.19610 1.058 1.75e-02 0.0476
## 46 -0.041858 0.011462 0.030423 -0.04611 1.062 6.80e-04 0.0307
## 47 0.804861 -0.355387 -0.540403 0.84314 0.816 5.32e-01 0.0654 *
## 48 -0.037016 0.160619 -0.073750 -0.19648 1.018 8.90e-03 0.0281
## 49 0.009536 -0.013517 0.003675 0.03413 1.041 4.50e-04 0.0117
## 50 -0.087918 0.070504 0.035569 -0.11503 1.046 3.65e-03 0.0281
## 51 0.043172 -0.052058 -0.021267 -0.08030 1.060 1.94e-03 0.0327
## 52 0.092149 -0.068067 -0.064841 -0.13381 1.059 4.98e-03 0.0394
## 53 0.016622 0.008614 -0.030916 -0.06292 1.036 1.14e-03 0.0132
## 54 0.070885 -0.190314 0.018869 -0.22352 1.049 1.23e-02 0.0469
## 55 0.003201 0.022505 -0.022650 -0.04744 1.042 6.87e-04 0.0147
## 56 -0.002244 0.003761 0.000357 0.00439 1.136 6.75e-06 0.0923 *
## 57 0.054602 -0.080093 -0.026563 -0.14149 1.021 4.93e-03 0.0201
## 58 -0.001214 -0.001250 0.002156 0.00263 1.067 2.43e-06 0.0333
## 59 -0.066805 0.019800 0.040410 -0.09931 1.024 2.57e-03 0.0141
## 60 0.019779 -0.140995 0.047556 -0.15907 1.072 6.99e-03 0.0514
## 61 -0.016834 -0.007934 0.027165 0.04373 1.045 7.45e-04 0.0162
## 62 0.018405 0.059728 -0.042925 0.08355 1.052 2.86e-03 0.0268
## 63 0.011056 0.004897 -0.016196 -0.02147 1.055 1.53e-04 0.0228
## 64 -0.003340 0.082702 -0.028851 0.11840 1.032 6.29e-03 0.0208
## 65 -0.007918 -0.052158 0.028832 -0.07294 1.049 1.58e-03 0.0233
## 66 0.002994 -0.086516 0.038558 -0.09741 1.087 2.89e-03 0.0557
## 67 0.012898 -0.004683 -0.011851 -0.01787 1.067 1.08e-04 0.0339
## 68 -0.016093 -0.003207 0.002997 -0.08489 1.020 1.87e-03 0.0102
## 69 0.569382 -0.081353 -0.468328 0.60251 0.853 2.57e-01 0.0443 *
## 70 -0.093187 -0.097574 0.137098 -0.16025 1.105 7.42e-03 0.0752 *
## 71 -0.139639 -0.003116 0.123772 -0.16056 1.036 6.52e-03 0.0296
## 72 -0.362441 0.026597 0.316523 -0.37993 1.018 2.92e-02 0.0579
## 73 -0.012135 0.041743 0.005340 0.10793 1.016 5.44e-03 0.0127
## 74 0.002222 0.014991 -0.005557 0.03315 1.043 4.21e-04 0.0131
## 75 -0.153012 0.074815 0.087156 -0.18868 1.003 7.87e-03 0.0217
## 76 -0.294970 0.081207 0.318646 0.55817 0.653 2.67e-01 0.0185 *
## 77 -0.009742 -0.017607 0.021897 0.02773 1.075 2.80e-04 0.0412
## 78 -0.005932 -0.087547 0.069480 0.12620 1.034 7.17e-03 0.0231
## 79 0.004801 0.033758 -0.033976 -0.07116 1.036 1.44e-03 0.0147
## 80 -0.000422 0.014137 -0.009941 -0.02029 1.053 1.37e-04 0.0213
## 81 -0.057906 0.033740 0.048226 0.09120 1.050 3.45e-03 0.0266
## 82 0.004585 0.067662 -0.053698 -0.09754 1.043 2.66e-03 0.0231
## 83 -0.061073 -0.174850 0.176380 0.23586 1.054 2.64e-02 0.0516
## 84 -0.061969 0.018367 0.037485 -0.09212 1.027 2.26e-03 0.0141
## 85 0.007497 0.050579 -0.018750 0.11186 1.015 5.88e-03 0.0131
## 86 -0.003530 -0.006566 0.008859 0.01347 1.052 6.53e-05 0.0198
## 87 0.001521 -0.001410 -0.000380 0.00252 1.050 2.22e-06 0.0172
## 88 0.054785 0.066769 -0.106452 -0.13844 1.038 5.00e-03 0.0271
## 89 -0.003493 0.007509 -0.002250 -0.01122 1.049 4.27e-05 0.0172
## 90 -0.061953 0.082302 -0.002721 -0.13210 1.016 4.24e-03 0.0166
## 91 0.004020 -0.005341 0.000177 0.00857 1.049 2.62e-05 0.0166
## 92 0.041152 -0.000189 -0.046709 -0.06030 1.060 1.13e-03 0.0307
## 93 -0.020446 0.028983 -0.007879 -0.07318 1.029 1.48e-03 0.0117
## 94 0.134424 -0.019206 -0.110566 0.14225 1.065 8.67e-03 0.0443
## 95 0.073681 -0.010528 -0.060604 0.07797 1.075 2.38e-03 0.0443
## 96 -0.221265 0.117320 0.130254 -0.24953 1.015 1.36e-02 0.0356
## 97 -0.594791 -0.224415 0.666427 -0.72716 0.885 6.77e-02 0.0668 *
## 98 -0.012563 0.056116 -0.029366 -0.08104 1.040 1.87e-03 0.0188
## 99 0.027064 -0.058184 0.017436 0.08695 1.036 3.25e-03 0.0172
## 100 -0.206508 0.165605 0.083546 -0.27019 0.982 1.43e-02 0.0281influencePlot(modelofinal)
#A la luz de todos los criterios propuestos en la literatura no se observan datos que puedan considerarse influyentes.
Realice una estimaci ́on con Child = A, Number = 10, T rial = 2, Shape = Cube y Age = 5
pred=data.frame(Child1=1, Number= 10,Trial= 2,formaCu= 1,Age = 5)
predict(modelofinal,pred,type="response")## 1
## 40.24463#Para apilar con exito 10 bloques,un niño de 5 años en dos intentos tarda en promedio 40.24463 segundos.
#PUNTO 2
#Se midi ́o el tiempo de muerte en semanas de dos grupos de pacientes con leucemia de acuerdo a un a #variable morfol ́ogica (AG) (1 positivo y 2 negativo). Los datos se encuentran en la base leukwbc y se tiene el n ́umero de c ́elulas blancas en la sangre de cada sujeto antes de su fallecimiento. #(a) Ajuste un modelo de regresi ́on que permita determinar el tiempo de muerte en semanas de los pacientes con leucemia. Muestre las medidas de bondad de ajuste. #(b) Estime el tiempo de vida que le resta a un paciente con un conteo de celulas blancas en la sangre de 1000 y positivo en AG. #(c) ¿Existen datos at ́ıpicos?. Muestre los residuos de deviance y pearson.
remove(list=ls(all=TRUE))
library(GLMsData)
library(MASS)
library(AER)
library(ISLR)
library(car)
library(carData)
data("leukwbc")
leukwbcleukwbc$AG1=ifelse(leukwbc$AG=="1",1,0)
leukwbcleukwbc$AG2=ifelse(leukwbc$AG=="2",1,0)
leukwbcleukwbc=leukwbc[,-3]
leukwbcmod1= glm(Time ~ WBC + AG1 + AG2, data= leukwbc, family = inverse.gaussian (link = "log"))## Warning: glm.fit: algorithm did not convergemod2= glm(Time ~ WBC + AG1 + AG2, data= leukwbc, family = Gamma (link = "log"))log(logLik(mod1)/logLik(mod2))## 'log Lik.' 0.03151353 (df=4)#Se debe hacer uso del modelo inversa Gausianna, debido a que el logaritmo del cociente de las log-verosimilitudes es mayor a cero.
mod1= glm(Time ~ WBC + AG1 + AG2, data= leukwbc, family = inverse.gaussian (link = "log"))## Warning: glm.fit: algorithm did not convergemod1##
## Call: glm(formula = Time ~ WBC + AG1 + AG2, family = inverse.gaussian(link = "log"),
## data = leukwbc)
##
## Coefficients:
## (Intercept) WBC AG1 AG2
## 2.968e+00 -2.196e-06 1.155e+00 NA
##
## Degrees of Freedom: 32 Total (i.e. Null); 30 Residual
## Null Deviance: 4.686
## Residual Deviance: 4.32 AIC: 314.7#selección del “mejor modelo”
step(mod1)## Start: AIC=314.69
## Time ~ WBC + AG1 + AG2## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge##
## Step: AIC=314.69
## Time ~ WBC + AG1
##
## Df Deviance AIC
## <none> 4.3198 314.69
## - AG1 1 4.5630 318.60
## - WBC 1 4.6016 319.53##
## Call: glm(formula = Time ~ WBC + AG1, family = inverse.gaussian(link = "log"),
## data = leukwbc)
##
## Coefficients:
## (Intercept) WBC AG1
## 2.968e+00 -2.196e-06 1.155e+00
##
## Degrees of Freedom: 32 Total (i.e. Null); 30 Residual
## Null Deviance: 4.686
## Residual Deviance: 4.32 AIC: 314.7modfinal= glm(formula = Time ~ WBC + AG1, family = inverse.gaussian(link = "log"),
data = leukwbc)## Warning: glm.fit: algorithm did not convergemodfinal##
## Call: glm(formula = Time ~ WBC + AG1, family = inverse.gaussian(link = "log"),
## data = leukwbc)
##
## Coefficients:
## (Intercept) WBC AG1
## 2.968e+00 -2.196e-06 1.155e+00
##
## Degrees of Freedom: 32 Total (i.e. Null); 30 Residual
## Null Deviance: 4.686
## Residual Deviance: 4.32 AIC: 314.7summary(modfinal)##
## Call:
## glm(formula = Time ~ WBC + AG1, family = inverse.gaussian(link = "log"),
## data = leukwbc)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.97982 -0.39282 -0.05775 0.07605 0.29310
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.968e+00 2.761e-01 10.752 8.24e-12 ***
## WBC -2.196e-06 5.372e-06 -0.409 0.6855
## AG1 1.155e+00 4.335e-01 2.664 0.0123 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for inverse.gaussian family taken to be 0.04120134)
##
## Null deviance: 4.6863 on 32 degrees of freedom
## Residual deviance: 4.3198 on 30 degrees of freedom
## AIC: 314.69
##
## Number of Fisher Scoring iterations: 25#No Rechace Ho para la variable WBC. Existe evidencia estadística de que el recuento de glóbulos blancos y la variable morfológica no influye en el tiempo de muertes en semanas en pacientes con Leucemia.
#Rechace Ho para la variable AG1. Existe evidencia estadística la variable morfológica AG1 influye en el tiempo de muertes en semanas en pacientes con Leucemia.
1-pchisq(4.6863- 4.3198,2)## [1] 0.83256#No rechace Ho. Existe evidencia estadística de que el modelo de regresión inversa gaussiana no se ajusta a los datos.
library(DescTools)
PseudoR2(modfinal,"Nagelkerke")## Nagelkerke
## 0.07819879#Existe una explicación buena de la variable dependiente. Por lo tanto, existe una buena explicación de que el recuento de glóbulos blancos y la variable morfológica en el tiempo de muertes por semana en los pacientes con Leucemia.
estimacion=data.frame(WBC= 1000, AG1= 1, WBC=0)
predict(modfinal,estimacion,type="response")## 1
## 61.59427#El tiempo de vida que le resta a un paciente con un conteo de las células blancas en la sangre de 1000 y positivo en AG es de 61.59427.
par(mfrow=c(1,2))
plot(abs(residuals(modfinal)))
abline(h=2,col="red")
plot(abs(residuals(modfinal, type="pearson")))
abline(h=2,col="red")
r1=abs(residuals(modfinal))
r2=abs(residuals(modfinal, type="pearson"))
residuales=data.frame(r1,r2)
residuales[residuales$r1>2& residuales$r2>2,]#En el conjunto de datos de los tiempos hasta la muerte y los recuentos de glóbulos blancos para dos grupos de pacientes con leucemia no se evidencia ningun dato atípico.
influence.measures(modfinal)## Influence measures of
## glm(formula = Time ~ WBC + AG1, family = inverse.gaussian(link = "log"), data = leukwbc) :
##
## dfb.1_ dfb.WBC dfb.AG1 dffit cov.r cook.d hat inf
## 1 0.001253 -0.002015 0.00408 0.00511 1.184 3.33e-05 0.0650
## 2 0.022362 -0.035946 0.06919 0.08738 1.172 2.32e-02 0.0658
## 3 0.010296 -0.016550 0.03588 0.04451 1.179 3.90e-03 0.0640
## 4 0.017562 -0.028230 0.05771 0.07219 1.175 1.37e-02 0.0648
## 5 -0.028201 0.045332 -0.10462 -0.12872 1.152 5.20e-03 0.0632
## 6 0.009647 -0.015507 0.04316 0.05207 1.174 5.84e-03 0.0614
## 7 0.011838 -0.019029 0.05177 0.06259 1.172 9.44e-03 0.0616
## 8 -0.042922 0.068995 -0.27335 -0.32247 0.998 7.98e-03 0.0596
## 9 -0.008998 0.014465 -0.03264 -0.04027 1.179 1.25e-03 0.0635
## 10 0.016614 -0.026705 0.06406 0.07846 1.170 1.74e-02 0.0628
## 11 -0.001314 0.002111 -0.00559 -0.00678 1.180 5.13e-05 0.0618
## 12 -0.000206 0.000331 -0.06242 -0.07201 1.166 2.82e-03 0.0590
## 13 0.002103 -0.003381 -0.07651 -0.08835 1.162 3.61e-03 0.0595
## 14 0.541227 -0.869991 -0.77568 -1.25118 0.525 2.83e-02 0.1350 *
## 15 0.541227 -0.869991 -0.77568 -1.25118 0.525 2.83e-02 0.1350 *
## 16 0.050237 -0.080753 -0.24847 -0.29819 1.046 9.33e-03 0.0669
## 17 -0.018408 0.029590 0.02638 0.04256 1.278 2.86e-03 0.1350
## 18 0.213183 -0.120805 -0.08738 0.21372 1.153 1.57e-01 0.0861
## 19 0.250631 -0.146613 -0.10089 0.25091 1.137 2.50e-01 0.0887
## 20 -0.023984 0.013718 0.00978 -0.02403 1.212 6.13e-04 0.0868
## 21 -0.209953 0.126787 0.08293 -0.21001 1.165 1.89e-02 0.0916
## 22 -0.031403 0.015753 0.01369 -0.03177 1.200 1.02e-03 0.0786
## 23 0.025180 -0.013964 -0.01044 0.02527 1.208 8.80e-04 0.0845
## 24 -0.385255 0.187391 0.17030 -0.39100 0.998 2.73e-02 0.0771
## 25 -0.269654 0.088392 0.13633 -0.28808 1.055 2.06e-02 0.0671
## 26 -0.392490 0.056406 0.22737 -0.45996 0.867 2.51e-02 0.0629
## 27 -0.289194 0.033810 0.17063 -0.34417 0.989 2.21e-02 0.0627
## 28 -0.109723 0.003321 0.06855 -0.13754 1.147 9.94e-03 0.0626
## 29 -0.239967 0.040691 0.13652 -0.27717 1.053 1.94e-02 0.0632
## 30 -0.325526 0.093185 0.16999 -0.35421 0.990 2.30e-02 0.0656
## 31 0.007036 0.147290 -0.06347 0.19384 1.303 8.25e-02 0.1665 *
## 32 0.101081 -0.635660 0.19022 -0.73853 1.351 1.61e-01 0.2860 *
## 33 -0.071842 0.451789 -0.13520 0.52490 1.445 8.93e-01 0.2860 *influencePlot(modfinal)
#A la luz de todos los criterios no se observan datos que se puedan considerar influyentes
#PUNTO3
Use la base de datos de los Affairs, disponible en la libreria AER contiene informaci ́on del n ́umero de infedelidades en grupo de 601 sujetos.
#(a) Estime el ”mejor” modelo de regresi ́on para explicar el n ́umero infelidades en la muestra. Muestre las medidas de bondad de ajuste. #(b) Existe alg ́un dato at ́ıpico o influyente?. Muestre los estad ́ısticos que sustenten dicha afirmaci ́on. #(c) Estime el promedio esperado de infidelidades de un hombre con 37 a ̃nos, con 8 a ̃nos de casado, sin hijos, poco religioso, con un grado de escolaridad en el nivel de maestr ́ıa, muy feliz con su matrimonio y ocupaci ́on en el rango de 8.
remove(list=ls(all=TRUE))
library(GLMsData)
library(MASS)
library(AER)
library(ISLR)
library(car)
library(carData)
data("Affairs")
head(Affairs)Affairs$niñosi=ifelse(Affairs$children=="yes",1,0)
AffairsAffairs=Affairs[,-5]
AffairsAffairs$genero=ifelse(Affairs$gender=="female",1,0)
AffairsAffairs=Affairs[,-2]
AffairsAffairs$ant=ifelse(Affairs$religiousness=="1",1,0)
Affairs$not=ifelse(Affairs$religiousness=="2",1,0)
Affairs$sli=ifelse(Affairs$religiousness=="3",1,0)
Affairs$som=ifelse(Affairs$religiousness=="4",1,0)
AffairsAffairs=Affairs[,-4]
AffairsAffairs$nue=ifelse(Affairs$education=="9",1,0)
Affairs$doc=ifelse(Affairs$education=="12",1,0)
Affairs$cat=ifelse(Affairs$education=="14",1,0)
Affairs$dsei=ifelse(Affairs$education=="16",1,0)
Affairs$dsie=ifelse(Affairs$education=="17",1,0)
Affairs$doch=ifelse(Affairs$education=="18",1,0)
AffairsAffairs=Affairs[,-4]
AffairsAffairs$tres=ifelse(Affairs$yearsmarried=="0.125",1,0)
Affairs$cuat=ifelse(Affairs$yearsmarried=="0.417",1,0)
Affairs$seis=ifelse(Affairs$yearsmarried=="0.75",1,0)
Affairs$dosy=ifelse(Affairs$yearsmarried=="1.5",1,0)
Affairs$trey=ifelse(Affairs$yearsmarried=="4",1,0)
Affairs$ochy=ifelse(Affairs$yearsmarried=="7",1,0)
Affairs$oncy=ifelse(Affairs$yearsmarried=="10",1,0)
AffairsAffairs=Affairs[,-3]
AffairsAffairs$very=ifelse(Affairs$rating=="5",1,0)
Affairs$somu=ifelse(Affairs$rating=="2",1,0)
Affairs$aver=ifelse(Affairs$rating=="3",1,0)
Affairs$hapaver=ifelse(Affairs$rating=="4",1,0)
AffairsAffairs=Affairs[,-4]
AffairsAffairs$und=ifelse(Affairs$age=="17.5",1,0)
Affairs$vcua=ifelse(Affairs$age=="22",1,0)
Affairs$vcin=ifelse(Affairs$age=="27",1,0)
Affairs$tcer=ifelse(Affairs$age=="32",1,0)
Affairs$tcin=ifelse(Affairs$age=="37",1,0)
Affairs$ccer=ifelse(Affairs$age=="42",1,0)
Affairs$ccin=ifelse(Affairs$age=="47",1,0)
Affairs$ccer=ifelse(Affairs$age=="52",1,0)
AffairsAffairs=Affairs[,-2]
Affairsmode1=glm(affairs~.,family = poisson, data = Affairs)
dispersiontest(mode1,trafo=1)##
## Overdispersion test
##
## data: mode1
## z = 6.0391, p-value = 7.748e-10
## alternative hypothesis: true alpha is greater than 0
## sample estimates:
## alpha
## 4.770966#Existe evidencia estadística de que hay sobredispersión en la variable número de infedelidades. #Por tanto, se debe ajustar un modelo binomial negativo.
library(MASS)
mode1.1=glm.nb(affairs~.,data = Affairs)## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Warning in glm.nb(affairs ~ ., data = Affairs): alternation limit reachedmode1.1##
## Call: glm.nb(formula = affairs ~ ., data = Affairs, init.theta = 0.1649076574,
## link = log)
##
## Coefficients:
## (Intercept) occupation niñosi genero ant not
## 1.12449 0.04904 -0.55480 0.07744 1.78046 0.76171
## sli som nue doc cat dsei
## 0.87568 0.10989 0.03626 0.25227 -0.24002 -0.09095
## dsie doch tres cuat seis dosy
## 0.45304 -0.10485 -2.23508 -3.06799 -4.60247 -1.56630
## trey ochy oncy very somu aver
## -0.44423 0.29250 0.17080 -1.61141 0.24962 -1.19045
## hapaver und vcua vcin tcer tcin
## -1.00370 5.57354 -0.39654 -0.40273 0.08817 -0.40657
## ccer ccin
## 1.47336 0.47511
##
## Degrees of Freedom: 600 Total (i.e. Null); 569 Residual
## Null Deviance: 428.1
## Residual Deviance: 337.5 AIC: 1491#selección del “mejor modelo”
step(mode1.1)## Start: AIC=1488.87
## affairs ~ occupation + niñosi + genero + ant + not + sli + som +
## nue + doc + cat + dsei + dsie + doch + tres + cuat + seis +
## dosy + trey + ochy + oncy + very + somu + aver + hapaver +
## und + vcua + vcin + tcer + tcin + ccer + ccin## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Df Deviance AIC
## - vcin 1 329.08 1478.5
## - som 1 329.11 1478.5
## - ochy 1 329.14 1478.5
## - oncy 1 329.25 1478.6
## - doch 1 329.27 1478.7
## - somu 1 329.29 1478.7
## - cat 1 329.38 1478.8
## - tcin 1 329.51 1478.9
## - vcua 1 329.55 1478.9
## - dsei 1 329.56 1478.9
## - nue 1 329.60 1479.0
## - tcer 1 329.77 1479.2
## - ccin 1 329.88 1479.3
## - genero 1 329.94 1479.3
## - hapaver 1 330.30 1479.7
## - doc 1 330.35 1479.7
## - niñosi 1 330.41 1479.8
## - trey 1 330.44 1479.8
## - aver 1 330.92 1480.3
## - occupation 1 330.96 1480.3
## - dsie 1 331.46 1480.8
## - tres 1 331.62 1481.0
## - not 1 332.12 1481.5
## - very 1 333.32 1482.7
## - cuat 1 333.77 1483.2
## - dosy 1 334.49 1483.9
## - sli 1 337.07 1486.5
## <none> 337.49 1488.9
## - seis 1 340.37 1489.8
## - ant 1 342.57 1492.0
## - ccer 1 344.36 1493.7
## - und 1 346.49 1495.9## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Warning in glm.nb(formula = affairs ~ occupation + niñosi + genero + ant + :
## alternation limit reached##
## Step: AIC=1478.64
## affairs ~ occupation + niñosi + genero + ant + not + sli + som +
## nue + doc + cat + dsei + dsie + doch + tres + cuat + seis +
## dosy + trey + ochy + oncy + very + somu + aver + hapaver +
## und + vcua + tcer + tcin + ccer + ccin## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Df Deviance AIC
## - som 1 338.25 1476.3
## - oncy 1 338.25 1476.3
## - genero 1 338.26 1476.3
## - nue 1 338.27 1476.3
## - vcua 1 338.29 1476.3
## - somu 1 338.32 1476.4
## - ochy 1 338.33 1476.4
## - doch 1 338.37 1476.4
## - cat 1 338.42 1476.5
## - doc 1 338.51 1476.6
## - tcin 1 338.69 1476.7
## - dsei 1 338.74 1476.8
## - tcer 1 338.79 1476.8
## - occupation 1 338.96 1477.0
## - ccin 1 339.08 1477.1
## - niñosi 1 339.22 1477.3
## - hapaver 1 339.35 1477.4
## - dsie 1 339.68 1477.7
## - aver 1 340.00 1478.0
## - trey 1 340.44 1478.5
## <none> 338.59 1478.6
## - ccer 1 340.72 1478.8
## - tres 1 340.85 1478.9
## - not 1 341.41 1479.5
## - sli 1 342.42 1480.5
## - very 1 342.56 1480.6
## - cuat 1 343.09 1481.1
## - dosy 1 346.21 1484.3
## - ant 1 347.74 1485.8
## - seis 1 350.92 1489.0
## - und 1 361.01 1499.1## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Warning in glm.nb(formula = affairs ~ occupation + niñosi + genero + ant + :
## alternation limit reached##
## Step: AIC=1476.52
## affairs ~ occupation + niñosi + genero + ant + not + sli + nue +
## doc + cat + dsei + dsie + doch + tres + cuat + seis + dosy +
## trey + ochy + oncy + very + somu + aver + hapaver + und +
## vcua + tcer + tcin + ccer + ccin## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Df Deviance AIC
## - oncy 1 338.37 1474.3
## - genero 1 338.38 1474.3
## - nue 1 338.39 1474.3
## - vcua 1 338.41 1474.3
## - somu 1 338.44 1474.4
## - ochy 1 338.45 1474.4
## - doch 1 338.49 1474.4
## - cat 1 338.53 1474.5
## - doc 1 338.64 1474.6
## - tcin 1 338.81 1474.7
## - dsei 1 338.86 1474.8
## - tcer 1 338.91 1474.8
## - occupation 1 339.08 1475.0
## - ccin 1 339.22 1475.2
## - niñosi 1 339.34 1475.3
## - hapaver 1 339.48 1475.4
## - dsie 1 339.81 1475.7
## - aver 1 340.15 1476.1
## - trey 1 340.58 1476.5
## <none> 338.59 1476.5
## - ccer 1 340.85 1476.8
## - tres 1 340.97 1476.9
## - very 1 342.74 1478.7
## - cuat 1 343.21 1479.1
## - not 1 345.20 1481.1
## - dosy 1 346.34 1482.3
## - sli 1 346.46 1482.4
## - seis 1 351.05 1487.0
## - ant 1 354.82 1490.8
## - und 1 360.56 1496.5## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Warning in glm.nb(formula = affairs ~ occupation + niñosi + genero + ant + :
## alternation limit reached##
## Step: AIC=1474.6
## affairs ~ occupation + niñosi + genero + ant + not + sli + nue +
## doc + cat + dsei + dsie + doch + tres + cuat + seis + dosy +
## trey + ochy + very + somu + aver + hapaver + und + vcua +
## tcer + tcin + ccer + ccin## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Df Deviance AIC
## - genero 1 338.30 1472.3
## - nue 1 338.32 1472.3
## - vcua 1 338.33 1472.3
## - somu 1 338.37 1472.4
## - ochy 1 338.39 1472.4
## - doch 1 338.41 1472.4
## - cat 1 338.46 1472.5
## - doc 1 338.57 1472.6
## - tcin 1 338.75 1472.8
## - dsei 1 338.78 1472.8
## - tcer 1 338.88 1472.9
## - occupation 1 339.03 1473.0
## - ccin 1 339.15 1473.2
## - niñosi 1 339.27 1473.3
## - hapaver 1 339.40 1473.4
## - dsie 1 339.73 1473.7
## - aver 1 340.11 1474.1
## <none> 338.58 1474.6
## - trey 1 340.76 1474.8
## - ccer 1 340.81 1474.8
## - tres 1 340.95 1475.0
## - very 1 342.75 1476.8
## - cuat 1 343.19 1477.2
## - not 1 345.49 1479.5
## - sli 1 346.38 1480.4
## - dosy 1 346.92 1480.9
## - seis 1 351.28 1485.3
## - ant 1 354.76 1488.8
## - und 1 360.54 1494.5## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Warning in glm.nb(formula = affairs ~ occupation + niñosi + ant + not + :
## alternation limit reached##
## Step: AIC=1472.31
## affairs ~ occupation + niñosi + ant + not + sli + nue + doc +
## cat + dsei + dsie + doch + tres + cuat + seis + dosy + trey +
## ochy + very + somu + aver + hapaver + und + vcua + tcer +
## tcin + ccer + ccin## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Df Deviance AIC
## - nue 1 338.52 1470.3
## - vcua 1 338.53 1470.3
## - somu 1 338.57 1470.4
## - ochy 1 338.60 1470.4
## - doch 1 338.61 1470.4
## - cat 1 338.66 1470.5
## - doc 1 338.80 1470.6
## - dsei 1 338.99 1470.8
## - tcin 1 339.00 1470.8
## - tcer 1 339.08 1470.9
## - occupation 1 339.24 1471.1
## - ccin 1 339.37 1471.2
## - niñosi 1 339.49 1471.3
## - hapaver 1 339.60 1471.4
## - dsie 1 340.06 1471.9
## - aver 1 340.31 1472.1
## <none> 338.49 1472.3
## - ccer 1 341.04 1472.8
## - trey 1 341.09 1472.9
## - tres 1 341.21 1473.0
## - very 1 342.96 1474.8
## - cuat 1 343.44 1475.2
## - not 1 345.81 1477.6
## - sli 1 346.59 1478.4
## - dosy 1 347.48 1479.3
## - seis 1 351.63 1483.5
## - ant 1 355.20 1487.0
## - und 1 359.23 1491.0## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Warning in glm.nb(formula = affairs ~ occupation + niñosi + ant + not + :
## alternation limit reached##
## Step: AIC=1470.33
## affairs ~ occupation + niñosi + ant + not + sli + doc + cat +
## dsei + dsie + doch + tres + cuat + seis + dosy + trey + ochy +
## very + somu + aver + hapaver + und + vcua + tcer + tcin +
## ccer + ccin## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Df Deviance AIC
## - vcua 1 338.50 1468.4
## - somu 1 338.51 1468.4
## - ochy 1 338.57 1468.5
## - doch 1 338.61 1468.5
## - cat 1 338.67 1468.5
## - doc 1 338.74 1468.6
## - tcin 1 339.00 1468.9
## - dsei 1 339.03 1468.9
## - tcer 1 339.05 1468.9
## - occupation 1 339.20 1469.1
## - ccin 1 339.33 1469.2
## - niñosi 1 339.45 1469.3
## - dsie 1 340.01 1469.9
## <none> 338.46 1470.3
## - hapaver 1 340.59 1470.5
## - ccer 1 341.01 1470.9
## - trey 1 341.05 1470.9
## - tres 1 341.18 1471.1
## - aver 1 341.47 1471.3
## - cuat 1 343.39 1473.3
## - not 1 345.76 1475.6
## - very 1 345.94 1475.8
## - sli 1 346.63 1476.5
## - dosy 1 347.44 1477.3
## - seis 1 351.61 1481.5
## - ant 1 355.19 1485.1
## - und 1 358.83 1488.7## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Warning in glm.nb(formula = affairs ~ occupation + niñosi + ant + not + :
## alternation limit reached##
## Step: AIC=1468.37
## affairs ~ occupation + niñosi + ant + not + sli + doc + cat +
## dsei + dsie + doch + tres + cuat + seis + dosy + trey + ochy +
## very + somu + aver + hapaver + und + tcer + tcin + ccer +
## ccin## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Df Deviance AIC
## - somu 1 338.50 1466.4
## - ochy 1 338.57 1466.5
## - doch 1 338.60 1466.5
## - cat 1 338.67 1466.6
## - doc 1 338.71 1466.6
## - tcin 1 338.99 1466.9
## - dsei 1 339.05 1467.0
## - tcer 1 339.08 1467.0
## - occupation 1 339.21 1467.1
## - ccin 1 339.32 1467.2
## - niñosi 1 339.41 1467.3
## - dsie 1 340.05 1468.0
## <none> 338.45 1468.4
## - hapaver 1 340.63 1468.5
## - ccer 1 341.08 1469.0
## - trey 1 341.33 1469.2
## - tres 1 341.44 1469.4
## - aver 1 341.50 1469.4
## - cuat 1 343.47 1471.4
## - not 1 345.78 1473.7
## - very 1 345.95 1473.9
## - sli 1 346.65 1474.6
## - dosy 1 349.29 1477.2
## - seis 1 352.57 1480.5
## - und 1 353.30 1481.2
## - ant 1 355.15 1483.1## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Warning in glm.nb(formula = affairs ~ occupation + niñosi + ant + not + :
## alternation limit reached##
## Step: AIC=1466.42
## affairs ~ occupation + niñosi + ant + not + sli + doc + cat +
## dsei + dsie + doch + tres + cuat + seis + dosy + trey + ochy +
## very + aver + hapaver + und + tcer + tcin + ccer + ccin## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Df Deviance AIC
## - ochy 1 338.55 1464.5
## - doch 1 338.58 1464.5
## - cat 1 338.63 1464.6
## - doc 1 338.72 1464.7
## - tcin 1 338.95 1464.9
## - dsei 1 339.00 1465.0
## - tcer 1 339.08 1465.0
## - occupation 1 339.30 1465.3
## - ccin 1 339.33 1465.3
## - niñosi 1 339.38 1465.3
## - dsie 1 340.12 1466.1
## <none> 338.44 1466.4
## - ccer 1 341.10 1467.1
## - trey 1 341.29 1467.3
## - tres 1 341.42 1467.4
## - cuat 1 343.44 1469.4
## - not 1 345.83 1471.8
## - sli 1 346.69 1472.7
## - hapaver 1 347.96 1473.9
## - aver 1 348.42 1474.4
## - dosy 1 349.23 1475.2
## - seis 1 352.53 1478.5
## - und 1 353.27 1479.2
## - ant 1 355.22 1481.2
## - very 1 363.63 1489.6## Warning in glm.nb(formula = affairs ~ occupation + niñosi + ant + not + :
## alternation limit reached##
## Step: AIC=1464.52
## affairs ~ occupation + niñosi + ant + not + sli + doc + cat +
## dsei + dsie + doch + tres + cuat + seis + dosy + trey + very +
## aver + hapaver + und + tcer + tcin + ccer + ccin## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge## Df Deviance AIC
## - doch 1 338.55 1462.7
## - cat 1 338.58 1462.7
## - doc 1 338.64 1462.8
## - tcin 1 338.81 1463.0
## - tcer 1 339.01 1463.2
## - dsei 1 339.03 1463.2
## - occupation 1 339.23 1463.4
## - niñosi 1 339.23 1463.4
## - ccin 1 339.46 1463.6
## - dsie 1 339.95 1464.1
## <none> 338.38 1464.5
## - ccer 1 341.02 1465.2
## - trey 1 341.14 1465.3
## - tres 1 341.25 1465.4
## - cuat 1 343.29 1467.4
## - not 1 346.02 1470.2
## - sli 1 347.20 1471.3
## - hapaver 1 347.89 1472.0
## - aver 1 348.30 1472.5
## - dosy 1 349.47 1473.6
## - seis 1 352.39 1476.5
## - und 1 353.06 1477.2
## - ant 1 355.16 1479.3
## - very 1 363.47 1487.6## Warning in glm.nb(formula = affairs ~ occupation + niñosi + ant + not + :
## alternation limit reached##
## Step: AIC=1462.7
## affairs ~ occupation + niñosi + ant + not + sli + doc + cat +
## dsei + dsie + tres + cuat + seis + dosy + trey + very + aver +
## hapaver + und + tcer + tcin + ccer + ccin## Warning: glm.fit: algorithm did not converge## Df Deviance AIC
## - cat 1 338.44 1460.8
## - tcin 1 338.79 1461.1
## - dsei 1 338.85 1461.2
## - tcer 1 338.89 1461.2
## - doc 1 338.89 1461.2
## - occupation 1 339.21 1461.5
## - niñosi 1 339.23 1461.6
## - ccin 1 339.46 1461.8
## <none> 338.37 1462.7
## - ccer 1 340.92 1463.2
## - tres 1 341.28 1463.6
## - trey 1 341.35 1463.7
## - dsie 1 341.41 1463.7
## - cuat 1 343.26 1465.6
## - not 1 346.00 1468.3
## - sli 1 347.02 1469.3
## - hapaver 1 347.71 1470.0
## - aver 1 348.15 1470.5
## - dosy 1 349.97 1472.3
## - seis 1 352.61 1474.9
## - und 1 353.00 1475.3
## - ant 1 355.17 1477.5
## - very 1 363.29 1485.6## Warning in glm.nb(formula = affairs ~ occupation + niñosi + ant + not + :
## alternation limit reached##
## Step: AIC=1460.77
## affairs ~ occupation + niñosi + ant + not + sli + doc + dsei +
## dsie + tres + cuat + seis + dosy + trey + very + aver + hapaver +
## und + tcer + tcin + ccer + ccin## Warning: glm.fit: algorithm did not converge## Df Deviance AIC
## - tcin 1 338.70 1459.2
## - dsei 1 338.72 1459.2
## - tcer 1 338.88 1459.3
## - niñosi 1 339.11 1459.6
## - doc 1 339.23 1459.7
## - ccin 1 339.40 1459.9
## - occupation 1 339.55 1460.0
## <none> 338.32 1460.8
## - ccer 1 341.00 1461.5
## - tres 1 341.21 1461.7
## - trey 1 341.31 1461.8
## - dsie 1 342.47 1462.9
## - cuat 1 343.27 1463.7
## - not 1 345.88 1466.3
## - sli 1 347.05 1467.5
## - hapaver 1 347.59 1468.0
## - aver 1 348.24 1468.7
## - dosy 1 349.91 1470.4
## - seis 1 352.49 1472.9
## - und 1 352.86 1473.3
## - ant 1 355.76 1476.2
## - very 1 363.17 1483.6## Warning in glm.nb(formula = affairs ~ occupation + niñosi + ant + not + :
## alternation limit reached##
## Step: AIC=1459.16
## affairs ~ occupation + niñosi + ant + not + sli + doc + dsei +
## dsie + tres + cuat + seis + dosy + trey + very + aver + hapaver +
## und + tcer + ccer + ccin## Warning: glm.fit: algorithm did not converge## Df Deviance AIC
## - dsei 1 339.03 1457.5
## - niñosi 1 339.51 1458.0
## - occupation 1 339.62 1458.1
## - tcer 1 339.66 1458.2
## - doc 1 339.76 1458.3
## - ccin 1 340.20 1458.7
## <none> 338.65 1459.2
## - trey 1 341.30 1459.8
## - tres 1 341.43 1459.9
## - ccer 1 341.85 1460.4
## - dsie 1 342.77 1461.3
## - cuat 1 343.55 1462.1
## - not 1 345.87 1464.4
## - sli 1 347.30 1465.8
## - hapaver 1 347.77 1466.3
## - aver 1 348.69 1467.2
## - dosy 1 349.88 1468.4
## - seis 1 352.52 1471.0
## - und 1 353.07 1471.6
## - ant 1 355.75 1474.3
## - very 1 363.12 1481.6
##
## Step: AIC=1457.53
## affairs ~ occupation + niñosi + ant + not + sli + doc + dsie +
## tres + cuat + seis + dosy + trey + very + aver + hapaver +
## und + tcer + ccer + ccin## Warning: glm.fit: algorithm did not converge## Df Deviance AIC
## - niñosi 1 339.70 1456.4
## - tcer 1 339.76 1456.4
## - occupation 1 340.15 1456.8
## - doc 1 340.21 1456.9
## - ccin 1 340.33 1457.0
## <none> 338.85 1457.5
## - trey 1 341.56 1458.2
## - ccer 1 341.68 1458.4
## - tres 1 341.91 1458.6
## - dsie 1 343.51 1460.2
## - cuat 1 343.71 1460.4
## - not 1 346.12 1462.8
## - sli 1 347.98 1464.7
## - hapaver 1 348.46 1465.1
## - aver 1 349.22 1465.9
## - dosy 1 350.13 1466.8
## - seis 1 353.05 1469.7
## - und 1 353.66 1470.3
## - ant 1 356.26 1472.9
## - very 1 364.67 1481.3
##
## Step: AIC=1456.38
## affairs ~ occupation + ant + not + sli + doc + dsie + tres +
## cuat + seis + dosy + trey + very + aver + hapaver + und +
## tcer + ccer + ccin
##
## Df Deviance AIC
## - tcer 1 339.41 1455.1
## - occupation 1 339.96 1455.7
## - doc 1 340.12 1455.8
## - ccin 1 340.22 1455.9
## - trey 1 340.63 1456.3
## <none> 338.66 1456.4
## - tres 1 341.21 1456.9
## - ccer 1 342.23 1457.9
## - dsie 1 343.06 1458.8
## - cuat 1 343.19 1458.9
## - not 1 346.75 1462.5
## - sli 1 347.94 1463.7
## - hapaver 1 348.43 1464.1
## - aver 1 349.05 1464.8
## - dosy 1 349.29 1465.0
## - seis 1 351.99 1467.7
## - und 1 352.59 1468.3
## - ant 1 356.37 1472.1
## - very 1 363.54 1479.3
##
## Step: AIC=1455.13
## affairs ~ occupation + ant + not + sli + doc + dsie + tres +
## cuat + seis + dosy + trey + very + aver + hapaver + und +
## ccer + ccin
##
## Df Deviance AIC
## - occupation 1 339.86 1454.2
## - ccin 1 340.04 1454.4
## - doc 1 340.10 1454.4
## <none> 338.80 1455.1
## - trey 1 341.55 1455.9
## - tres 1 341.57 1455.9
## - ccer 1 341.83 1456.2
## - dsie 1 342.89 1457.2
## - cuat 1 343.31 1457.6
## - not 1 347.03 1461.3
## - hapaver 1 348.04 1462.4
## - sli 1 348.55 1462.9
## - aver 1 349.11 1463.4
## - dosy 1 351.32 1465.7
## - und 1 352.70 1467.0
## - seis 1 352.84 1467.2
## - ant 1 355.85 1470.2
## - very 1 362.95 1477.3
##
## Step: AIC=1454.18
## affairs ~ ant + not + sli + doc + dsie + tres + cuat + seis +
## dosy + trey + very + aver + hapaver + und + ccer + ccin
##
## Df Deviance AIC
## - doc 1 339.14 1453.1
## - ccin 1 339.99 1453.9
## <none> 338.23 1454.2
## - tres 1 340.87 1454.8
## - trey 1 340.93 1454.9
## - ccer 1 341.16 1455.1
## - dsie 1 342.68 1456.6
## - cuat 1 342.81 1456.8
## - hapaver 1 347.04 1461.0
## - not 1 347.82 1461.8
## - sli 1 348.05 1462.0
## - aver 1 348.06 1462.0
## - dosy 1 351.47 1465.4
## - und 1 351.88 1465.8
## - seis 1 352.70 1466.7
## - ant 1 355.66 1469.6
## - very 1 361.99 1475.9
##
## Step: AIC=1453.09
## affairs ~ ant + not + sli + dsie + tres + cuat + seis + dosy +
## trey + very + aver + hapaver + und + ccer + ccin
##
## Df Deviance AIC
## - ccin 1 340.08 1452.8
## <none> 338.32 1453.1
## - trey 1 340.78 1453.5
## - tres 1 340.85 1453.6
## - ccer 1 341.08 1453.8
## - dsie 1 342.30 1455.1
## - cuat 1 342.47 1455.2
## - aver 1 347.73 1460.5
## - not 1 347.81 1460.6
## - hapaver 1 347.82 1460.6
## - sli 1 347.87 1460.7
## - dosy 1 351.08 1463.9
## - seis 1 352.76 1465.5
## - ant 1 355.33 1468.1
## - und 1 355.49 1468.3
## - very 1 363.62 1476.4
##
## Step: AIC=1452.85
## affairs ~ ant + not + sli + dsie + tres + cuat + seis + dosy +
## trey + very + aver + hapaver + und + ccer
##
## Df Deviance AIC
## <none> 338.10 1452.8
## - ccer 1 340.35 1453.1
## - tres 1 340.83 1453.6
## - trey 1 341.15 1453.9
## - cuat 1 342.46 1455.2
## - dsie 1 343.25 1456.0
## - not 1 346.27 1459.0
## - sli 1 346.53 1459.3
## - hapaver 1 346.55 1459.3
## - aver 1 347.43 1460.2
## - dosy 1 351.92 1464.7
## - seis 1 352.86 1465.6
## - ant 1 355.31 1468.0
## - und 1 355.44 1468.2
## - very 1 361.76 1474.5##
## Call: glm.nb(formula = affairs ~ ant + not + sli + dsie + tres + cuat +
## seis + dosy + trey + very + aver + hapaver + und + ccer,
## data = Affairs, init.theta = 0.1648233505, link = log)
##
## Coefficients:
## (Intercept) ant not sli dsie tres
## 0.8071 1.6223 0.8542 0.9481 0.7199 -2.1614
## cuat seis dosy trey very aver
## -2.8875 -4.6441 -1.4807 -0.5916 -1.6912 -1.2805
## hapaver und ccer
## -0.9830 5.9752 0.8331
##
## Degrees of Freedom: 600 Total (i.e. Null); 586 Residual
## Null Deviance: 431.6
## Residual Deviance: 338.1 AIC: 1455modefinal=glm.nb(formula = affairs ~ ant + not + sli + dsie + tres + cuat +
seis + dosy + trey + very + aver + hapaver + und + ccer,
data = Affairs, init.theta = 0.1648233505, link = log)
modefinal##
## Call: glm.nb(formula = affairs ~ ant + not + sli + dsie + tres + cuat +
## seis + dosy + trey + very + aver + hapaver + und + ccer,
## data = Affairs, init.theta = 0.1648233517, link = log)
##
## Coefficients:
## (Intercept) ant not sli dsie tres
## 0.8071 1.6223 0.8542 0.9481 0.7199 -2.1614
## cuat seis dosy trey very aver
## -2.8875 -4.6441 -1.4807 -0.5916 -1.6912 -1.2805
## hapaver und ccer
## -0.9830 5.9752 0.8331
##
## Degrees of Freedom: 600 Total (i.e. Null); 586 Residual
## Null Deviance: 431.6
## Residual Deviance: 338.1 AIC: 1455summary(modefinal)##
## Call:
## glm.nb(formula = affairs ~ ant + not + sli + dsie + tres + cuat +
## seis + dosy + trey + very + aver + hapaver + und + ccer,
## data = Affairs, init.theta = 0.1648233517, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5076 -0.8141 -0.6431 -0.1644 2.2707
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.8071 0.3229 2.500 0.012429 *
## ant 1.6223 0.4306 3.768 0.000165 ***
## not 0.8542 0.2907 2.938 0.003303 **
## sli 0.9481 0.3053 3.105 0.001900 **
## dsie 0.7199 0.3168 2.272 0.023065 *
## tres -2.1614 1.1484 -1.882 0.059820 .
## cuat -2.8875 1.3384 -2.157 0.030977 *
## seis -4.6441 1.3706 -3.388 0.000703 ***
## dosy -1.4807 0.3773 -3.924 8.69e-05 ***
## trey -0.5916 0.3155 -1.875 0.060742 .
## very -1.6912 0.3540 -4.778 1.77e-06 ***
## aver -1.2805 0.4080 -3.138 0.001698 **
## hapaver -0.9830 0.3439 -2.858 0.004260 **
## und 5.9752 1.3653 4.376 1.21e-05 ***
## ccer 0.8331 0.5781 1.441 0.149567
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(0.1648) family taken to be 1)
##
## Null deviance: 431.59 on 600 degrees of freedom
## Residual deviance: 338.10 on 586 degrees of freedom
## AIC: 1454.8
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 0.1648
## Std. Err.: 0.0187
##
## 2 x log-likelihood: -1422.84501-pchisq( 431.6 - 338.1 , 600 - 586)## [1] 8.248957e-14#Rechace Ho. Existe evidencia estadística de que el modelo binimial negativo se ajusta alos datos #Rechace Ho. existe evidencia estadística de que la variable ant(anti religioso) influye sobre el número de infelidades #Rechace Ho. existe evidencia estadística de que la variable not(nada religioso) influye sobre el número de infelidades #Rechace Ho. existe evidencia estadística de que la variable sli(ligeramente religioso) influye sobre el número de infelidades #Rechace Ho. existe evidencia estadística de que la variable dsie(algun trabajo de post grado) influye sobre el número de infelidades #Rechace Ho. existe evidencia estadística de que la variable und(inferior a 20 años de edad) influye sobre el número de infelidades #Rechace Ho. existe evidencia estadística de que la variable ccer(edad entre 50-54 años) influye sobre el número de infelidades #Rechace Ho. existe evidencia estadística de que la variable tres(tres meses o menos de casado)no influye sobre el número de infelidades #Rechace Ho. existe evidencia estadística de que la variable cua(de 4 a 6 meses de casado)no influye sobre el número de infelidades #Rechace Ho. existe evidencia estadística de que la variable seis(6 meses a un año)no influye sobre el número de infelidades #Rechace Ho. existe evidencia estadística de que la variable dosy(entrte 1 -2 años de casado)no influye sobre el número de infelidades #Rechace Ho. existe evidencia estadística de que la variable trey(entre 3-5años de casado)no influye sobre el número de infelidades #Rechace Ho. existe evidencia estadística de que la variable very(autoevaluación del matrimonio muy feliz)no influye sobre el número de infelidades #Rechace Ho. existe evidencia estadística de que la variable hapaver (autoevaluación del matrimonio más feliz que el promedio)no influye sobre el número de infelidades
par(mfrow=c(1,2))
plot(abs(residuals(modefinal)))
abline(h=2,col="red")
plot(abs(residuals(modefinal,type="pearson")))
abline(h=2,col="red")
residuos=data.frame(abs(residuals(modefinal)),abs(residuals(modefinal,type="pearson")))
residuos[residuos[,1]>2&residuos[,2]>2,]#En el conjunto de datos Affairs, se puden considerar como datos atípicos las observaciones 1573.
library(car)
influence.measures(modefinal)## Influence measures of
## glm.nb(formula = affairs ~ ant + not + sli + dsie + tres + cuat + seis + dosy + trey + very + aver + hapaver + und + ccer, data = Affairs, init.theta = 0.1648233517, link = log) :
##
## dfb.1_ dfb.ant dfb.not dfb.sli dfb.dsie dfb.tres dfb.cuat
## 4 1.60e-02 -6.12e-03 -8.13e-03 -1.02e-01 0.027125 4.37e-03 1.39e-02
## 5 -1.97e-02 3.92e-02 4.39e-02 4.49e-02 0.012127 -4.01e-03 -8.57e-04
## 11 7.56e-03 -2.02e-01 -9.37e-03 -1.15e-02 0.009972 1.18e-02 3.38e-02
## 16 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 23 2.34e-03 -3.20e-04 -2.93e-03 6.15e-04 -0.011342 1.07e-04 3.02e-04
## 29 2.79e-02 1.48e-03 -3.95e-02 8.23e-03 -0.078148 3.32e-03 3.20e-03
## 44 2.78e-04 2.90e-04 -1.60e-03 -4.28e-05 0.002272 1.56e-04 -4.33e-04
## 45 1.96e-02 -5.10e-03 -8.91e-02 -8.51e-03 0.022185 1.14e-02 7.28e-03
## 47 -1.59e-01 3.25e-02 6.32e-02 5.59e-02 0.018001 5.60e-03 -1.01e-03
## 49 -7.69e-03 1.31e-02 1.99e-02 1.60e-02 0.007566 1.26e-05 -1.15e-03
## 50 -1.19e-01 -9.16e-03 -9.36e-02 -1.10e-03 0.023550 1.50e-02 -3.32e-03
## 55 -1.97e-02 3.92e-02 4.39e-02 4.49e-02 0.012127 -4.01e-03 -8.57e-04
## 64 -1.81e-02 2.49e-02 5.11e-02 5.21e-02 -0.128466 2.43e-03 1.97e-02
## 80 3.68e-02 1.94e-03 -4.41e-02 1.63e-02 -0.107553 -2.35e-03 8.86e-03
## 86 -1.97e-02 3.92e-02 4.39e-02 4.49e-02 0.012127 -4.01e-03 -8.57e-04
## 93 2.73e-02 -2.16e-01 -1.70e-02 -1.18e-02 -0.162177 1.92e-02 4.09e-02
## 108 1.83e-02 -9.21e-03 -9.01e-02 -4.91e-03 0.028679 1.23e-02 -2.91e-03
## 114 1.20e-04 7.14e-04 4.42e-03 -7.29e-03 0.003004 -4.47e-04 6.55e-04
## 115 1.17e-02 4.92e-03 -2.87e-02 2.72e-03 0.014058 3.20e-03 -2.80e-03
## 116 1.58e-02 -4.28e-03 -8.16e-02 -1.28e-02 0.024835 1.62e-02 1.39e-03
## 123 1.17e-02 4.92e-03 -2.87e-02 2.72e-03 0.014058 3.20e-03 -2.80e-03
## 127 1.17e-02 4.92e-03 -2.87e-02 2.72e-03 0.014058 3.20e-03 -2.80e-03
## 129 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 134 1.28e-02 -5.18e-03 -1.42e-02 -9.13e-02 0.029300 1.05e-02 6.91e-03
## 137 2.17e-02 1.85e-02 -6.20e-02 1.23e-02 0.018080 7.19e-04 -2.70e-03
## 139 1.17e-02 4.92e-03 -2.87e-02 2.72e-03 0.014058 3.20e-03 -2.80e-03
## 147 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 151 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 153 1.44e-02 1.45e-02 9.52e-04 -5.95e-02 0.023266 1.15e-03 -1.68e-03
## 155 4.07e-02 1.26e-02 1.29e-02 -7.48e-02 -0.134965 -8.81e-03 1.53e-02
## 162 -2.59e-02 2.12e-02 3.13e-02 3.62e-02 0.020555 4.38e-03 -9.49e-04
## 163 1.05e-02 4.76e-03 8.54e-03 -4.06e-02 0.016918 -4.20e-05 9.79e-05
## 165 -6.67e-03 2.56e-02 1.30e-02 2.17e-02 -0.025032 -1.57e-03 -1.84e-01
## 168 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 170 1.24e-02 -1.54e-01 1.04e-02 1.36e-02 0.007503 -6.07e-04 1.94e-02
## 172 -1.01e-02 1.58e-02 2.71e-02 2.49e-02 0.010376 -2.18e-03 -3.20e-03
## 184 1.60e-02 -6.12e-03 -8.13e-03 -1.02e-01 0.027125 4.37e-03 1.39e-02
## 187 -9.15e-03 3.40e-02 4.04e-02 4.54e-02 -0.082212 -1.23e-04 2.73e-03
## 192 8.44e-03 -1.04e-01 1.19e-02 3.15e-03 0.011476 3.76e-03 1.01e-02
## 194 3.07e-05 3.60e-04 1.91e-03 -3.95e-03 0.001811 8.17e-05 1.13e-04
## 210 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 217 -9.76e-03 2.46e-02 5.04e-02 3.78e-02 0.018659 -3.44e-03 -6.66e-03
## 220 -6.39e-03 1.53e-02 6.73e-03 1.08e-02 0.010792 -5.42e-04 -1.02e-01
## 224 -1.03e-01 2.06e-02 -6.60e-02 2.39e-02 0.019995 2.03e-03 -1.45e-02
## 227 -1.71e-02 1.90e-02 4.16e-02 5.17e-02 -0.112421 3.76e-03 8.94e-03
## 228 1.44e-02 1.45e-02 9.52e-04 -5.95e-02 0.023266 1.15e-03 -1.68e-03
## 239 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 241 1.96e-02 -5.10e-03 -8.91e-02 -8.51e-03 0.022185 1.14e-02 7.28e-03
## 245 -7.69e-03 1.31e-02 1.99e-02 1.60e-02 0.007566 1.26e-05 -1.15e-03
## 249 1.96e-02 -5.10e-03 -8.91e-02 -8.51e-03 0.022185 1.14e-02 7.28e-03
## 262 -7.72e-04 7.54e-04 1.64e-03 6.78e-04 0.000614 3.43e-05 -2.55e-05
## 265 1.96e-02 -5.10e-03 -8.91e-02 -8.51e-03 0.022185 1.14e-02 7.28e-03
## 267 -1.43e-02 2.79e-02 2.74e-02 2.89e-02 0.012097 8.05e-04 -3.20e-03
## 269 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 271 9.76e-03 -1.30e-01 1.19e-03 5.47e-03 0.013021 6.56e-03 1.13e-02
## 277 -1.59e-01 3.25e-02 6.32e-02 5.59e-02 0.018001 5.60e-03 -1.01e-03
## 290 -1.20e-02 2.09e-02 3.53e-02 2.85e-02 0.008956 -3.30e-03 5.18e-04
## 292 -1.55e-02 2.05e-02 3.53e-02 3.76e-02 -0.099905 8.39e-03 1.17e-02
## 293 -2.02e-03 2.52e-03 7.28e-03 3.57e-03 -0.007348 -4.71e-04 9.30e-04
## 295 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 299 -1.59e-01 3.25e-02 6.32e-02 5.59e-02 0.018001 5.60e-03 -1.01e-03
## 320 1.60e-02 -6.12e-03 -8.13e-03 -1.02e-01 0.027125 4.37e-03 1.39e-02
## 321 1.67e-04 3.60e-04 -1.14e-03 -3.84e-04 0.001497 3.28e-04 -1.22e-04
## 324 1.66e-02 1.46e-02 -5.49e-02 5.08e-03 0.019458 6.02e-03 -6.00e-03
## 334 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 351 3.65e-02 -1.39e-02 -9.67e-02 -8.69e-03 -0.123650 1.77e-02 1.30e-02
## 355 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 361 3.32e-02 -1.51e-02 -1.47e-02 -1.03e-01 -0.121719 1.06e-02 1.98e-02
## 362 -7.69e-03 1.31e-02 1.99e-02 1.60e-02 0.007566 1.26e-05 -1.15e-03
## 366 1.71e-02 6.97e-03 -3.52e-02 8.19e-03 0.015069 -5.58e-04 -3.53e-04
## 370 3.37e-02 1.07e-02 3.27e-03 -6.93e-02 -0.107321 -2.93e-04 7.90e-03
## 374 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 378 1.23e-02 3.16e-03 -2.25e-02 -5.07e-03 0.007300 -1.71e-01 -1.61e-03
## 381 -2.59e-02 2.12e-02 3.13e-02 3.62e-02 0.020555 4.38e-03 -9.49e-04
## 382 5.82e-03 -1.77e-01 -1.70e-02 -1.70e-02 0.016461 1.85e-02 2.38e-02
## 383 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 384 1.05e-02 4.76e-03 8.54e-03 -4.06e-02 0.016918 -4.20e-05 9.79e-05
## 400 3.37e-02 1.07e-02 3.27e-03 -6.93e-02 -0.107321 -2.93e-04 7.90e-03
## 403 1.05e-02 4.76e-03 8.54e-03 -4.06e-02 0.016918 -4.20e-05 9.79e-05
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## 651 1.98e-02 1.33e-02 -6.25e-02 1.40e-02 0.023306 1.97e-03 -1.08e-02
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## 670 1.67e-04 3.60e-04 -1.14e-03 -3.84e-04 0.001497 3.28e-04 -1.22e-04
## 671 1.96e-02 -5.10e-03 -8.91e-02 -8.51e-03 0.022185 1.14e-02 7.28e-03
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## 760 1.66e-02 1.46e-02 -5.49e-02 5.08e-03 0.019458 6.02e-03 -6.00e-03
## 763 1.66e-02 1.46e-02 -5.49e-02 5.08e-03 0.019458 6.02e-03 -6.00e-03
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## 812 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
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## 823 1.17e-02 4.92e-03 -2.87e-02 2.72e-03 0.014058 3.20e-03 -2.80e-03
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## 867 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
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## 875 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
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## 904 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
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## 920 1.05e-02 4.76e-03 8.54e-03 -4.06e-02 0.016918 -4.20e-05 9.79e-05
## 921 2.78e-04 2.90e-04 -1.60e-03 -4.28e-05 0.002272 1.56e-04 -4.33e-04
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## 929 -1.59e-01 3.25e-02 6.32e-02 5.59e-02 0.018001 5.60e-03 -1.01e-03
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## 945 1.96e-02 -5.10e-03 -8.91e-02 -8.51e-03 0.022185 1.14e-02 7.28e-03
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## 995 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
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## 1030 1.60e-02 -6.12e-03 -8.13e-03 -1.02e-01 0.027125 4.37e-03 1.39e-02
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## 1034 -1.81e-02 2.49e-02 5.11e-02 5.21e-02 -0.128466 2.43e-03 1.97e-02
## 1037 1.71e-02 6.97e-03 -3.52e-02 8.19e-03 0.015069 -5.58e-04 -3.53e-04
## 1038 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
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## 1046 -1.55e-02 2.05e-02 3.53e-02 3.76e-02 -0.099905 8.39e-03 1.17e-02
## 1054 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
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## 1072 -9.14e-03 2.59e-02 4.32e-02 2.49e-02 0.015878 2.37e-03 -1.80e-03
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## 1077 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
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## 1083 1.60e-02 -6.12e-03 -8.13e-03 -1.02e-01 0.027125 4.37e-03 1.39e-02
## 1084 5.82e-03 -1.77e-01 -1.70e-02 -1.70e-02 0.016461 1.85e-02 2.38e-02
## 1086 1.44e-02 1.45e-02 9.52e-04 -5.95e-02 0.023266 1.15e-03 -1.68e-03
## 1087 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1089 1.20e-04 7.14e-04 4.42e-03 -7.29e-03 0.003004 -4.47e-04 6.55e-04
## 1096 1.33e-02 3.12e-03 1.16e-02 -4.43e-02 0.021374 -3.77e-03 -2.70e-03
## 1102 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1103 5.19e-02 -1.25e-02 8.61e-04 -1.38e-01 0.021734 -6.66e-03 1.02e-02
## 1107 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1109 -2.59e-02 2.12e-02 3.13e-02 3.62e-02 0.020555 4.38e-03 -9.49e-04
## 1115 1.96e-02 -5.10e-03 -8.91e-02 -8.51e-03 0.022185 1.14e-02 7.28e-03
## 1119 -1.55e-02 2.05e-02 3.53e-02 3.76e-02 -0.099905 8.39e-03 1.17e-02
## 1124 -1.81e-02 2.49e-02 5.11e-02 5.21e-02 -0.128466 2.43e-03 1.97e-02
## 1126 8.44e-03 -1.04e-01 1.19e-02 3.15e-03 0.011476 3.76e-03 1.01e-02
## 1128 1.87e-02 1.82e-02 8.78e-03 -6.85e-02 0.022450 -5.64e-03 2.91e-03
## 1129 1.17e-02 4.92e-03 -2.87e-02 2.72e-03 0.014058 3.20e-03 -2.80e-03
## 1130 1.96e-02 -5.10e-03 -8.91e-02 -8.51e-03 0.022185 1.14e-02 7.28e-03
## 1133 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1140 1.58e-02 -4.28e-03 -8.16e-02 -1.28e-02 0.024835 1.62e-02 1.39e-03
## 1143 1.44e-02 1.45e-02 9.52e-04 -5.95e-02 0.023266 1.15e-03 -1.68e-03
## 1146 -2.59e-02 2.12e-02 3.13e-02 3.62e-02 0.020555 4.38e-03 -9.49e-04
## 1153 7.56e-03 -2.02e-01 -9.37e-03 -1.15e-02 0.009972 1.18e-02 3.38e-02
## 1156 1.17e-02 4.92e-03 -2.87e-02 2.72e-03 0.014058 3.20e-03 -2.80e-03
## 1157 1.33e-02 3.12e-03 1.16e-02 -4.43e-02 0.021374 -3.77e-03 -2.70e-03
## 1158 4.80e-02 -9.82e-03 -9.58e-02 -2.83e-02 0.022079 1.05e-02 -4.17e-03
## 1160 -1.19e-01 -9.16e-03 -9.36e-02 -1.10e-03 0.023550 1.50e-02 -3.32e-03
## 1161 1.58e-02 -4.28e-03 -8.16e-02 -1.28e-02 0.024835 1.62e-02 1.39e-03
## 1166 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1177 1.44e-02 1.45e-02 9.52e-04 -5.95e-02 0.023266 1.15e-03 -1.68e-03
## 1178 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1180 1.05e-02 4.76e-03 8.54e-03 -4.06e-02 0.016918 -4.20e-05 9.79e-05
## 1187 1.68e-02 2.24e-03 -2.95e-02 -2.48e-03 0.010172 -2.32e-01 -4.23e-03
## 1191 1.83e-02 -9.21e-03 -9.01e-02 -4.91e-03 0.028679 1.23e-02 -2.91e-03
## 1195 -1.97e-02 3.92e-02 4.39e-02 4.49e-02 0.012127 -4.01e-03 -8.57e-04
## 1207 -1.19e-01 -9.16e-03 -9.36e-02 -1.10e-03 0.023550 1.50e-02 -3.32e-03
## 1208 -1.75e-02 3.19e-02 3.49e-02 4.15e-02 0.015451 -2.60e-03 -6.69e-03
## 1209 3.65e-02 -1.39e-02 -9.67e-02 -8.69e-03 -0.123650 1.77e-02 1.30e-02
## 1211 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1215 8.44e-03 -1.04e-01 1.19e-02 3.15e-03 0.011476 3.76e-03 1.01e-02
## 1221 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1226 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1229 -1.81e-02 2.49e-02 5.11e-02 5.21e-02 -0.128466 2.43e-03 1.97e-02
## 1231 1.49e-02 -1.03e-02 -1.41e-02 -9.24e-02 0.033524 5.69e-03 3.15e-03
## 1234 1.60e-02 -6.12e-03 -8.13e-03 -1.02e-01 0.027125 4.37e-03 1.39e-02
## 1235 -8.92e-02 6.92e-03 3.50e-02 -6.55e-02 0.023384 -6.50e-03 -3.92e-03
## 1242 1.05e-02 4.76e-03 8.54e-03 -4.06e-02 0.016918 -4.20e-05 9.79e-05
## 1245 -1.10e-01 2.00e-02 2.37e-02 -7.75e-02 0.025254 -5.95e-03 -7.60e-03
## 1260 -1.26e-01 -1.04e-02 4.15e-03 -1.12e-01 0.029276 6.48e-03 4.42e-03
## 1266 1.96e-02 -5.10e-03 -8.91e-02 -8.51e-03 0.022185 1.14e-02 7.28e-03
## 1271 1.60e-02 -6.12e-03 -8.13e-03 -1.02e-01 0.027125 4.37e-03 1.39e-02
## 1273 1.17e-02 4.92e-03 -2.87e-02 2.72e-03 0.014058 3.20e-03 -2.80e-03
## 1276 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1280 -1.01e-02 1.58e-02 2.71e-02 2.49e-02 0.010376 -2.18e-03 -3.20e-03
## 1282 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1285 4.13e-02 7.92e-03 -7.48e-02 2.38e-02 -0.120888 5.25e-04 -2.35e-03
## 1295 -3.41e-03 8.00e-03 2.80e-03 5.06e-03 0.006277 3.85e-04 -5.36e-02
## 1298 1.66e-02 1.46e-02 -5.49e-02 5.08e-03 0.019458 6.02e-03 -6.00e-03
## 1299 -2.59e-02 2.12e-02 3.13e-02 3.62e-02 0.020555 4.38e-03 -9.49e-04
## 1304 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1305 -1.19e-01 -9.16e-03 -9.36e-02 -1.10e-03 0.023550 1.50e-02 -3.32e-03
## 1311 1.66e-02 1.46e-02 -5.49e-02 5.08e-03 0.019458 6.02e-03 -6.00e-03
## 1314 2.17e-02 1.85e-02 -6.20e-02 1.23e-02 0.018080 7.19e-04 -2.70e-03
## 1319 1.05e-02 4.76e-03 8.54e-03 -4.06e-02 0.016918 -4.20e-05 9.79e-05
## 1322 -1.97e-02 3.92e-02 4.39e-02 4.49e-02 0.012127 -4.01e-03 -8.57e-04
## 1324 2.09e-02 -1.48e-02 -5.70e-02 -1.06e-03 -0.003685 -5.33e-03 5.32e-03
## 1327 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1328 -1.15e-03 1.02e-03 2.50e-03 1.25e-03 0.000940 -9.29e-05 -1.47e-04
## 1330 -1.71e-02 1.90e-02 4.16e-02 5.17e-02 -0.112421 3.76e-03 8.94e-03
## 1332 1.44e-02 1.45e-02 9.52e-04 -5.95e-02 0.023266 1.15e-03 -1.68e-03
## 1333 -8.11e-02 -2.25e-04 -4.45e-02 2.92e-02 -0.141200 -1.93e-03 5.53e-04
## 1336 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1341 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1344 1.24e-02 -1.54e-01 1.04e-02 1.36e-02 0.007503 -6.07e-04 1.94e-02
## 1352 -2.59e-02 2.12e-02 3.13e-02 3.62e-02 0.020555 4.38e-03 -9.49e-04
## 1358 -1.43e-02 2.79e-02 2.74e-02 2.89e-02 0.012097 8.05e-04 -3.20e-03
## 1359 1.66e-02 1.46e-02 -5.49e-02 5.08e-03 0.019458 6.02e-03 -6.00e-03
## 1361 5.57e-02 -1.11e-02 -9.86e-02 -2.40e-02 0.015942 2.24e-03 2.13e-03
## 1364 1.71e-02 6.97e-03 -3.52e-02 8.19e-03 0.015069 -5.58e-04 -3.53e-04
## 1368 -1.02e-02 3.12e-02 6.09e-02 3.70e-02 0.011322 -5.38e-03 3.66e-03
## 1384 1.00e-02 1.66e-02 -8.01e-03 -2.84e-02 -0.025250 8.93e-04 -2.22e-01
## 1390 1.17e-02 4.92e-03 -2.87e-02 2.72e-03 0.014058 3.20e-03 -2.80e-03
## 1393 -1.97e-02 3.92e-02 4.39e-02 4.49e-02 0.012127 -4.01e-03 -8.57e-04
## 1394 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1402 1.17e-02 4.92e-03 -2.87e-02 2.72e-03 0.014058 3.20e-03 -2.80e-03
## 1407 9.76e-03 -1.30e-01 1.19e-03 5.47e-03 0.013021 6.56e-03 1.13e-02
## 1408 1.60e-02 -6.12e-03 -8.13e-03 -1.02e-01 0.027125 4.37e-03 1.39e-02
## 1412 1.58e-02 -4.28e-03 -8.16e-02 -1.28e-02 0.024835 1.62e-02 1.39e-03
## 1413 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1416 -1.01e-02 1.58e-02 2.71e-02 2.49e-02 0.010376 -2.18e-03 -3.20e-03
## 1417 1.71e-02 6.97e-03 -3.52e-02 8.19e-03 0.015069 -5.58e-04 -3.53e-04
## 1418 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1419 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1420 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1423 1.44e-02 1.45e-02 9.52e-04 -5.95e-02 0.023266 1.15e-03 -1.68e-03
## 1424 -1.59e-01 3.25e-02 6.32e-02 5.59e-02 0.018001 5.60e-03 -1.01e-03
## 1432 -7.72e-04 7.54e-04 1.64e-03 6.78e-04 0.000614 3.43e-05 -2.55e-05
## 1433 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1437 1.28e-02 -5.18e-03 -1.42e-02 -9.13e-02 0.029300 1.05e-02 6.91e-03
## 1438 1.58e-02 -4.28e-03 -8.16e-02 -1.28e-02 0.024835 1.62e-02 1.39e-03
## 1439 1.58e-02 -4.28e-03 -8.16e-02 -1.28e-02 0.024835 1.62e-02 1.39e-03
## 1446 1.83e-02 -9.21e-03 -9.01e-02 -4.91e-03 0.028679 1.23e-02 -2.91e-03
## 1450 3.68e-02 1.94e-03 -4.41e-02 1.63e-02 -0.107553 -2.35e-03 8.86e-03
## 1451 1.67e-04 3.60e-04 -1.14e-03 -3.84e-04 0.001497 3.28e-04 -1.22e-04
## 1452 -1.97e-02 3.92e-02 4.39e-02 4.49e-02 0.012127 -4.01e-03 -8.57e-04
## 1453 4.06e-04 1.96e-02 3.87e-02 2.56e-02 -0.067036 -2.65e-01 6.53e-03
## 1456 -7.69e-03 1.31e-02 1.99e-02 1.60e-02 0.007566 1.26e-05 -1.15e-03
## 1464 1.49e-02 -1.03e-02 -1.41e-02 -9.24e-02 0.033524 5.69e-03 3.15e-03
## 1469 -9.76e-03 2.46e-02 5.04e-02 3.78e-02 0.018659 -3.44e-03 -6.66e-03
## 1473 -1.01e-01 3.24e-02 6.52e-02 5.15e-02 0.012787 -4.68e-03 -6.00e-03
## 1481 1.17e-02 4.92e-03 -2.87e-02 2.72e-03 0.014058 3.20e-03 -2.80e-03
## 1482 2.64e-02 9.37e-04 1.36e-02 -5.28e-02 -0.078447 -1.36e-03 7.63e-03
## 1496 -4.70e-03 1.41e-02 2.24e-02 1.33e-02 0.003777 -1.57e-01 6.90e-04
## 1497 -1.43e-02 2.79e-02 2.74e-02 2.89e-02 0.012097 8.05e-04 -3.20e-03
## 1504 -1.43e-02 2.79e-02 2.74e-02 2.89e-02 0.012097 8.05e-04 -3.20e-03
## 1513 1.58e-02 -4.28e-03 -8.16e-02 -1.28e-02 0.024835 1.62e-02 1.39e-03
## 1515 1.49e-02 -1.03e-02 -1.41e-02 -9.24e-02 0.033524 5.69e-03 3.15e-03
## 1534 1.58e-02 -4.28e-03 -8.16e-02 -1.28e-02 0.024835 1.62e-02 1.39e-03
## 1535 -1.53e-03 1.51e-03 3.55e-03 1.57e-03 0.001003 -1.75e-04 1.21e-04
## 1536 1.18e-02 3.09e-03 3.89e-03 -3.69e-02 0.009356 -1.86e-01 4.70e-04
## 1540 1.28e-02 -5.18e-03 -1.42e-02 -9.13e-02 0.029300 1.05e-02 6.91e-03
## 1551 -3.41e-03 8.00e-03 2.80e-03 5.06e-03 0.006277 3.85e-04 -5.36e-02
## 1555 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1557 1.28e-02 -5.18e-03 -1.42e-02 -9.13e-02 0.029300 1.05e-02 6.91e-03
## 1566 -1.55e-02 2.05e-02 3.53e-02 3.76e-02 -0.099905 8.39e-03 1.17e-02
## 1567 1.44e-02 1.45e-02 9.52e-04 -5.95e-02 0.023266 1.15e-03 -1.68e-03
## 1576 -1.55e-02 2.05e-02 3.53e-02 3.76e-02 -0.099905 8.39e-03 1.17e-02
## 1584 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1585 7.56e-03 -2.02e-01 -9.37e-03 -1.15e-02 0.009972 1.18e-02 3.38e-02
## 1590 1.87e-02 1.82e-02 8.78e-03 -6.85e-02 0.022450 -5.64e-03 2.91e-03
## 1594 -1.59e-01 3.25e-02 6.32e-02 5.59e-02 0.018001 5.60e-03 -1.01e-03
## 1595 -1.59e-01 3.25e-02 6.32e-02 5.59e-02 0.018001 5.60e-03 -1.01e-03
## 1603 1.58e-02 -4.28e-03 -8.16e-02 -1.28e-02 0.024835 1.62e-02 1.39e-03
## 1608 -6.21e-03 1.96e-02 4.26e-02 4.16e-02 -0.065012 -4.36e-03 1.05e-03
## 1609 -7.69e-03 1.31e-02 1.99e-02 1.60e-02 0.007566 1.26e-05 -1.15e-03
## 1615 -1.20e-02 2.09e-02 3.53e-02 2.85e-02 0.008956 -3.30e-03 5.18e-04
## 1616 1.28e-02 -5.18e-03 -1.42e-02 -9.13e-02 0.029300 1.05e-02 6.91e-03
## 1617 1.60e-02 -6.12e-03 -8.13e-03 -1.02e-01 0.027125 4.37e-03 1.39e-02
## 1620 3.07e-05 3.60e-04 1.91e-03 -3.95e-03 0.001811 8.17e-05 1.13e-04
## 1621 1.17e-02 4.92e-03 -2.87e-02 2.72e-03 0.014058 3.20e-03 -2.80e-03
## 1637 9.76e-03 -1.30e-01 1.19e-03 5.47e-03 0.013021 6.56e-03 1.13e-02
## 1638 -9.14e-03 2.59e-02 4.32e-02 2.49e-02 0.015878 2.37e-03 -1.80e-03
## 1650 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1654 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1665 -1.19e-01 -9.16e-03 -9.36e-02 -1.10e-03 0.023550 1.50e-02 -3.32e-03
## 1670 -7.69e-03 1.31e-02 1.99e-02 1.60e-02 0.007566 1.26e-05 -1.15e-03
## 1671 1.96e-02 -5.10e-03 -8.91e-02 -8.51e-03 0.022185 1.14e-02 7.28e-03
## 1675 3.84e-04 7.15e-04 -1.65e-03 -2.88e-04 0.002438 9.28e-05 1.52e-04
## 1688 1.17e-02 4.92e-03 -2.87e-02 2.72e-03 0.014058 3.20e-03 -2.80e-03
## 1691 1.60e-02 -6.12e-03 -8.13e-03 -1.02e-01 0.027125 4.37e-03 1.39e-02
## 1695 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1698 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1704 -1.26e-01 -1.04e-02 4.15e-03 -1.12e-01 0.029276 6.48e-03 4.42e-03
## 1705 -1.26e-01 -1.04e-02 4.15e-03 -1.12e-01 0.029276 6.48e-03 4.42e-03
## 1711 -1.19e-01 -9.16e-03 -9.36e-02 -1.10e-03 0.023550 1.50e-02 -3.32e-03
## 1719 -1.43e-02 2.79e-02 2.74e-02 2.89e-02 0.012097 8.05e-04 -3.20e-03
## 1723 8.44e-03 -1.04e-01 1.19e-02 3.15e-03 0.011476 3.76e-03 1.01e-02
## 1726 -6.32e-04 -1.17e-02 3.65e-03 -9.02e-04 0.002126 5.85e-04 1.34e-03
## 1749 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1752 1.50e-02 3.48e-03 -3.48e-02 9.07e-03 0.017965 4.35e-04 -6.18e-03
## 1754 -9.15e-03 3.40e-02 4.04e-02 4.54e-02 -0.082212 -1.23e-04 2.73e-03
## 1758 -1.43e-02 2.79e-02 2.74e-02 2.89e-02 0.012097 8.05e-04 -3.20e-03
## 1761 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1773 1.50e-02 3.48e-03 -3.48e-02 9.07e-03 0.017965 4.35e-04 -6.18e-03
## 1775 -1.59e-01 3.25e-02 6.32e-02 5.59e-02 0.018001 5.60e-03 -1.01e-03
## 1786 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1793 8.64e-02 -2.44e-02 -1.08e-01 -1.78e-02 -0.191524 -1.44e-04 1.77e-02
## 1799 -1.55e-02 2.05e-02 3.53e-02 3.76e-02 -0.099905 8.39e-03 1.17e-02
## 1803 4.27e-02 -1.59e-02 -1.01e-01 -2.11e-03 -0.150285 1.10e-02 2.14e-02
## 1806 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1807 1.98e-02 1.33e-02 -6.25e-02 1.40e-02 0.023306 1.97e-03 -1.08e-02
## 1808 1.58e-02 -4.28e-03 -8.16e-02 -1.28e-02 0.024835 1.62e-02 1.39e-03
## 1814 -2.59e-02 2.12e-02 3.13e-02 3.62e-02 0.020555 4.38e-03 -9.49e-04
## 1815 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1818 1.05e-02 4.76e-03 8.54e-03 -4.06e-02 0.016918 -4.20e-05 9.79e-05
## 1827 1.98e-02 1.33e-02 -6.25e-02 1.40e-02 0.023306 1.97e-03 -1.08e-02
## 1834 -2.23e-02 2.08e-02 2.54e-02 2.44e-02 0.016902 7.56e-03 2.11e-03
## 1835 -2.59e-02 2.12e-02 3.13e-02 3.62e-02 0.020555 4.38e-03 -9.49e-04
## 1843 1.28e-02 -5.18e-03 -1.42e-02 -9.13e-02 0.029300 1.05e-02 6.91e-03
## 1846 -6.99e-02 3.53e-02 7.07e-02 1.06e-01 -0.023194 1.87e-02 1.14e-02
## 1850 -1.43e-02 2.79e-02 2.74e-02 2.89e-02 0.012097 8.05e-04 -3.20e-03
## 1851 2.17e-02 1.85e-02 -6.20e-02 1.23e-02 0.018080 7.19e-04 -2.70e-03
## 1854 -1.19e-01 -9.16e-03 -9.36e-02 -1.10e-03 0.023550 1.50e-02 -3.32e-03
## 1859 -1.20e-02 2.09e-02 3.53e-02 2.85e-02 0.008956 -3.30e-03 5.18e-04
## 1861 1.96e-02 -5.10e-03 -8.91e-02 -8.51e-03 0.022185 1.14e-02 7.28e-03
## 1866 -1.43e-02 2.79e-02 2.74e-02 2.89e-02 0.012097 8.05e-04 -3.20e-03
## 1873 1.36e-02 -8.56e-02 6.12e-03 -9.29e-03 0.004892 -2.90e-01 8.94e-03
## 1875 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1885 -2.59e-02 2.12e-02 3.13e-02 3.62e-02 0.020555 4.38e-03 -9.49e-04
## 1892 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1895 1.96e-02 -5.10e-03 -8.91e-02 -8.51e-03 0.022185 1.14e-02 7.28e-03
## 1896 2.64e-02 9.37e-04 1.36e-02 -5.28e-02 -0.078447 -1.36e-03 7.63e-03
## 1897 -1.11e-02 4.38e-02 5.92e-02 6.42e-02 -0.112205 -6.85e-03 8.26e-03
## 1899 -9.14e-03 2.59e-02 4.32e-02 2.49e-02 0.015878 2.37e-03 -1.80e-03
## 1904 -1.19e-01 -9.16e-03 -9.36e-02 -1.10e-03 0.023550 1.50e-02 -3.32e-03
## 1905 1.60e-02 -6.12e-03 -8.13e-03 -1.02e-01 0.027125 4.37e-03 1.39e-02
## 1908 1.96e-02 -5.10e-03 -8.91e-02 -8.51e-03 0.022185 1.14e-02 7.28e-03
## 1916 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 1918 -2.59e-02 2.12e-02 3.13e-02 3.62e-02 0.020555 4.38e-03 -9.49e-04
## 1920 -1.19e-01 -9.16e-03 -9.36e-02 -1.10e-03 0.023550 1.50e-02 -3.32e-03
## 1930 1.49e-02 -1.03e-02 -1.41e-02 -9.24e-02 0.033524 5.69e-03 3.15e-03
## 1940 1.44e-02 1.45e-02 9.52e-04 -5.95e-02 0.023266 1.15e-03 -1.68e-03
## 1947 1.60e-02 -6.12e-03 -8.13e-03 -1.02e-01 0.027125 4.37e-03 1.39e-02
## 1949 1.17e-02 4.92e-03 -2.87e-02 2.72e-03 0.014058 3.20e-03 -2.80e-03
## 1951 -1.97e-02 3.92e-02 4.39e-02 4.49e-02 0.012127 -4.01e-03 -8.57e-04
## 1952 -1.43e-02 2.79e-02 2.74e-02 2.89e-02 0.012097 8.05e-04 -3.20e-03
## 1960 1.17e-02 4.92e-03 -2.87e-02 2.72e-03 0.014058 3.20e-03 -2.80e-03
## 9001 -2.83e-02 2.71e-02 3.97e-02 3.71e-02 0.015977 3.51e-03 7.21e-03
## 9012 -1.19e-01 -9.16e-03 -9.36e-02 -1.10e-03 0.023550 1.50e-02 -3.32e-03
## 9023 3.88e-02 -1.72e-02 -6.70e-03 -1.09e-01 -0.147614 2.77e-03 2.92e-02
## 9029 2.17e-02 1.85e-02 -6.20e-02 1.23e-02 0.018080 7.19e-04 -2.70e-03
## 6 -2.13e-02 -9.34e-03 -2.47e-02 7.32e-02 -0.026090 7.51e-03 -5.46e-03
## 12 -1.66e-02 -5.25e-03 -1.61e-03 3.41e-02 0.052828 1.44e-04 -3.89e-03
## 43 9.39e-02 -1.92e-02 -3.73e-02 -3.30e-02 -0.010617 -3.30e-03 5.93e-04
## 53 3.94e-04 7.81e-05 -2.60e-05 3.90e-04 0.000585 -1.54e-05 -6.74e-05
## 67 1.39e-02 -3.98e-02 -5.62e-02 -3.20e-02 -0.016741 4.37e-01 2.62e-03
## 79 -1.48e-02 -6.20e-03 3.62e-02 -3.43e-03 -0.017737 -4.04e-03 3.53e-03
## 122 1.55e-01 -3.17e-02 -6.17e-02 -5.46e-02 -0.017564 -5.46e-03 9.81e-04
## 126 -4.71e-02 -1.92e-02 9.68e-02 -2.25e-02 -0.041446 1.53e-03 9.71e-04
## 133 -1.25e-04 3.23e-05 5.65e-04 5.40e-05 -0.000141 -7.25e-05 -4.62e-05
## 138 2.04e-02 -4.16e-03 -8.09e-03 -7.16e-03 -0.002303 -7.16e-04 1.29e-04
## 154 -4.66e-02 9.51e-03 1.85e-02 1.64e-02 0.005270 1.64e-03 -2.94e-04
## 159 -7.25e-03 3.21e-03 1.25e-03 2.05e-02 0.027595 -5.18e-04 -5.46e-03
## 174 1.55e-01 -3.17e-02 -6.17e-02 -5.46e-02 -0.017564 -5.46e-03 9.81e-04
## 176 4.25e-02 3.26e-03 3.33e-02 3.93e-04 -0.008383 -5.33e-03 1.18e-03
## 181 3.86e-02 3.19e-03 -1.27e-03 3.44e-02 -0.008962 -1.98e-03 -1.35e-03
## 182 -5.13e-03 6.83e-02 -6.25e-04 -2.87e-03 -0.006846 -3.45e-03 -5.95e-03
## 186 -1.73e-02 8.74e-03 8.55e-02 4.66e-03 -0.027212 -1.17e-02 2.76e-03
## 189 -3.86e-02 -1.22e-02 -3.74e-03 7.93e-02 0.122815 3.35e-04 -9.04e-03
## 204 1.27e-02 -1.19e-02 -1.45e-02 -1.40e-02 -0.009666 -4.32e-03 -1.21e-03
## 215 1.27e-02 -1.19e-02 -1.45e-02 -1.40e-02 -0.009666 -4.32e-03 -1.21e-03
## 232 -2.17e-02 -2.11e-02 -1.02e-02 7.92e-02 -0.025983 6.52e-03 -3.37e-03
## 233 2.48e-03 -2.37e-03 -3.48e-03 -3.25e-03 -0.001398 -3.07e-04 -6.31e-04
## 252 -6.84e-02 -2.43e-03 -4.91e-02 1.23e-01 0.212215 1.71e-02 -2.90e-02
## 253 1.55e-01 -3.17e-02 -6.17e-02 -5.46e-02 -0.017564 -5.46e-03 9.81e-04
## 274 2.25e-02 -2.15e-02 -3.16e-02 -2.95e-02 -0.012709 -2.79e-03 -5.73e-03
## 275 5.41e-02 -4.43e-02 -6.53e-02 -7.54e-02 -0.042886 -9.14e-03 1.98e-03
## 287 -6.61e-03 5.98e-04 -3.12e-03 1.46e-03 0.001494 1.04e-05 -7.59e-04
## 288 9.33e-03 -1.86e-02 -2.08e-02 -2.13e-02 -0.005754 1.90e-03 4.07e-04
## 325 2.84e-03 -1.96e-03 -2.69e-03 -1.76e-02 0.006382 1.08e-03 5.99e-04
## 328 2.25e-02 -2.15e-02 -3.16e-02 -2.95e-02 -0.012709 -2.79e-03 -5.73e-03
## 344 1.21e-03 -2.41e-04 7.72e-04 -2.80e-04 -0.000234 -2.37e-05 1.69e-04
## 353 -9.00e-03 6.26e-03 1.99e-02 1.01e-02 0.000648 -5.17e-04 -9.65e-05
## 354 1.27e-02 -1.19e-02 -1.45e-02 -1.40e-02 -0.009666 -4.32e-03 -1.21e-03
## 367 -9.93e-03 3.68e-03 2.34e-02 4.90e-04 0.034928 -2.56e-03 -4.96e-03
## 369 -1.35e-02 3.50e-03 6.12e-02 5.85e-03 -0.015256 -7.85e-03 -5.00e-03
## 390 -2.57e-03 7.30e-02 6.07e-03 2.68e-03 -0.006937 -4.82e-03 -7.57e-03
## 392 -3.23e-02 8.78e-03 1.67e-01 2.62e-02 -0.050897 -3.32e-02 -2.85e-03
## 423 -9.90e-03 1.98e-03 1.75e-02 4.27e-03 -0.002836 -3.99e-04 -3.79e-04
## 432 -2.57e-03 7.30e-02 6.07e-03 2.68e-03 -0.006937 -4.82e-03 -7.57e-03
## 436 2.08e-02 -6.35e-03 -4.01e-02 -7.49e-03 0.009804 1.59e-03 -3.97e-03
## 483 -4.24e-03 1.71e-03 4.67e-03 3.01e-02 -0.009661 -3.46e-03 -2.28e-03
## 513 -3.22e-02 -3.12e-02 -1.51e-02 1.18e-01 -0.038555 9.68e-03 -5.00e-03
## 516 2.93e-03 4.54e-03 -8.82e-05 5.68e-05 -0.000152 -2.87e-04 -4.86e-04
## 518 -4.29e-03 -4.32e-03 -2.84e-04 1.77e-02 -0.006932 -3.41e-04 4.99e-04
## 520 3.55e-02 -4.70e-02 -8.08e-02 -8.60e-02 0.228714 -1.92e-02 -2.68e-02
## 526 1.55e-01 -3.17e-02 -6.17e-02 -5.46e-02 -0.017564 -5.46e-03 9.81e-04
## 528 -2.17e-02 -2.11e-02 -1.02e-02 7.92e-02 -0.025983 6.52e-03 -3.37e-03
## 553 6.55e-02 -6.13e-02 -7.47e-02 -7.19e-02 -0.049723 -2.22e-02 -6.21e-03
## 576 -9.47e-03 -4.47e-02 1.31e-02 6.44e-02 -0.059676 2.84e-03 5.09e-01
## 611 2.25e-02 -2.15e-02 -3.16e-02 -2.95e-02 -0.012709 -2.79e-03 -5.73e-03
## 625 -2.16e-02 5.62e-03 9.83e-02 9.38e-03 -0.024475 -1.26e-02 -8.03e-03
## 635 -2.60e-02 -7.87e-03 4.27e-02 -1.29e-02 0.080853 6.83e-04 -4.94e-03
## 646 -6.03e-03 -5.31e-03 1.99e-02 -1.85e-03 -0.007073 -2.19e-03 2.18e-03
## 657 9.46e-03 -2.39e-02 -4.89e-02 -3.66e-02 -0.018089 3.34e-03 6.45e-03
## 659 -9.75e-04 -7.36e-04 -1.46e-04 3.45e-03 -0.001571 2.20e-04 3.34e-04
## 666 2.48e-03 -2.37e-03 -3.48e-03 -3.25e-03 -0.001398 -3.07e-04 -6.31e-04
## 679 4.54e-03 -1.73e-03 -2.30e-03 -2.87e-02 0.007675 1.24e-03 3.95e-03
## 729 1.33e-02 1.02e-03 1.04e-02 1.23e-04 -0.002620 -1.67e-03 3.70e-04
## 755 7.29e-03 1.10e-02 -2.61e-03 -1.32e-03 -0.000493 8.69e-05 -7.49e-04
## 758 -2.17e-02 -2.11e-02 -1.02e-02 7.92e-02 -0.025983 6.52e-03 -3.37e-03
## 770 4.56e-02 -4.35e-02 -6.40e-02 -5.97e-02 -0.025715 -5.65e-03 -1.16e-02
## 786 1.92e-03 -5.14e-02 -2.38e-03 -2.92e-03 0.002535 3.00e-03 8.58e-03
## 797 3.93e-02 -7.18e-03 -8.51e-03 2.78e-02 -0.009050 2.13e-03 2.72e-03
## 811 3.79e-03 -4.71e-02 3.18e-03 4.17e-03 0.002294 -1.85e-04 5.92e-03
## 834 4.56e-02 -4.35e-02 -6.40e-02 -5.97e-02 -0.025715 -5.65e-03 -1.16e-02
## 858 1.29e-02 -9.89e-02 -3.11e-03 -1.69e-03 -0.078366 4.43e-03 2.18e-02
## 885 9.39e-02 -1.92e-02 -3.73e-02 -3.30e-02 -0.010617 -3.30e-03 5.93e-04
## 893 -2.57e-03 7.30e-02 6.07e-03 2.68e-03 -0.006937 -4.82e-03 -7.57e-03
## 927 -4.43e-02 -8.79e-03 2.93e-03 -4.39e-02 -0.065785 1.73e-03 7.58e-03
## 928 2.43e-02 -5.85e-03 4.04e-04 -6.48e-02 0.010181 -3.12e-03 4.75e-03
## 933 6.69e-03 -3.95e-03 -2.54e-03 -1.83e-02 -0.023936 8.17e-04 2.95e-03
## 951 4.25e-02 3.26e-03 3.33e-02 3.93e-04 -0.008383 -5.33e-03 1.18e-03
## 968 1.74e-02 -3.38e-02 -3.32e-02 -3.51e-02 -0.014692 -9.78e-04 3.89e-03
## 972 -2.33e-03 8.87e-04 1.18e-03 1.47e-02 -0.003932 -6.34e-04 -2.02e-03
## 975 -6.07e-03 1.85e-01 1.77e-02 1.77e-02 -0.017162 -1.93e-02 -2.48e-02
## 977 -1.25e-02 7.39e-03 4.76e-03 3.43e-02 0.044829 -1.53e-03 -5.52e-03
## 981 -2.48e-02 9.41e-03 6.56e-02 5.90e-03 0.083886 -1.20e-02 -8.79e-03
## 986 1.43e-02 -8.43e-03 -5.43e-03 -3.92e-02 -0.051111 1.74e-03 6.30e-03
## 1002 -8.60e-03 -3.50e-03 1.77e-02 -4.12e-03 -0.007576 2.80e-04 1.78e-04
## 1007 1.55e-01 -3.17e-02 -6.17e-02 -5.46e-02 -0.017564 -5.46e-03 9.81e-04
## 1011 -9.87e-03 3.77e-03 5.00e-03 6.25e-02 -0.016696 -2.69e-03 -8.58e-03
## 1035 -9.06e-03 1.18e-01 -2.44e-03 -1.27e-02 -0.012126 -8.39e-04 -6.25e-03
## 1050 -2.10e-03 -1.41e-03 6.63e-03 -1.48e-03 -0.002474 -2.10e-04 1.15e-03
## 1056 -1.98e-02 1.37e-02 1.88e-02 1.23e-01 -0.044552 -7.56e-03 -4.18e-03
## 1057 1.27e-02 -1.19e-02 -1.45e-02 -1.40e-02 -0.009666 -4.32e-03 -1.21e-03
## 1075 4.25e-02 3.26e-03 3.33e-02 3.93e-04 -0.008383 -5.33e-03 1.18e-03
## 1080 -4.80e-03 1.37e-01 1.14e-02 5.01e-03 -0.012976 -9.02e-03 -1.42e-02
## 1125 2.04e-02 -4.16e-03 -8.09e-03 -7.16e-03 -0.002303 -7.16e-04 1.29e-04
## 1131 -3.78e-03 9.81e-04 1.71e-02 1.64e-03 -0.004272 -2.20e-03 -1.40e-03
## 1138 -6.50e-03 7.00e-03 2.14e-02 -1.31e-03 -0.002974 8.66e-04 3.49e-03
## 1150 -1.62e-02 6.18e-03 8.20e-03 1.02e-01 -0.027376 -4.41e-03 -1.41e-02
## 1163 1.94e-02 -1.59e-02 -2.34e-02 -2.71e-02 -0.015384 -3.28e-03 7.10e-04
## 1169 1.27e-02 -1.19e-02 -1.45e-02 -1.40e-02 -0.009666 -4.32e-03 -1.21e-03
## 1198 4.28e-02 -8.55e-03 2.73e-02 -9.90e-03 -0.008282 -8.39e-04 5.99e-03
## 1204 1.99e-02 -3.13e-02 -5.36e-02 -4.92e-02 -0.020513 4.31e-03 6.32e-03
## 1218 3.94e-04 7.81e-05 -2.60e-05 3.90e-04 0.000585 -1.54e-05 -6.74e-05
## 1230 4.25e-02 3.26e-03 3.33e-02 3.93e-04 -0.008383 -5.33e-03 1.18e-03
## 1236 -9.87e-03 3.77e-03 5.00e-03 6.25e-02 -0.016696 -2.69e-03 -8.58e-03
## 1247 4.25e-02 3.26e-03 3.33e-02 3.93e-04 -0.008383 -5.33e-03 1.18e-03
## 1259 3.86e-02 3.19e-03 -1.27e-03 3.44e-02 -0.008962 -1.98e-03 -1.35e-03
## 1294 6.55e-02 -6.13e-02 -7.47e-02 -7.19e-02 -0.049723 -2.22e-02 -6.21e-03
## 1353 6.54e-02 -6.25e-02 -9.18e-02 -8.58e-02 -0.036926 -8.11e-03 -1.67e-02
## 1370 6.16e-03 -3.09e-03 -1.58e-02 2.49e-04 -0.020417 1.88e-03 1.35e-03
## 1427 -2.40e-02 1.42e-02 9.12e-03 6.57e-02 0.085835 -2.93e-03 -1.06e-02
## 1445 7.08e-02 -1.18e-02 -3.28e-02 -3.14e-02 0.069105 -1.80e-03 -5.22e-03
## 1460 -1.35e-02 3.50e-03 6.12e-02 5.85e-03 -0.015256 -7.85e-03 -5.00e-03
## 1480 -1.25e-04 3.23e-05 5.65e-04 5.40e-05 -0.000141 -7.25e-05 -4.62e-05
## 1505 2.93e-03 4.54e-03 -8.82e-05 5.68e-05 -0.000152 -2.87e-04 -4.86e-04
## 1543 -1.25e-02 7.39e-03 4.76e-03 3.43e-02 0.044829 -1.53e-03 -5.52e-03
## 1548 -7.40e-03 1.51e-03 2.94e-03 2.60e-03 0.000836 2.60e-04 -4.67e-05
## 1550 -7.60e-02 -4.03e-03 1.08e-01 -2.24e-02 0.212877 -9.05e-03 -8.72e-03
## 1561 -3.98e-02 -7.84e-03 -3.66e-02 2.80e-03 -0.064013 5.09e-03 4.04e-03
## 1564 -2.19e-02 8.12e-03 5.17e-02 1.08e-03 0.076976 -5.65e-03 -1.09e-02
## 1573 -5.49e-02 -4.83e-02 1.81e-01 -1.68e-02 -0.064387 -1.99e-02 1.98e-02
## 1575 2.66e-02 -2.49e-02 -3.04e-02 -2.92e-02 -0.020211 -9.04e-03 -2.53e-03
## 1599 -9.75e-04 -7.36e-04 -1.46e-04 3.45e-03 -0.001571 2.20e-04 3.34e-04
## 1622 1.91e-02 -5.40e-02 -9.02e-02 -5.19e-02 -0.033119 -4.94e-03 3.76e-03
## 1629 -2.72e-03 3.61e-02 -3.30e-04 -1.52e-03 -0.003621 -1.82e-03 -3.15e-03
## 1664 -9.93e-03 3.68e-03 2.34e-02 4.90e-04 0.034928 -2.56e-03 -4.96e-03
## 1669 -6.08e-02 -1.06e-01 1.58e-02 1.83e-02 -0.099330 -1.01e-03 1.31e-02
## 1674 -1.01e-02 7.56e-03 3.60e-02 5.98e-03 -0.004168 -3.67e-03 1.81e-03
## 1682 4.56e-02 -4.35e-02 -6.40e-02 -5.97e-02 -0.025715 -5.65e-03 -1.16e-02
## 1685 -1.17e-02 1.55e-01 -1.42e-03 -6.54e-03 -0.015570 -7.85e-03 -1.35e-02
## 1697 2.07e-02 -4.13e-02 -4.64e-02 -4.74e-02 -0.012795 4.23e-03 9.04e-04
## 1716 -1.05e-02 8.11e-04 4.10e-03 -7.68e-03 0.002743 -7.62e-04 -4.59e-04
## 1730 4.67e-03 -1.73e-03 -1.10e-02 -2.31e-04 -0.016444 1.21e-03 2.34e-03
## 1731 2.48e-03 -2.37e-03 -3.48e-03 -3.25e-03 -0.001398 -3.07e-04 -6.31e-04
## 1732 3.86e-02 3.19e-03 -1.27e-03 3.44e-02 -0.008962 -1.98e-03 -1.35e-03
## 1743 2.84e-03 -1.96e-03 -2.69e-03 -1.76e-02 0.006382 1.08e-03 5.99e-04
## 1751 3.79e-03 -4.71e-02 3.18e-03 4.17e-03 0.002294 -1.85e-04 5.92e-03
## 1757 1.94e-02 -1.59e-02 -2.34e-02 -2.71e-02 -0.015384 -3.28e-03 7.10e-04
## 1763 -9.87e-03 3.77e-03 5.00e-03 6.25e-02 -0.016696 -2.69e-03 -8.58e-03
## 1766 -3.02e-02 -7.04e-03 7.03e-02 -1.83e-02 -0.036300 -8.78e-04 1.25e-02
## 1772 -2.79e-03 5.09e-04 6.04e-04 -1.97e-03 0.000642 -1.51e-04 -1.93e-04
## 1776 2.66e-02 -2.49e-02 -3.04e-02 -2.92e-02 -0.020211 -9.04e-03 -2.53e-03
## 1782 -1.62e-02 9.04e-02 4.02e-03 1.42e-02 -0.003491 -4.29e-03 -9.01e-03
## 1784 -6.07e-03 1.85e-01 1.77e-02 1.77e-02 -0.017162 -1.93e-02 -2.48e-02
## 1791 2.25e-02 -2.15e-02 -3.16e-02 -2.95e-02 -0.012709 -2.79e-03 -5.73e-03
## 1831 9.15e-03 7.55e-04 -3.01e-04 8.14e-03 -0.002122 -4.70e-04 -3.20e-04
## 1840 9.39e-02 -1.92e-02 -3.73e-02 -3.30e-02 -0.010617 -3.30e-03 5.93e-04
## 1844 3.86e-02 3.19e-03 -1.27e-03 3.44e-02 -0.008962 -1.98e-03 -1.35e-03
## 1856 1.33e-02 1.02e-03 1.04e-02 1.23e-04 -0.002620 -1.67e-03 3.70e-04
## 1876 -2.19e-02 8.12e-03 5.17e-02 1.08e-03 0.076976 -5.65e-03 -1.09e-02
## 1929 2.86e-02 -2.35e-02 -3.46e-02 -4.00e-02 -0.022710 -4.84e-03 1.05e-03
## 1935 9.15e-03 7.55e-04 -3.01e-04 8.14e-03 -0.002122 -4.70e-04 -3.20e-04
## 1938 -3.84e-03 4.72e-02 -5.39e-03 -1.43e-03 -0.005217 -1.71e-03 -4.61e-03
## 1941 -1.35e-02 3.50e-03 6.12e-02 5.85e-03 -0.015256 -7.85e-03 -5.00e-03
## 1954 -5.03e-05 1.91e-05 1.33e-04 1.20e-05 0.000170 -2.43e-05 -1.78e-05
## 1959 -4.16e-02 -3.43e-03 1.37e-03 -3.70e-02 0.009650 2.14e-03 1.46e-03
## 9010 3.76e-04 -1.51e-04 -4.14e-04 -2.67e-03 0.000857 3.07e-04 2.02e-04
## dfb.seis dfb.dosy dfb.trey dfb.very dfb.aver dfb.hpvr dfb.und
## 4 2.78e-03 2.58e-02 4.34e-02 -1.47e-02 -3.48e-03 -6.10e-02 3.22e-03
## 5 -3.57e-03 -1.73e-02 -8.45e-02 1.49e-02 7.62e-03 -2.53e-02 -4.52e-04
## 11 7.70e-03 3.10e-02 6.65e-02 1.16e-03 -2.93e-03 -5.86e-02 -4.45e-03
## 16 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 23 -6.60e-02 -2.10e-03 4.57e-04 7.33e-04 -7.11e-03 1.36e-03 4.35e-02
## 29 1.06e-03 -7.74e-02 4.41e-03 -2.56e-02 -9.27e-04 -4.07e-03 4.91e-03
## 44 -3.34e-02 -1.12e-03 3.04e-04 -1.09e-04 -4.03e-03 5.59e-04 2.24e-02
## 45 1.56e-02 3.45e-02 3.69e-02 -1.83e-02 -1.31e-02 -6.25e-02 -2.91e-04
## 47 -3.43e-03 6.20e-03 5.58e-03 1.11e-01 9.68e-02 1.15e-01 1.89e-03
## 49 -3.97e-03 -3.85e-02 -2.15e-03 -8.45e-03 7.76e-04 2.35e-04 4.94e-03
## 50 1.27e-02 3.91e-02 2.63e-02 1.10e-01 9.75e-02 1.23e-01 -1.00e-03
## 55 -3.57e-03 -1.73e-02 -8.45e-02 1.49e-02 7.62e-03 -2.53e-02 -4.52e-04
## 64 4.54e-03 8.96e-03 1.87e-02 6.87e-03 8.11e-03 -4.94e-02 -6.09e-03
## 80 1.88e-03 -1.22e-01 -5.10e-03 1.69e-02 5.04e-04 -4.86e-02 -9.51e-04
## 86 -3.57e-03 -1.73e-02 -8.45e-02 1.49e-02 7.62e-03 -2.53e-02 -4.52e-04
## 93 1.06e-02 6.32e-02 8.14e-02 -5.92e-02 4.75e-03 5.02e-03 -4.41e-03
## 108 2.05e-02 3.58e-02 3.50e-02 -1.82e-02 -1.07e-01 -9.04e-03 -8.41e-04
## 114 -5.01e-02 -2.61e-03 8.43e-04 2.22e-04 2.64e-03 -3.03e-03 3.29e-02
## 115 -5.76e-04 -5.47e-02 3.72e-03 -2.20e-02 -4.50e-03 -3.95e-03 7.34e-03
## 116 1.26e-02 5.02e-02 4.17e-02 -6.52e-02 -1.26e-02 -9.17e-03 7.04e-03
## 123 -5.76e-04 -5.47e-02 3.72e-03 -2.20e-02 -4.50e-03 -3.95e-03 7.34e-03
## 127 -5.76e-04 -5.47e-02 3.72e-03 -2.20e-02 -4.50e-03 -3.95e-03 7.34e-03
## 129 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 134 1.82e-03 4.37e-02 4.76e-02 -6.37e-02 -4.68e-03 -6.98e-03 1.02e-02
## 137 7.29e-03 -2.46e-04 -9.90e-02 5.76e-03 -9.80e-04 -4.23e-02 -2.93e-03
## 139 -5.76e-04 -5.47e-02 3.72e-03 -2.20e-02 -4.50e-03 -3.95e-03 7.34e-03
## 147 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 151 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 153 -3.65e-03 1.14e-02 -6.81e-02 -3.49e-02 4.63e-03 4.11e-03 6.25e-03
## 155 -1.73e-03 -9.00e-03 -1.17e-01 2.10e-02 1.82e-02 -4.63e-02 -9.12e-03
## 162 5.72e-03 8.41e-03 1.51e-02 -2.22e-03 -7.33e-02 -1.55e-05 1.29e-03
## 163 -6.79e-03 -6.21e-02 6.57e-03 -2.14e-02 -1.23e-04 -2.80e-03 9.39e-03
## 165 -1.05e-04 -5.51e-03 -7.37e-03 7.04e-03 2.13e-03 -3.40e-03 -1.49e-03
## 168 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 170 -7.50e-04 -9.00e-03 -9.99e-02 2.75e-02 1.00e-02 -3.80e-02 -7.26e-03
## 172 -2.94e-03 -5.90e-02 -7.26e-03 9.19e-03 -3.36e-02 7.88e-04 3.34e-03
## 184 2.78e-03 2.58e-02 4.34e-02 -1.47e-02 -3.48e-03 -6.10e-02 3.22e-03
## 187 -2.10e-03 -1.11e-04 -7.80e-02 -1.81e-02 1.19e-02 9.79e-03 -3.92e-04
## 192 -6.89e-03 -8.56e-02 1.85e-02 -1.94e-02 4.38e-04 2.69e-03 8.44e-03
## 194 -2.54e-02 -3.29e-04 9.27e-04 -2.20e-03 1.25e-03 4.69e-04 1.70e-02
## 210 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 217 2.16e-04 -1.53e-02 -1.02e-02 4.55e-03 -8.47e-02 5.26e-03 -7.08e-04
## 220 -5.25e-04 -3.17e-03 -3.79e-03 2.37e-03 -2.08e-04 -2.24e-03 5.74e-04
## 224 3.65e-03 -2.59e-03 -1.42e-01 1.28e-01 1.02e-01 1.27e-01 -4.30e-03
## 227 9.86e-03 1.16e-02 1.78e-02 5.49e-03 -8.47e-02 2.19e-03 -6.24e-03
## 228 -3.65e-03 1.14e-02 -6.81e-02 -3.49e-02 4.63e-03 4.11e-03 6.25e-03
## 239 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 241 1.56e-02 3.45e-02 3.69e-02 -1.83e-02 -1.31e-02 -6.25e-02 -2.91e-04
## 245 -3.97e-03 -3.85e-02 -2.15e-03 -8.45e-03 7.76e-04 2.35e-04 4.94e-03
## 249 1.56e-02 3.45e-02 3.69e-02 -1.83e-02 -1.31e-02 -6.25e-02 -2.91e-04
## 262 -1.00e-02 -3.93e-04 2.97e-05 -6.78e-04 5.43e-04 2.88e-04 6.67e-03
## 265 1.56e-02 3.45e-02 3.69e-02 -1.83e-02 -1.31e-02 -6.25e-02 -2.91e-04
## 267 -2.82e-03 -5.75e-04 -5.42e-02 -1.69e-02 4.49e-03 5.77e-03 3.70e-03
## 269 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 271 -1.17e-03 1.51e-02 -7.27e-02 -3.00e-02 6.62e-03 1.40e-02 1.65e-03
## 277 -3.43e-03 6.20e-03 5.58e-03 1.11e-01 9.68e-02 1.15e-01 1.89e-03
## 290 -6.10e-03 -7.22e-02 -8.83e-03 1.14e-02 2.09e-03 -2.12e-02 4.25e-03
## 292 3.26e-03 2.79e-02 2.58e-02 -4.24e-02 5.06e-03 8.42e-04 2.00e-03
## 293 -4.02e-02 -3.09e-03 -7.62e-04 1.50e-03 2.85e-03 -1.88e-03 2.59e-02
## 295 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 299 -3.43e-03 6.20e-03 5.58e-03 1.11e-01 9.68e-02 1.15e-01 1.89e-03
## 320 2.78e-03 2.58e-02 4.34e-02 -1.47e-02 -3.48e-03 -6.10e-02 3.22e-03
## 321 -2.27e-02 2.67e-05 6.50e-04 -2.15e-03 8.01e-04 3.27e-04 1.54e-02
## 324 5.35e-03 1.73e-02 -6.95e-02 -3.64e-02 -2.17e-03 2.00e-03 3.67e-03
## 334 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 351 1.82e-02 6.20e-02 4.91e-02 -6.99e-02 -6.71e-03 -8.67e-03 -1.94e-04
## 355 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 361 5.21e-03 5.37e-02 5.59e-02 -6.73e-02 3.16e-03 -5.90e-03 3.36e-03
## 362 -3.97e-03 -3.85e-02 -2.15e-03 -8.45e-03 7.76e-04 2.35e-04 4.94e-03
## 366 -4.30e-04 -9.37e-02 -3.00e-03 7.07e-03 -5.06e-03 -3.87e-02 4.80e-03
## 370 -2.01e-03 1.54e-02 -8.75e-02 -3.61e-02 1.35e-02 7.39e-03 1.53e-04
## 374 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 378 2.11e-03 7.26e-03 3.62e-03 -1.79e-02 -4.29e-03 -4.62e-03 1.44e-03
## 381 5.72e-03 8.41e-03 1.51e-02 -2.22e-03 -7.33e-02 -1.55e-05 1.29e-03
## 382 6.12e-03 5.33e-02 7.15e-02 -6.04e-02 -4.66e-03 1.82e-03 5.07e-03
## 383 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 384 -6.79e-03 -6.21e-02 6.57e-03 -2.14e-02 -1.23e-04 -2.80e-03 9.39e-03
## 400 -2.01e-03 1.54e-02 -8.75e-02 -3.61e-02 1.35e-02 7.39e-03 1.53e-04
## 403 -6.79e-03 -6.21e-02 6.57e-03 -2.14e-02 -1.23e-04 -2.80e-03 9.39e-03
## 409 1.27e-02 3.91e-02 2.63e-02 1.10e-01 9.75e-02 1.23e-01 -1.00e-03
## 412 9.73e-01 -2.41e-01 9.77e-04 -2.13e-02 -1.09e-03 -8.76e-03 -1.51e+00
## 413 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 416 -4.49e-02 -1.73e-03 3.83e-04 -7.26e-05 1.73e-03 -2.97e-03 2.98e-02
## 418 2.74e-03 -1.71e-02 -1.01e-01 2.39e-02 -6.24e-02 1.26e-02 -7.64e-03
## 422 -3.97e-03 -3.85e-02 -2.15e-03 -8.45e-03 7.76e-04 2.35e-04 4.94e-03
## 435 3.74e-03 -3.30e-04 9.32e-03 2.25e-02 4.12e-03 -7.00e-03 -9.42e-03
## 439 3.26e-03 2.79e-02 2.58e-02 -4.24e-02 5.06e-03 8.42e-04 2.00e-03
## 445 -1.73e-02 -1.46e-01 -1.33e-02 1.16e-01 8.89e-02 9.94e-02 9.62e-03
## 447 -6.79e-03 -6.21e-02 6.57e-03 -2.14e-02 -1.23e-04 -2.80e-03 9.39e-03
## 448 2.78e-03 2.58e-02 4.34e-02 -1.47e-02 -3.48e-03 -6.10e-02 3.22e-03
## 449 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 478 2.05e-02 3.58e-02 3.50e-02 -1.82e-02 -1.07e-01 -9.04e-03 -8.41e-04
## 482 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 486 5.72e-03 8.41e-03 1.51e-02 -2.22e-03 -7.33e-02 -1.55e-05 1.29e-03
## 489 8.70e-04 -8.28e-04 7.04e-03 1.00e-02 -2.03e-03 -6.06e-03 -1.46e-03
## 490 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 491 -2.88e-03 2.84e-02 3.35e-02 1.17e-01 1.11e-01 1.28e-01 3.19e-03
## 492 7.70e-03 3.10e-02 6.65e-02 1.16e-03 -2.93e-03 -5.86e-02 -4.45e-03
## 503 5.21e-03 5.37e-02 5.59e-02 -6.73e-02 3.16e-03 -5.90e-03 3.36e-03
## 508 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 509 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 512 1.56e-02 3.45e-02 3.69e-02 -1.83e-02 -1.31e-02 -6.25e-02 -2.91e-04
## 515 -1.18e-03 -6.20e-04 -8.26e-04 -6.30e-03 -7.38e-05 -7.65e-04 1.15e-03
## 517 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 532 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 533 -3.57e-03 -1.73e-02 -8.45e-02 1.49e-02 7.62e-03 -2.53e-02 -4.52e-04
## 535 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 537 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 538 8.54e-03 2.77e-02 4.11e-02 -1.48e-02 -1.01e-01 -6.50e-03 2.45e-03
## 543 -4.30e-04 -9.37e-02 -3.00e-03 7.07e-03 -5.06e-03 -3.87e-02 4.80e-03
## 547 3.26e-03 2.79e-02 2.58e-02 -4.24e-02 5.06e-03 8.42e-04 2.00e-03
## 550 3.26e-03 2.79e-02 2.58e-02 -4.24e-02 5.06e-03 8.42e-04 2.00e-03
## 558 -3.97e-03 -3.85e-02 -2.15e-03 -8.45e-03 7.76e-04 2.35e-04 4.94e-03
## 571 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 578 2.78e-03 2.58e-02 4.34e-02 -1.47e-02 -3.48e-03 -6.10e-02 3.22e-03
## 583 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 586 -3.43e-03 6.20e-03 5.58e-03 1.11e-01 9.68e-02 1.15e-01 1.89e-03
## 594 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 597 -1.00e-02 -3.93e-04 2.97e-05 -6.78e-04 5.43e-04 2.88e-04 6.67e-03
## 602 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 603 1.26e-02 5.02e-02 4.17e-02 -6.52e-02 -1.26e-02 -9.17e-03 7.04e-03
## 604 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 612 9.86e-03 1.16e-02 1.78e-02 5.49e-03 -8.47e-02 2.19e-03 -6.24e-03
## 613 1.27e-02 3.91e-02 2.63e-02 1.10e-01 9.75e-02 1.23e-01 -1.00e-03
## 621 -6.79e-03 -6.21e-02 6.57e-03 -2.14e-02 -1.23e-04 -2.80e-03 9.39e-03
## 627 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 630 2.11e-03 7.26e-03 3.62e-03 -1.79e-02 -4.29e-03 -4.62e-03 1.44e-03
## 631 -3.97e-03 -3.85e-02 -2.15e-03 -8.45e-03 7.76e-04 2.35e-04 4.94e-03
## 632 -5.76e-04 -5.47e-02 3.72e-03 -2.20e-02 -4.50e-03 -3.95e-03 7.34e-03
## 639 1.06e-03 -7.74e-02 4.41e-03 -2.56e-02 -9.27e-04 -4.07e-03 4.91e-03
## 645 5.72e-03 8.41e-03 1.51e-02 -2.22e-03 -7.33e-02 -1.55e-05 1.29e-03
## 647 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 648 -5.76e-04 -5.47e-02 3.72e-03 -2.20e-02 -4.50e-03 -3.95e-03 7.34e-03
## 651 1.16e-02 2.60e-03 -9.10e-02 4.49e-03 -8.19e-02 3.42e-03 -3.18e-03
## 655 8.54e-03 2.77e-02 4.11e-02 -1.48e-02 -1.01e-01 -6.50e-03 2.45e-03
## 667 2.78e-03 2.58e-02 4.34e-02 -1.47e-02 -3.48e-03 -6.10e-02 3.22e-03
## 670 -2.27e-02 2.67e-05 6.50e-04 -2.15e-03 8.01e-04 3.27e-04 1.54e-02
## 671 1.56e-02 3.45e-02 3.69e-02 -1.83e-02 -1.31e-02 -6.25e-02 -2.91e-04
## 673 -2.82e-03 -5.75e-04 -5.42e-02 -1.69e-02 4.49e-03 5.77e-03 3.70e-03
## 701 -7.50e-04 -9.00e-03 -9.99e-02 2.75e-02 1.00e-02 -3.80e-02 -7.26e-03
## 705 1.56e-02 3.45e-02 3.69e-02 -1.83e-02 -1.31e-02 -6.25e-02 -2.91e-04
## 706 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 709 5.72e-03 8.41e-03 1.51e-02 -2.22e-03 -7.33e-02 -1.55e-05 1.29e-03
## 717 9.76e-03 8.76e-03 6.89e-03 -1.30e-02 1.29e-03 -7.00e-02 -3.43e-03
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## 1319 -6.79e-03 -6.21e-02 6.57e-03 -2.14e-02 -1.23e-04 -2.80e-03 9.39e-03
## 1322 -3.57e-03 -1.73e-02 -8.45e-02 1.49e-02 7.62e-03 -2.53e-02 -4.52e-04
## 1324 -3.42e-01 5.36e-03 -1.24e-02 5.47e-02 3.86e-02 -6.35e-02 -3.60e-01
## 1327 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 1328 -1.49e-02 -1.09e-03 -2.44e-04 3.27e-04 -1.46e-03 4.63e-04 9.79e-03
## 1330 9.86e-03 1.16e-02 1.78e-02 5.49e-03 -8.47e-02 2.19e-03 -6.24e-03
## 1332 -3.65e-03 1.14e-02 -6.81e-02 -3.49e-02 4.63e-03 4.11e-03 6.25e-03
## 1333 -3.06e-03 -1.59e-01 -2.29e-02 1.40e-01 1.02e-01 1.15e-01 -1.81e-03
## 1336 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 1341 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 1344 -7.50e-04 -9.00e-03 -9.99e-02 2.75e-02 1.00e-02 -3.80e-02 -7.26e-03
## 1352 5.72e-03 8.41e-03 1.51e-02 -2.22e-03 -7.33e-02 -1.55e-05 1.29e-03
## 1358 -2.82e-03 -5.75e-04 -5.42e-02 -1.69e-02 4.49e-03 5.77e-03 3.70e-03
## 1359 5.35e-03 1.73e-02 -6.95e-02 -3.64e-02 -2.17e-03 2.00e-03 3.67e-03
## 1361 9.76e-03 8.76e-03 6.89e-03 -1.30e-02 1.29e-03 -7.00e-02 -3.43e-03
## 1364 -4.30e-04 -9.37e-02 -3.00e-03 7.07e-03 -5.06e-03 -3.87e-02 4.80e-03
## 1368 -6.16e-03 -2.00e-02 -1.12e-02 5.92e-03 1.45e-02 -4.93e-02 -1.30e-04
## 1384 9.80e-06 8.61e-03 3.54e-03 -1.72e-02 6.98e-04 1.19e-02 1.59e-03
## 1390 -5.76e-04 -5.47e-02 3.72e-03 -2.20e-02 -4.50e-03 -3.95e-03 7.34e-03
## 1393 -3.57e-03 -1.73e-02 -8.45e-02 1.49e-02 7.62e-03 -2.53e-02 -4.52e-04
## 1394 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 1402 -5.76e-04 -5.47e-02 3.72e-03 -2.20e-02 -4.50e-03 -3.95e-03 7.34e-03
## 1407 -1.17e-03 1.51e-02 -7.27e-02 -3.00e-02 6.62e-03 1.40e-02 1.65e-03
## 1408 2.78e-03 2.58e-02 4.34e-02 -1.47e-02 -3.48e-03 -6.10e-02 3.22e-03
## 1412 1.26e-02 5.02e-02 4.17e-02 -6.52e-02 -1.26e-02 -9.17e-03 7.04e-03
## 1413 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 1416 -2.94e-03 -5.90e-02 -7.26e-03 9.19e-03 -3.36e-02 7.88e-04 3.34e-03
## 1417 -4.30e-04 -9.37e-02 -3.00e-03 7.07e-03 -5.06e-03 -3.87e-02 4.80e-03
## 1418 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 1419 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 1420 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 1423 -3.65e-03 1.14e-02 -6.81e-02 -3.49e-02 4.63e-03 4.11e-03 6.25e-03
## 1424 -3.43e-03 6.20e-03 5.58e-03 1.11e-01 9.68e-02 1.15e-01 1.89e-03
## 1432 -1.00e-02 -3.93e-04 2.97e-05 -6.78e-04 5.43e-04 2.88e-04 6.67e-03
## 1433 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 1437 1.82e-03 4.37e-02 4.76e-02 -6.37e-02 -4.68e-03 -6.98e-03 1.02e-02
## 1438 1.26e-02 5.02e-02 4.17e-02 -6.52e-02 -1.26e-02 -9.17e-03 7.04e-03
## 1439 1.26e-02 5.02e-02 4.17e-02 -6.52e-02 -1.26e-02 -9.17e-03 7.04e-03
## 1446 2.05e-02 3.58e-02 3.50e-02 -1.82e-02 -1.07e-01 -9.04e-03 -8.41e-04
## 1450 1.88e-03 -1.22e-01 -5.10e-03 1.69e-02 5.04e-04 -4.86e-02 -9.51e-04
## 1451 -2.27e-02 2.67e-05 6.50e-04 -2.15e-03 8.01e-04 3.27e-04 1.54e-02
## 1452 -3.57e-03 -1.73e-02 -8.45e-02 1.49e-02 7.62e-03 -2.53e-02 -4.52e-04
## 1453 -1.85e-03 -1.37e-02 -9.40e-03 1.43e-02 4.87e-03 -2.61e-02 -4.93e-03
## 1456 -3.97e-03 -3.85e-02 -2.15e-03 -8.45e-03 7.76e-04 2.35e-04 4.94e-03
## 1464 8.54e-03 2.77e-02 4.11e-02 -1.48e-02 -1.01e-01 -6.50e-03 2.45e-03
## 1469 2.16e-04 -1.53e-02 -1.02e-02 4.55e-03 -8.47e-02 5.26e-03 -7.08e-04
## 1473 -1.34e-02 -1.19e-01 -2.47e-02 9.35e-02 6.76e-02 7.76e-02 6.55e-03
## 1481 -5.76e-04 -5.47e-02 3.72e-03 -2.20e-02 -4.50e-03 -3.95e-03 7.34e-03
## 1482 -7.61e-03 -8.68e-02 8.33e-03 -2.40e-02 5.56e-03 -2.31e-03 7.43e-03
## 1496 -1.94e-03 -8.46e-03 -5.20e-03 5.88e-03 4.53e-04 -1.63e-02 -4.53e-04
## 1497 -2.82e-03 -5.75e-04 -5.42e-02 -1.69e-02 4.49e-03 5.77e-03 3.70e-03
## 1504 -2.82e-03 -5.75e-04 -5.42e-02 -1.69e-02 4.49e-03 5.77e-03 3.70e-03
## 1513 1.26e-02 5.02e-02 4.17e-02 -6.52e-02 -1.26e-02 -9.17e-03 7.04e-03
## 1515 8.54e-03 2.77e-02 4.11e-02 -1.48e-02 -1.01e-01 -6.50e-03 2.45e-03
## 1534 1.26e-02 5.02e-02 4.17e-02 -6.52e-02 -1.26e-02 -9.17e-03 7.04e-03
## 1535 -2.01e-02 -1.57e-03 -3.36e-04 4.69e-04 1.15e-03 -1.01e-03 1.31e-02
## 1536 -2.19e-03 4.62e-03 5.80e-03 -1.78e-02 -1.27e-03 -3.98e-03 2.73e-03
## 1540 1.82e-03 4.37e-02 4.76e-02 -6.37e-02 -4.68e-03 -6.98e-03 1.02e-02
## 1551 -3.25e-04 4.87e-04 -8.96e-04 -3.78e-03 -2.80e-04 3.18e-03 1.03e-03
## 1555 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 1557 1.82e-03 4.37e-02 4.76e-02 -6.37e-02 -4.68e-03 -6.98e-03 1.02e-02
## 1566 3.26e-03 2.79e-02 2.58e-02 -4.24e-02 5.06e-03 8.42e-04 2.00e-03
## 1567 -3.65e-03 1.14e-02 -6.81e-02 -3.49e-02 4.63e-03 4.11e-03 6.25e-03
## 1576 3.26e-03 2.79e-02 2.58e-02 -4.24e-02 5.06e-03 8.42e-04 2.00e-03
## 1584 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 1585 7.70e-03 3.10e-02 6.65e-02 1.16e-03 -2.93e-03 -5.86e-02 -4.45e-03
## 1590 -4.04e-03 -8.48e-03 -9.68e-02 9.42e-03 7.62e-03 -4.09e-02 6.27e-05
## 1594 -3.43e-03 6.20e-03 5.58e-03 1.11e-01 9.68e-02 1.15e-01 1.89e-03
## 1595 -3.43e-03 6.20e-03 5.58e-03 1.11e-01 9.68e-02 1.15e-01 1.89e-03
## 1603 1.26e-02 5.02e-02 4.17e-02 -6.52e-02 -1.26e-02 -9.17e-03 7.04e-03
## 1608 -2.93e-03 -8.95e-02 -1.18e-02 1.89e-02 -4.67e-02 2.49e-03 4.98e-04
## 1609 -3.97e-03 -3.85e-02 -2.15e-03 -8.45e-03 7.76e-04 2.35e-04 4.94e-03
## 1615 -6.10e-03 -7.22e-02 -8.83e-03 1.14e-02 2.09e-03 -2.12e-02 4.25e-03
## 1616 1.82e-03 4.37e-02 4.76e-02 -6.37e-02 -4.68e-03 -6.98e-03 1.02e-02
## 1617 2.78e-03 2.58e-02 4.34e-02 -1.47e-02 -3.48e-03 -6.10e-02 3.22e-03
## 1620 -2.54e-02 -3.29e-04 9.27e-04 -2.20e-03 1.25e-03 4.69e-04 1.70e-02
## 1621 -5.76e-04 -5.47e-02 3.72e-03 -2.20e-02 -4.50e-03 -3.95e-03 7.34e-03
## 1637 -1.17e-03 1.51e-02 -7.27e-02 -3.00e-02 6.62e-03 1.40e-02 1.65e-03
## 1638 -5.64e-03 5.33e-03 1.81e-03 -4.65e-02 1.03e-02 3.50e-03 7.40e-03
## 1650 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 1654 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 1665 1.27e-02 3.91e-02 2.63e-02 1.10e-01 9.75e-02 1.23e-01 -1.00e-03
## 1670 -3.97e-03 -3.85e-02 -2.15e-03 -8.45e-03 7.76e-04 2.35e-04 4.94e-03
## 1671 1.56e-02 3.45e-02 3.69e-02 -1.83e-02 -1.31e-02 -6.25e-02 -2.91e-04
## 1675 -4.49e-02 -1.73e-03 3.83e-04 -7.26e-05 1.73e-03 -2.97e-03 2.98e-02
## 1688 -5.76e-04 -5.47e-02 3.72e-03 -2.20e-02 -4.50e-03 -3.95e-03 7.34e-03
## 1691 2.78e-03 2.58e-02 4.34e-02 -1.47e-02 -3.48e-03 -6.10e-02 3.22e-03
## 1695 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 1698 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 1704 -2.88e-03 2.84e-02 3.35e-02 1.17e-01 1.11e-01 1.28e-01 3.19e-03
## 1705 -2.88e-03 2.84e-02 3.35e-02 1.17e-01 1.11e-01 1.28e-01 3.19e-03
## 1711 1.27e-02 3.91e-02 2.63e-02 1.10e-01 9.75e-02 1.23e-01 -1.00e-03
## 1719 -2.82e-03 -5.75e-04 -5.42e-02 -1.69e-02 4.49e-03 5.77e-03 3.70e-03
## 1723 -6.89e-03 -8.56e-02 1.85e-02 -1.94e-02 4.38e-04 2.69e-03 8.44e-03
## 1726 -4.84e-02 -5.56e-04 2.83e-03 -3.08e-03 2.47e-03 1.64e-03 3.20e-02
## 1749 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 1752 3.10e-03 -8.05e-02 -2.47e-03 5.65e-03 -6.16e-02 -4.15e-03 3.89e-03
## 1754 -2.10e-03 -1.11e-04 -7.80e-02 -1.81e-02 1.19e-02 9.79e-03 -3.92e-04
## 1758 -2.82e-03 -5.75e-04 -5.42e-02 -1.69e-02 4.49e-03 5.77e-03 3.70e-03
## 1761 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 1773 3.10e-03 -8.05e-02 -2.47e-03 5.65e-03 -6.16e-02 -4.15e-03 3.89e-03
## 1775 -3.43e-03 6.20e-03 5.58e-03 1.11e-01 9.68e-02 1.15e-01 1.89e-03
## 1786 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 1793 1.50e-02 1.14e-02 5.84e-03 -2.11e-03 1.32e-02 -7.56e-02 -1.58e-02
## 1799 3.26e-03 2.79e-02 2.58e-02 -4.24e-02 5.06e-03 8.42e-04 2.00e-03
## 1803 2.15e-02 4.14e-02 4.13e-02 -1.08e-02 -5.32e-03 -6.99e-02 -1.03e-02
## 1806 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 1807 1.16e-02 2.60e-03 -9.10e-02 4.49e-03 -8.19e-02 3.42e-03 -3.18e-03
## 1808 1.26e-02 5.02e-02 4.17e-02 -6.52e-02 -1.26e-02 -9.17e-03 7.04e-03
## 1814 5.72e-03 8.41e-03 1.51e-02 -2.22e-03 -7.33e-02 -1.55e-05 1.29e-03
## 1815 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 1818 -6.79e-03 -6.21e-02 6.57e-03 -2.14e-02 -1.23e-04 -2.80e-03 9.39e-03
## 1827 1.16e-02 2.60e-03 -9.10e-02 4.49e-03 -8.19e-02 3.42e-03 -3.18e-03
## 1834 6.96e-04 2.05e-02 2.05e-02 -3.77e-02 -1.36e-03 -8.44e-04 6.79e-03
## 1835 5.72e-03 8.41e-03 1.51e-02 -2.22e-03 -7.33e-02 -1.55e-05 1.29e-03
## 1843 1.82e-03 4.37e-02 4.76e-02 -6.37e-02 -4.68e-03 -6.98e-03 1.02e-02
## 1846 1.04e+00 1.51e-01 4.91e-02 -6.34e-02 -5.91e-03 1.90e-03 -1.57e+00
## 1850 -2.82e-03 -5.75e-04 -5.42e-02 -1.69e-02 4.49e-03 5.77e-03 3.70e-03
## 1851 7.29e-03 -2.46e-04 -9.90e-02 5.76e-03 -9.80e-04 -4.23e-02 -2.93e-03
## 1854 1.27e-02 3.91e-02 2.63e-02 1.10e-01 9.75e-02 1.23e-01 -1.00e-03
## 1859 -6.10e-03 -7.22e-02 -8.83e-03 1.14e-02 2.09e-03 -2.12e-02 4.25e-03
## 1861 1.56e-02 3.45e-02 3.69e-02 -1.83e-02 -1.31e-02 -6.25e-02 -2.91e-04
## 1866 -2.82e-03 -5.75e-04 -5.42e-02 -1.69e-02 4.49e-03 5.77e-03 3.70e-03
## 1873 -1.38e-03 7.82e-03 1.68e-02 -1.97e-02 -1.51e-03 -8.79e-04 5.30e-04
## 1875 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 1885 5.72e-03 8.41e-03 1.51e-02 -2.22e-03 -7.33e-02 -1.55e-05 1.29e-03
## 1892 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 1895 1.56e-02 3.45e-02 3.69e-02 -1.83e-02 -1.31e-02 -6.25e-02 -2.91e-04
## 1896 -7.61e-03 -8.68e-02 8.33e-03 -2.40e-02 5.56e-03 -2.31e-03 7.43e-03
## 1897 -2.20e-03 -2.17e-02 -1.11e-01 2.70e-02 1.71e-02 -3.10e-02 -7.86e-03
## 1899 -5.64e-03 5.33e-03 1.81e-03 -4.65e-02 1.03e-02 3.50e-03 7.40e-03
## 1904 1.27e-02 3.91e-02 2.63e-02 1.10e-01 9.75e-02 1.23e-01 -1.00e-03
## 1905 2.78e-03 2.58e-02 4.34e-02 -1.47e-02 -3.48e-03 -6.10e-02 3.22e-03
## 1908 1.56e-02 3.45e-02 3.69e-02 -1.83e-02 -1.31e-02 -6.25e-02 -2.91e-04
## 1916 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 1918 5.72e-03 8.41e-03 1.51e-02 -2.22e-03 -7.33e-02 -1.55e-05 1.29e-03
## 1920 1.27e-02 3.91e-02 2.63e-02 1.10e-01 9.75e-02 1.23e-01 -1.00e-03
## 1930 8.54e-03 2.77e-02 4.11e-02 -1.48e-02 -1.01e-01 -6.50e-03 2.45e-03
## 1940 -3.65e-03 1.14e-02 -6.81e-02 -3.49e-02 4.63e-03 4.11e-03 6.25e-03
## 1947 2.78e-03 2.58e-02 4.34e-02 -1.47e-02 -3.48e-03 -6.10e-02 3.22e-03
## 1949 -5.76e-04 -5.47e-02 3.72e-03 -2.20e-02 -4.50e-03 -3.95e-03 7.34e-03
## 1951 -3.57e-03 -1.73e-02 -8.45e-02 1.49e-02 7.62e-03 -2.53e-02 -4.52e-04
## 1952 -2.82e-03 -5.75e-04 -5.42e-02 -1.69e-02 4.49e-03 5.77e-03 3.70e-03
## 1960 -5.76e-04 -5.47e-02 3.72e-03 -2.20e-02 -4.50e-03 -3.95e-03 7.34e-03
## 9001 1.38e-03 6.43e-03 1.63e-02 -1.62e-03 -1.48e-04 -4.21e-02 1.89e-03
## 9012 1.27e-02 3.91e-02 2.63e-02 1.10e-01 9.75e-02 1.23e-01 -1.00e-03
## 9023 6.64e-03 3.11e-02 4.85e-02 -6.30e-03 6.00e-03 -6.76e-02 -6.45e-03
## 9029 7.29e-03 -2.46e-04 -9.90e-02 5.76e-03 -9.80e-04 -4.23e-02 -2.93e-03
## 6 1.27e-02 1.45e-01 -7.93e-04 -1.40e-02 -1.66e-03 5.38e-02 -1.02e-02
## 12 9.88e-04 -7.57e-03 4.31e-02 1.78e-02 -6.66e-03 -3.64e-03 -7.54e-05
## 43 2.03e-03 -3.66e-03 -3.29e-03 -6.55e-02 -5.71e-02 -6.78e-02 -1.12e-03
## 53 -2.55e-06 -1.11e-04 -1.19e-04 -4.78e-04 -4.54e-04 -4.91e-04 2.75e-05
## 67 5.96e-03 3.14e-03 4.18e-03 3.19e-02 3.73e-04 3.87e-03 -5.80e-03
## 79 7.27e-04 6.90e-02 -4.70e-03 2.78e-02 5.68e-03 4.98e-03 -9.27e-03
## 122 3.35e-03 -6.05e-03 -5.44e-03 -1.08e-01 -9.44e-02 -1.12e-01 -1.85e-03
## 126 1.18e-03 2.58e-01 8.26e-03 -1.94e-02 1.39e-02 1.06e-01 -1.32e-02
## 133 -9.92e-05 -2.19e-04 -2.34e-04 1.16e-04 8.29e-05 3.97e-04 1.84e-06
## 138 4.39e-04 -7.94e-04 -7.14e-04 -1.42e-02 -1.24e-02 -1.47e-02 -2.42e-04
## 154 -1.01e-03 1.82e-03 1.63e-03 3.25e-02 2.83e-02 3.37e-02 5.54e-04
## 159 -1.24e-03 -5.82e-03 -9.07e-03 1.18e-03 -1.12e-03 1.26e-02 1.21e-03
## 174 3.35e-03 -6.05e-03 -5.44e-03 -1.08e-01 -9.44e-02 -1.12e-01 -1.85e-03
## 176 -4.53e-03 -1.39e-02 -9.35e-03 -3.92e-02 -3.47e-02 -4.37e-02 3.57e-04
## 181 8.80e-04 -8.70e-03 -1.02e-02 -3.57e-02 -3.40e-02 -3.93e-02 -9.76e-04
## 182 6.13e-04 -7.96e-03 3.82e-02 1.58e-02 -3.48e-03 -7.34e-03 -8.67e-04
## 186 -1.94e-02 -3.40e-02 -3.32e-02 1.73e-02 1.02e-01 8.58e-03 7.98e-04
## 189 2.30e-03 -1.76e-02 1.00e-01 4.13e-02 -1.55e-02 -8.46e-03 -1.75e-04
## 204 -3.98e-04 -1.18e-02 -1.17e-02 2.16e-02 7.76e-04 4.83e-04 -3.88e-03
## 215 -3.98e-04 -1.18e-02 -1.17e-02 2.16e-02 7.76e-04 4.83e-04 -3.88e-03
## 232 4.68e-03 9.82e-03 1.12e-01 -1.09e-02 -8.81e-03 4.73e-02 -7.26e-05
## 233 -1.21e-04 -5.63e-04 -1.43e-03 1.42e-04 1.29e-05 3.69e-03 -1.66e-04
## 252 1.83e-02 2.64e-01 9.38e-04 -4.12e-02 -1.76e-02 9.44e-02 -3.95e-03
## 253 3.35e-03 -6.05e-03 -5.44e-03 -1.08e-01 -9.44e-02 -1.12e-01 -1.85e-03
## 274 -1.10e-03 -5.12e-03 -1.30e-02 1.29e-03 1.17e-04 3.35e-02 -1.51e-03
## 275 -1.19e-02 -1.75e-02 -3.15e-02 4.62e-03 1.53e-01 3.23e-05 -2.68e-03
## 287 -4.12e-04 -1.06e-02 -1.42e-03 8.66e-03 6.18e-03 7.47e-03 4.97e-04
## 288 1.69e-03 8.21e-03 4.01e-02 -7.05e-03 -3.62e-03 1.20e-02 2.14e-04
## 325 1.63e-03 5.28e-03 7.83e-03 -2.83e-03 -1.91e-02 -1.24e-03 4.66e-04
## 328 -1.10e-03 -5.12e-03 -1.30e-02 1.29e-03 1.17e-04 3.35e-02 -1.51e-03
## 344 -4.27e-05 3.03e-05 1.66e-03 -1.50e-03 -1.20e-03 -1.49e-03 5.03e-05
## 353 -4.86e-02 5.12e-04 -2.39e-03 -1.82e-03 7.43e-03 4.42e-03 -4.57e-02
## 354 -3.98e-04 -1.18e-02 -1.17e-02 2.16e-02 7.76e-04 4.83e-04 -3.88e-03
## 367 -5.01e-03 -9.61e-03 -9.60e-03 2.50e-03 1.24e-03 1.62e-02 2.40e-03
## 369 -1.07e-02 -2.37e-02 -2.54e-02 1.26e-02 8.98e-03 4.30e-02 2.00e-04
## 390 -5.25e-03 -1.23e-02 -2.35e-02 1.38e-05 4.28e-02 -1.29e-03 1.81e-03
## 392 -2.58e-02 -1.03e-01 -8.54e-02 1.34e-01 2.59e-02 1.88e-02 -1.44e-02
## 423 -1.74e-03 -1.56e-03 -1.23e-03 2.31e-03 -2.29e-04 1.25e-02 6.10e-04
## 432 -5.25e-03 -1.23e-02 -2.35e-02 1.38e-05 4.28e-02 -1.29e-03 1.81e-03
## 436 6.50e-03 4.91e-03 2.73e-03 -5.35e-03 -4.60e-02 -1.92e-03 -1.56e-03
## 483 -6.01e-04 -1.44e-02 -1.57e-02 2.10e-02 1.54e-03 2.30e-03 -3.36e-03
## 513 6.94e-03 1.46e-02 1.66e-01 -1.62e-02 -1.31e-02 7.02e-02 -1.08e-04
## 516 -3.83e-05 -6.31e-04 -1.08e-03 -2.88e-03 -2.38e-03 -2.99e-03 1.13e-04
## 518 1.09e-03 -3.39e-03 2.03e-02 1.04e-02 -1.38e-03 -1.22e-03 -1.86e-03
## 520 -7.46e-03 -6.39e-02 -5.91e-02 9.70e-02 -1.16e-02 -1.93e-03 -4.57e-03
## 526 3.35e-03 -6.05e-03 -5.44e-03 -1.08e-01 -9.44e-02 -1.12e-01 -1.85e-03
## 528 4.68e-03 9.82e-03 1.12e-01 -1.09e-02 -8.81e-03 4.73e-02 -7.26e-05
## 553 -2.05e-03 -6.05e-02 -6.02e-02 1.11e-01 3.99e-03 2.48e-03 -2.00e-02
## 576 1.85e-03 2.01e-03 1.30e-03 -1.89e-03 3.66e-03 1.68e-02 -3.52e-03
## 611 -1.10e-03 -5.12e-03 -1.30e-02 1.29e-03 1.17e-04 3.35e-02 -1.51e-03
## 625 -1.72e-02 -3.81e-02 -4.07e-02 2.02e-02 1.44e-02 6.90e-02 3.21e-04
## 635 -6.98e-03 -4.90e-04 7.13e-02 -9.71e-03 -4.55e-03 2.86e-02 7.41e-03
## 646 -1.95e-03 -6.29e-03 2.53e-02 1.32e-02 7.89e-04 -7.28e-04 -1.33e-03
## 657 -2.09e-04 1.48e-02 9.86e-03 -4.41e-03 8.21e-02 -5.10e-03 6.86e-04
## 659 -7.66e-05 2.81e-04 5.08e-03 -4.48e-04 4.35e-03 -3.34e-04 2.50e-05
## 666 -1.21e-04 -5.63e-04 -1.43e-03 1.42e-04 1.29e-05 3.69e-03 -1.66e-04
## 679 7.86e-04 7.31e-03 1.23e-02 -4.17e-03 -9.85e-04 -1.73e-02 9.10e-04
## 729 -1.42e-03 -4.35e-03 -2.92e-03 -1.22e-02 -1.08e-02 -1.37e-02 1.12e-04
## 755 1.03e-03 1.08e-02 1.46e-05 -9.70e-03 -6.70e-03 -8.10e-03 -2.38e-04
## 758 4.68e-03 9.82e-03 1.12e-01 -1.09e-02 -8.81e-03 4.73e-02 -7.26e-05
## 770 -2.22e-03 -1.04e-02 -2.63e-02 2.61e-03 2.38e-04 6.78e-02 -3.05e-03
## 786 1.96e-03 7.87e-03 1.69e-02 2.96e-04 -7.45e-04 -1.49e-02 -1.13e-03
## 797 3.86e-03 4.65e-03 4.96e-02 -4.83e-02 -4.12e-02 -4.77e-02 1.90e-04
## 811 -2.29e-04 -2.75e-03 -3.05e-02 8.40e-03 3.06e-03 -1.16e-02 -2.22e-03
## 834 -2.22e-03 -1.04e-02 -2.63e-02 2.61e-03 2.38e-04 6.78e-02 -3.05e-03
## 858 5.17e-03 1.50e-02 3.02e-02 5.51e-03 3.29e-03 -2.60e-02 -6.79e-03
## 885 2.03e-03 -3.66e-03 -3.29e-03 -6.55e-02 -5.71e-02 -6.78e-02 -1.12e-03
## 893 -5.25e-03 -1.23e-02 -2.35e-02 1.38e-05 4.28e-02 -1.29e-03 1.81e-03
## 927 2.86e-04 1.25e-02 1.34e-02 5.38e-02 5.11e-02 5.53e-02 -3.09e-03
## 928 -2.91e-03 -1.22e-03 6.53e-03 -3.91e-03 6.28e-03 -3.18e-02 3.77e-04
## 933 2.42e-03 6.13e-03 8.49e-03 -1.30e-03 -1.97e-02 -8.89e-04 -1.24e-03
## 951 -4.53e-03 -1.39e-02 -9.35e-03 -3.92e-02 -3.47e-02 -4.37e-02 3.57e-04
## 968 3.42e-03 6.98e-04 6.58e-02 2.05e-02 -5.45e-03 -7.01e-03 -4.49e-03
## 972 -4.03e-04 -3.75e-03 -6.29e-03 2.13e-03 5.04e-04 8.84e-03 -4.66e-04
## 975 -6.38e-03 -5.56e-02 -7.46e-02 6.29e-02 4.85e-03 -1.90e-03 -5.29e-03
## 977 -4.53e-03 -1.15e-02 -1.59e-02 2.44e-03 3.69e-02 1.67e-03 2.33e-03
## 981 -1.23e-02 -4.20e-02 -3.33e-02 4.74e-02 4.55e-03 5.88e-03 1.32e-04
## 986 5.16e-03 1.31e-02 1.81e-02 -2.78e-03 -4.21e-02 -1.90e-03 -2.66e-03
## 1002 2.16e-04 4.71e-02 1.51e-03 -3.55e-03 2.55e-03 1.95e-02 -2.41e-03
## 1007 3.35e-03 -6.05e-03 -5.44e-03 -1.08e-01 -9.44e-02 -1.12e-01 -1.85e-03
## 1011 -1.71e-03 -1.59e-02 -2.67e-02 9.06e-03 2.14e-03 3.75e-02 -1.98e-03
## 1035 -4.30e-03 3.92e-03 7.41e-02 -1.97e-02 7.22e-02 -1.34e-02 5.88e-03
## 1050 -1.24e-03 -2.76e-04 9.66e-03 -4.77e-04 8.69e-03 -3.63e-04 3.38e-04
## 1056 -1.14e-02 -3.68e-02 -5.47e-02 1.97e-02 1.34e-01 8.64e-03 -3.25e-03
## 1057 -3.98e-04 -1.18e-02 -1.17e-02 2.16e-02 7.76e-04 4.83e-04 -3.88e-03
## 1075 -4.53e-03 -1.39e-02 -9.35e-03 -3.92e-02 -3.47e-02 -4.37e-02 3.57e-04
## 1080 -9.82e-03 -2.31e-02 -4.40e-02 2.57e-05 8.01e-02 -2.42e-03 3.39e-03
## 1125 4.39e-04 -7.94e-04 -7.14e-04 -1.42e-02 -1.24e-02 -1.47e-02 -2.42e-04
## 1131 -3.01e-03 -6.64e-03 -7.11e-03 3.53e-03 2.52e-03 1.20e-02 5.59e-05
## 1138 1.05e-01 -3.45e-03 3.76e-03 -1.68e-02 3.82e-02 -6.35e-03 1.14e-01
## 1150 -2.80e-03 -2.61e-02 -4.38e-02 1.49e-02 3.51e-03 6.16e-02 -3.25e-03
## 1163 -4.28e-03 -6.30e-03 -1.13e-02 1.66e-03 5.49e-02 1.16e-05 -9.62e-04
## 1169 -3.98e-04 -1.18e-02 -1.17e-02 2.16e-02 7.76e-04 4.83e-04 -3.88e-03
## 1198 -1.51e-03 1.07e-03 5.88e-02 -5.30e-02 -4.24e-02 -5.28e-02 1.78e-03
## 1204 5.81e-03 1.17e-01 1.44e-02 -1.82e-02 6.64e-02 -1.56e-03 -6.60e-03
## 1218 -2.55e-06 -1.11e-04 -1.19e-04 -4.78e-04 -4.54e-04 -4.91e-04 2.75e-05
## 1230 -4.53e-03 -1.39e-02 -9.35e-03 -3.92e-02 -3.47e-02 -4.37e-02 3.57e-04
## 1236 -1.71e-03 -1.59e-02 -2.67e-02 9.06e-03 2.14e-03 3.75e-02 -1.98e-03
## 1247 -4.53e-03 -1.39e-02 -9.35e-03 -3.92e-02 -3.47e-02 -4.37e-02 3.57e-04
## 1259 8.80e-04 -8.70e-03 -1.02e-02 -3.57e-02 -3.40e-02 -3.93e-02 -9.76e-04
## 1294 -2.05e-03 -6.05e-02 -6.02e-02 1.11e-01 3.99e-03 2.48e-03 -2.00e-02
## 1353 -3.19e-03 -1.49e-02 -3.78e-02 3.75e-03 3.41e-04 9.73e-02 -4.37e-03
## 1370 4.17e-03 6.60e-03 6.05e-03 -1.74e-03 -1.78e-02 -1.21e-03 -1.60e-03
## 1427 -8.67e-03 -2.20e-02 -3.04e-02 4.68e-03 7.06e-02 3.19e-03 4.46e-03
## 1445 2.20e-04 -3.66e-03 -2.07e-03 -6.02e-02 -5.28e-02 -5.87e-02 3.49e-03
## 1460 -1.07e-02 -2.37e-02 -2.54e-02 1.26e-02 8.98e-03 4.30e-02 2.00e-04
## 1480 -9.92e-05 -2.19e-04 -2.34e-04 1.16e-04 8.29e-05 3.97e-04 1.84e-06
## 1505 -3.83e-05 -6.31e-04 -1.08e-03 -2.88e-03 -2.38e-03 -2.99e-03 1.13e-04
## 1543 -4.53e-03 -1.15e-02 -1.59e-02 2.44e-03 3.69e-02 1.67e-03 2.33e-03
## 1548 -1.60e-04 2.88e-04 2.59e-04 5.16e-03 4.50e-03 5.34e-03 8.80e-05
## 1550 -2.89e-03 2.11e-01 -1.20e-02 6.97e-02 2.52e-03 1.11e-02 -1.34e-02
## 1561 6.53e-03 1.63e-02 9.90e-03 4.87e-02 4.34e-02 5.06e-02 -4.58e-03
## 1564 -1.10e-02 -2.12e-02 -2.12e-02 5.52e-03 2.72e-03 3.58e-02 5.29e-03
## 1573 -1.77e-02 -5.73e-02 2.30e-01 1.20e-01 7.18e-03 -6.63e-03 -1.21e-02
## 1575 -8.33e-04 -2.46e-02 -2.45e-02 4.51e-02 1.62e-03 1.01e-03 -8.12e-03
## 1599 -7.66e-05 2.81e-04 5.08e-03 -4.48e-04 4.35e-03 -3.34e-04 2.50e-05
## 1622 1.18e-02 -1.11e-02 -3.78e-03 9.69e-02 -2.14e-02 -7.29e-03 -1.54e-02
## 1629 3.24e-04 -4.21e-03 2.02e-02 8.34e-03 -1.84e-03 -3.88e-03 -4.58e-04
## 1664 -5.01e-03 -9.61e-03 -9.60e-03 2.50e-03 1.24e-03 1.62e-02 2.40e-03
## 1669 -1.94e-03 -6.41e-03 -7.49e-02 9.78e-02 7.56e-02 8.85e-02 -1.08e-02
## 1674 1.75e-01 -2.33e-02 -4.05e-03 2.01e-02 -1.81e-02 -9.18e-03 1.77e-01
## 1682 -2.22e-03 -1.04e-02 -2.63e-02 2.61e-03 2.38e-04 6.78e-02 -3.05e-03
## 1685 1.40e-03 -1.81e-02 8.69e-02 3.59e-02 -7.92e-03 -1.67e-02 -1.97e-03
## 1697 3.77e-03 1.83e-02 8.92e-02 -1.57e-02 -8.04e-03 2.67e-02 4.77e-04
## 1716 -2.03e-03 -1.71e-02 -1.56e-03 1.36e-02 1.04e-02 1.17e-02 1.13e-03
## 1730 2.36e-03 4.52e-03 4.52e-03 -1.18e-03 -5.82e-04 -7.64e-03 -1.13e-03
## 1731 -1.21e-04 -5.63e-04 -1.43e-03 1.42e-04 1.29e-05 3.69e-03 -1.66e-04
## 1732 8.80e-04 -8.70e-03 -1.02e-02 -3.57e-02 -3.40e-02 -3.93e-02 -9.76e-04
## 1743 1.63e-03 5.28e-03 7.83e-03 -2.83e-03 -1.91e-02 -1.24e-03 4.66e-04
## 1751 -2.29e-04 -2.75e-03 -3.05e-02 8.40e-03 3.06e-03 -1.16e-02 -2.22e-03
## 1757 -4.28e-03 -6.30e-03 -1.13e-02 1.66e-03 5.49e-02 1.16e-05 -9.62e-04
## 1763 -1.71e-03 -1.59e-02 -2.67e-02 9.06e-03 2.14e-03 3.75e-02 -1.98e-03
## 1766 -6.27e-03 1.63e-01 4.99e-03 -1.14e-02 1.25e-01 8.38e-03 -7.85e-03
## 1772 -2.74e-04 -3.30e-04 -3.52e-03 3.43e-03 2.92e-03 3.38e-03 -1.35e-05
## 1776 -8.33e-04 -2.46e-02 -2.45e-02 4.51e-02 1.62e-03 1.01e-03 -8.12e-03
## 1782 8.92e-04 -1.25e-02 -1.96e-02 2.61e-02 -4.93e-03 -3.70e-03 -1.24e-03
## 1784 -6.38e-03 -5.56e-02 -7.46e-02 6.29e-02 4.85e-03 -1.90e-03 -5.29e-03
## 1791 -1.10e-03 -5.12e-03 -1.30e-02 1.29e-03 1.17e-04 3.35e-02 -1.51e-03
## 1831 2.08e-04 -2.06e-03 -2.43e-03 -8.46e-03 -8.04e-03 -9.30e-03 -2.31e-04
## 1840 2.03e-03 -3.66e-03 -3.29e-03 -6.55e-02 -5.71e-02 -6.78e-02 -1.12e-03
## 1844 8.80e-04 -8.70e-03 -1.02e-02 -3.57e-02 -3.40e-02 -3.93e-02 -9.76e-04
## 1856 -1.42e-03 -4.35e-03 -2.92e-03 -1.22e-02 -1.08e-02 -1.37e-02 1.12e-04
## 1876 -1.10e-02 -2.12e-02 -2.12e-02 5.52e-03 2.72e-03 3.58e-02 5.29e-03
## 1929 -6.32e-03 -9.29e-03 -1.67e-02 2.45e-03 8.10e-02 1.71e-05 -1.42e-03
## 1935 2.08e-04 -2.06e-03 -2.43e-03 -8.46e-03 -8.04e-03 -9.30e-03 -2.31e-04
## 1938 3.13e-03 3.89e-02 -8.40e-03 8.84e-03 -1.99e-04 -1.22e-03 -3.84e-03
## 1941 -1.07e-02 -2.37e-02 -2.54e-02 1.26e-02 8.98e-03 4.30e-02 2.00e-04
## 1954 -2.51e-05 -8.53e-05 -6.77e-05 9.62e-05 9.23e-06 1.19e-05 2.67e-07
## 1959 -9.48e-04 9.37e-03 1.10e-02 3.85e-02 3.66e-02 4.23e-02 1.05e-03
## 9010 5.34e-05 1.28e-03 1.39e-03 -1.86e-03 -1.37e-04 -2.04e-04 2.98e-04
## dfb.ccer dffit cov.r cook.d hat inf
## 4 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 5 3.45e-04 -0.113731 1.022 1.37e-04 0.01636
## 11 1.72e-02 -0.242962 1.006 3.32e-04 0.02955
## 16 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 23 5.72e-04 -0.069650 1.075 8.63e-05 0.04771
## 29 3.78e-04 -0.142526 1.032 2.17e-04 0.02554
## 44 5.55e-04 -0.034759 1.051 2.23e-05 0.02459
## 45 2.20e-02 -0.140149 1.001 1.36e-04 0.01306
## 47 1.48e-02 -0.159190 1.002 1.69e-04 0.01600
## 49 3.33e-04 -0.050058 1.029 3.88e-05 0.00955
## 50 2.26e-02 -0.194637 0.992 2.02e-04 0.01825
## 55 3.45e-04 -0.113731 1.022 1.37e-04 0.01636
## 64 1.23e-02 -0.172315 1.010 2.14e-04 0.02044
## 80 -7.78e-04 -0.198045 1.032 3.39e-04 0.03398
## 86 3.45e-04 -0.113731 1.022 1.37e-04 0.01636
## 93 1.33e-02 -0.299290 1.020 5.02e-04 0.04342
## 108 2.70e-02 -0.169849 1.013 2.18e-04 0.02111
## 114 -4.93e-04 -0.051422 1.063 4.80e-05 0.03620
## 115 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 116 1.96e-02 -0.129164 1.012 1.42e-04 0.01469
## 123 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 127 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 129 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 134 1.14e-02 -0.137995 1.012 1.58e-04 0.01605
## 137 4.24e-03 -0.153693 1.015 1.94e-04 0.01957
## 139 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 147 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 151 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 153 -2.71e-03 -0.126991 1.021 1.59e-04 0.01790
## 155 -1.10e-02 -0.232015 1.016 3.50e-04 0.03187
## 162 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 163 -2.19e-03 -0.095731 1.030 1.16e-04 0.01714
## 165 -6.93e-05 -0.190770 1.144 5.62e-04 0.11006 *
## 168 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 170 -2.58e-03 -0.232745 1.018 3.57e-04 0.03253
## 172 2.27e-03 -0.081002 1.035 9.41e-05 0.01787
## 184 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 187 -1.96e-03 -0.137209 1.029 1.99e-04 0.02333
## 192 -1.13e-03 -0.160613 1.038 2.72e-04 0.03122
## 194 -1.91e-04 -0.026141 1.045 1.28e-05 0.01910
## 210 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 217 -2.45e-01 -0.288636 1.050 6.32e-04 0.05704
## 220 7.73e-04 -0.103461 1.094 1.82e-04 0.06563 *
## 224 1.81e-03 -0.226356 1.011 3.18e-04 0.02885
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## 228 -2.71e-03 -0.126991 1.021 1.59e-04 0.01790
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## 293 -3.46e-04 -0.041689 1.056 3.19e-05 0.02993
## 295 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 299 1.48e-02 -0.159190 1.002 1.69e-04 0.01600
## 320 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 321 1.87e-04 -0.023336 1.043 1.02e-05 0.01676
## 324 4.39e-03 -0.122163 1.022 1.52e-04 0.01741
## 334 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 351 1.87e-02 -0.197344 1.013 2.69e-04 0.02516
## 355 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 361 8.55e-03 -0.202913 1.012 2.77e-04 0.02570
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## 366 2.64e-03 -0.128024 1.028 1.76e-04 0.02094
## 370 -7.98e-03 -0.191191 1.023 2.91e-04 0.02844
## 374 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 378 1.42e-03 -0.177494 1.116 4.74e-04 0.08859 *
## 381 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 382 1.66e-02 -0.217243 1.016 3.21e-04 0.02969
## 383 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 384 -2.19e-03 -0.095731 1.030 1.16e-04 0.01714
## 400 -7.98e-03 -0.191191 1.023 2.91e-04 0.02844
## 403 -2.19e-03 -0.095731 1.030 1.16e-04 0.01714
## 409 2.26e-02 -0.194637 0.992 2.02e-04 0.01825
## 412 -2.49e-02 -1.577783 1.310 7.46e-03 0.31703 *
## 413 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 416 2.65e-04 -0.045945 1.058 3.85e-05 0.03187
## 418 1.72e-03 -0.183475 1.035 3.15e-04 0.03305
## 422 3.33e-04 -0.050058 1.029 3.88e-05 0.00955
## 435 -2.70e-03 -0.546019 1.293 3.12e-03 0.22700 *
## 439 1.14e-02 -0.141119 1.017 1.77e-04 0.01849
## 445 -8.64e-03 -0.214711 1.028 3.59e-04 0.03426
## 447 -2.19e-03 -0.095731 1.030 1.16e-04 0.01714
## 448 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 449 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 478 2.70e-02 -0.169849 1.013 2.18e-04 0.02111
## 482 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 486 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 489 1.03e-03 -0.363793 1.226 1.69e-03 0.17655 *
## 490 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 491 1.05e-02 -0.209656 0.993 2.29e-04 0.02054
## 492 1.72e-02 -0.242962 1.006 3.32e-04 0.02955
## 503 8.55e-03 -0.202913 1.012 2.77e-04 0.02570
## 508 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 509 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 512 2.20e-02 -0.140149 1.001 1.36e-04 0.01306
## 515 1.20e-05 -0.088616 1.075 1.34e-04 0.04908
## 517 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 532 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 533 3.45e-04 -0.113731 1.022 1.37e-04 0.01636
## 535 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 537 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 538 1.78e-02 -0.172193 1.011 2.18e-04 0.02093
## 543 2.64e-03 -0.128024 1.028 1.76e-04 0.02094
## 547 1.14e-02 -0.141119 1.017 1.77e-04 0.01849
## 550 1.14e-02 -0.141119 1.017 1.77e-04 0.01849
## 558 3.33e-04 -0.050058 1.029 3.88e-05 0.00955
## 571 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 578 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 583 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 586 1.48e-02 -0.159190 1.002 1.69e-04 0.01600
## 594 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 597 1.44e-05 -0.010194 1.033 1.98e-06 0.00753
## 602 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 603 1.96e-02 -0.129164 1.012 1.42e-04 0.01469
## 604 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 612 1.76e-02 -0.181799 1.019 2.59e-04 0.02531
## 613 2.26e-02 -0.194637 0.992 2.02e-04 0.01825
## 621 -2.19e-03 -0.095731 1.030 1.16e-04 0.01714
## 627 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 630 1.42e-03 -0.177494 1.116 4.74e-04 0.08859 *
## 631 3.33e-04 -0.050058 1.029 3.88e-05 0.00955
## 632 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 639 3.78e-04 -0.142526 1.032 2.17e-04 0.02554
## 645 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 647 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 648 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 651 9.30e-03 -0.168239 1.026 2.54e-04 0.02642
## 655 1.78e-02 -0.172193 1.011 2.18e-04 0.02093
## 667 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 670 1.87e-04 -0.023336 1.043 1.02e-05 0.01676
## 671 2.20e-02 -0.140149 1.001 1.36e-04 0.01306
## 673 9.42e-04 -0.078224 1.025 7.87e-05 0.01203
## 701 -2.58e-03 -0.232745 1.018 3.57e-04 0.03253
## 705 2.20e-02 -0.140149 1.001 1.36e-04 0.01306
## 706 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 709 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 717 -3.12e-01 -0.355252 1.034 6.98e-04 0.05836
## 719 1.17e-03 -0.168624 1.024 2.49e-04 0.02546
## 723 -2.58e-03 -0.232745 1.018 3.57e-04 0.03253
## 724 3.45e-04 -0.113731 1.022 1.37e-04 0.01636
## 726 -2.45e-01 -0.288636 1.050 6.32e-04 0.05704
## 734 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 735 1.72e-02 -0.242962 1.006 3.32e-04 0.02955
## 736 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 737 5.55e-04 -0.034759 1.051 2.23e-05 0.02459
## 739 9.30e-03 -0.168239 1.026 2.54e-04 0.02642
## 743 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 745 5.55e-04 -0.034759 1.051 2.23e-05 0.02459
## 747 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 751 -3.78e-01 -0.475740 1.043 1.02e-03 0.08002 *
## 752 1.48e-02 -0.159190 1.002 1.69e-04 0.01600
## 754 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 760 4.39e-03 -0.122163 1.022 1.52e-04 0.01741
## 763 4.39e-03 -0.122163 1.022 1.52e-04 0.01741
## 774 4.39e-03 -0.122163 1.022 1.52e-04 0.01741
## 776 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 779 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 784 7.97e-03 -0.226631 1.001 2.85e-04 0.02558
## 788 -7.50e-03 -0.235827 1.041 4.63e-04 0.04452
## 794 2.26e-02 -0.194637 0.992 2.02e-04 0.01825
## 795 -9.95e-05 -0.087743 1.032 1.03e-04 0.01695
## 798 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 800 1.48e-02 -0.159190 1.002 1.69e-04 0.01600
## 803 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 807 1.20e-05 -0.088616 1.075 1.34e-04 0.04908
## 812 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 820 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 823 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 830 -2.75e-04 -0.481921 1.214 2.28e-03 0.17798 *
## 843 -4.74e-03 -0.159288 1.015 2.03e-04 0.02018
## 848 2.20e-02 -0.140149 1.001 1.36e-04 0.01306
## 851 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 854 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 856 1.12e-02 -0.242833 1.004 3.25e-04 0.02888
## 857 3.33e-04 -0.050058 1.029 3.88e-05 0.00955
## 859 -3.64e-03 -0.235510 1.025 3.92e-04 0.03616
## 863 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 865 9.28e-05 -0.129912 1.111 2.80e-04 0.08116 *
## 867 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 870 -1.91e-04 -0.026141 1.045 1.28e-05 0.01910
## 873 4.39e-03 -0.122163 1.022 1.52e-04 0.01741
## 875 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 876 1.66e-02 -0.217243 1.016 3.21e-04 0.02969
## 877 -7.73e-04 -0.052828 1.064 5.07e-05 0.03768
## 880 1.23e-02 -0.172315 1.010 2.14e-04 0.02044
## 903 7.73e-04 -0.103461 1.094 1.82e-04 0.06563 *
## 904 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 905 2.70e-02 -0.169849 1.013 2.18e-04 0.02111
## 908 -2.58e-03 -0.232745 1.018 3.57e-04 0.03253
## 909 1.76e-02 -0.181799 1.019 2.59e-04 0.02531
## 910 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 912 -3.30e-01 -0.382765 1.039 7.84e-04 0.06447
## 914 -1.59e-03 -0.095263 1.035 1.22e-04 0.01971
## 915 9.42e-04 -0.078224 1.025 7.87e-05 0.01203
## 916 4.39e-03 -0.122163 1.022 1.52e-04 0.01741
## 920 -2.19e-03 -0.095731 1.030 1.16e-04 0.01714
## 921 5.55e-04 -0.034759 1.051 2.23e-05 0.02459
## 925 2.26e-02 -0.194637 0.992 2.02e-04 0.01825
## 926 5.72e-04 -0.069650 1.075 8.63e-05 0.04771
## 929 1.48e-02 -0.159190 1.002 1.69e-04 0.01600
## 931 -1.13e-03 -0.160613 1.038 2.72e-04 0.03122
## 945 2.20e-02 -0.140149 1.001 1.36e-04 0.01306
## 947 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 949 1.78e-02 -0.172193 1.011 2.18e-04 0.02093
## 950 4.39e-03 -0.122163 1.022 1.52e-04 0.01741
## 961 -3.65e-01 -0.430594 1.026 8.14e-04 0.06555
## 965 1.78e-05 -0.047327 1.060 4.09e-05 0.03334
## 966 1.19e-03 -0.188737 1.024 2.92e-04 0.02881
## 967 3.33e-04 -0.050058 1.029 3.88e-05 0.00955
## 987 -1.59e-03 -0.095263 1.035 1.22e-04 0.01971
## 990 1.72e-03 -0.183475 1.035 3.15e-04 0.03305
## 992 -2.58e-03 -0.232745 1.018 3.57e-04 0.03253
## 995 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1009 1.78e-02 -0.172193 1.011 2.18e-04 0.02093
## 1021 1.14e-02 -0.141119 1.017 1.77e-04 0.01849
## 1026 1.14e-02 -0.141119 1.017 1.77e-04 0.01849
## 1027 -9.95e-05 -0.087743 1.032 1.03e-04 0.01695
## 1030 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 1031 1.66e-02 -0.217243 1.016 3.21e-04 0.02969
## 1034 1.23e-02 -0.172315 1.010 2.14e-04 0.02044
## 1037 2.64e-03 -0.128024 1.028 1.76e-04 0.02094
## 1038 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1039 -2.71e-03 -0.126991 1.021 1.59e-04 0.01790
## 1045 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1046 1.14e-02 -0.141119 1.017 1.77e-04 0.01849
## 1054 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1059 3.45e-04 -0.113731 1.022 1.37e-04 0.01636
## 1063 1.44e-05 -0.010194 1.033 1.98e-06 0.00753
## 1068 4.16e-03 -0.116927 1.030 1.58e-04 0.02036
## 1070 1.44e-05 -0.010194 1.033 1.98e-06 0.00753
## 1072 -2.22e-01 -0.246836 1.049 5.22e-04 0.05030
## 1073 1.14e-02 -0.137995 1.012 1.58e-04 0.01605
## 1077 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1081 2.20e-02 -0.140149 1.001 1.36e-04 0.01306
## 1083 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 1084 1.66e-02 -0.217243 1.016 3.21e-04 0.02969
## 1086 -2.71e-03 -0.126991 1.021 1.59e-04 0.01790
## 1087 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1089 -4.93e-04 -0.051422 1.063 4.80e-05 0.03620
## 1096 3.85e-04 -0.137743 1.036 2.15e-04 0.02691
## 1102 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1103 -3.31e-01 -0.379481 1.039 7.78e-04 0.06405
## 1107 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1109 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 1115 2.20e-02 -0.140149 1.001 1.36e-04 0.01306
## 1119 1.14e-02 -0.141119 1.017 1.77e-04 0.01849
## 1124 1.23e-02 -0.172315 1.010 2.14e-04 0.02044
## 1126 -1.13e-03 -0.160613 1.038 2.72e-04 0.03122
## 1128 -4.74e-03 -0.159288 1.015 2.03e-04 0.02018
## 1129 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 1130 2.20e-02 -0.140149 1.001 1.36e-04 0.01306
## 1133 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1140 1.96e-02 -0.129164 1.012 1.42e-04 0.01469
## 1143 -2.71e-03 -0.126991 1.021 1.59e-04 0.01790
## 1146 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 1153 1.72e-02 -0.242962 1.006 3.32e-04 0.02955
## 1156 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 1157 3.85e-04 -0.137743 1.036 2.15e-04 0.02691
## 1158 -2.72e-01 -0.318155 1.045 6.76e-04 0.05859
## 1160 2.26e-02 -0.194637 0.992 2.02e-04 0.01825
## 1161 1.96e-02 -0.129164 1.012 1.42e-04 0.01469
## 1166 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1177 -2.71e-03 -0.126991 1.021 1.59e-04 0.01790
## 1178 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1180 -2.19e-03 -0.095731 1.030 1.16e-04 0.01714
## 1187 4.18e-03 -0.245018 1.148 8.30e-04 0.11654 *
## 1191 2.70e-02 -0.169849 1.013 2.18e-04 0.02111
## 1195 3.45e-04 -0.113731 1.022 1.37e-04 0.01636
## 1207 2.26e-02 -0.194637 0.992 2.02e-04 0.01825
## 1208 4.16e-03 -0.116927 1.030 1.58e-04 0.02036
## 1209 1.87e-02 -0.197344 1.013 2.69e-04 0.02516
## 1211 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1215 -1.13e-03 -0.160613 1.038 2.72e-04 0.03122
## 1221 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1226 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1229 1.23e-02 -0.172315 1.010 2.14e-04 0.02044
## 1231 1.78e-02 -0.172193 1.011 2.18e-04 0.02093
## 1234 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 1235 -8.64e-03 -0.214711 1.028 3.59e-04 0.03426
## 1242 -2.19e-03 -0.095731 1.030 1.16e-04 0.01714
## 1245 -9.58e-03 -0.234753 1.011 3.34e-04 0.03004
## 1260 1.05e-02 -0.209656 0.993 2.29e-04 0.02054
## 1266 2.20e-02 -0.140149 1.001 1.36e-04 0.01306
## 1271 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 1273 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 1276 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1280 2.27e-03 -0.081002 1.035 9.41e-05 0.01787
## 1282 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1285 6.49e-03 -0.238361 1.028 4.13e-04 0.03820
## 1295 5.32e-04 -0.054701 1.063 5.40e-05 0.03720
## 1298 4.39e-03 -0.122163 1.022 1.52e-04 0.01741
## 1299 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 1304 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1305 2.26e-02 -0.194637 0.992 2.02e-04 0.01825
## 1311 4.39e-03 -0.122163 1.022 1.52e-04 0.01741
## 1314 4.24e-03 -0.153693 1.015 1.94e-04 0.01957
## 1319 -2.19e-03 -0.095731 1.030 1.16e-04 0.01714
## 1322 3.45e-04 -0.113731 1.022 1.37e-04 0.01636
## 1324 -3.38e-03 -0.865674 1.212 3.67e-03 0.21185 *
## 1327 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1328 1.41e-04 -0.015300 1.037 4.43e-06 0.01124
## 1330 1.76e-02 -0.181799 1.019 2.59e-04 0.02531
## 1332 -2.71e-03 -0.126991 1.021 1.59e-04 0.01790
## 1333 -4.49e-03 -0.272538 1.028 4.83e-04 0.04297
## 1336 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1341 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1344 -2.58e-03 -0.232745 1.018 3.57e-04 0.03253
## 1352 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 1358 9.42e-04 -0.078224 1.025 7.87e-05 0.01203
## 1359 4.39e-03 -0.122163 1.022 1.52e-04 0.01741
## 1361 -3.12e-01 -0.355252 1.034 6.98e-04 0.05836
## 1364 2.64e-03 -0.128024 1.028 1.76e-04 0.02094
## 1368 -2.69e-01 -0.295286 1.042 6.07e-04 0.05371
## 1384 -1.63e-03 -0.232847 1.168 8.01e-04 0.12984 *
## 1390 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 1393 3.45e-04 -0.113731 1.022 1.37e-04 0.01636
## 1394 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1402 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 1407 -8.33e-04 -0.191988 1.024 2.98e-04 0.02919
## 1408 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 1412 1.96e-02 -0.129164 1.012 1.42e-04 0.01469
## 1413 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1416 2.27e-03 -0.081002 1.035 9.41e-05 0.01787
## 1417 2.64e-03 -0.128024 1.028 1.76e-04 0.02094
## 1418 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1419 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1420 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1423 -2.71e-03 -0.126991 1.021 1.59e-04 0.01790
## 1424 1.48e-02 -0.159190 1.002 1.69e-04 0.01600
## 1432 1.44e-05 -0.010194 1.033 1.98e-06 0.00753
## 1433 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1437 1.14e-02 -0.137995 1.012 1.58e-04 0.01605
## 1438 1.96e-02 -0.129164 1.012 1.42e-04 0.01469
## 1439 1.96e-02 -0.129164 1.012 1.42e-04 0.01469
## 1446 2.70e-02 -0.169849 1.013 2.18e-04 0.02111
## 1450 -7.78e-04 -0.198045 1.032 3.39e-04 0.03398
## 1451 1.87e-04 -0.023336 1.043 1.02e-05 0.01676
## 1452 3.45e-04 -0.113731 1.022 1.37e-04 0.01636
## 1453 -3.13e-03 -0.278186 1.164 1.03e-03 0.12989 *
## 1456 3.33e-04 -0.050058 1.029 3.88e-05 0.00955
## 1464 1.78e-02 -0.172193 1.011 2.18e-04 0.02093
## 1469 -2.45e-01 -0.288636 1.050 6.32e-04 0.05704
## 1473 -2.37e-03 -0.158877 1.035 2.60e-04 0.02953
## 1481 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 1482 -6.48e-03 -0.154266 1.034 2.47e-04 0.02827
## 1496 -4.14e-04 -0.160949 1.110 4.00e-04 0.08280 *
## 1497 9.42e-04 -0.078224 1.025 7.87e-05 0.01203
## 1504 9.42e-04 -0.078224 1.025 7.87e-05 0.01203
## 1513 1.96e-02 -0.129164 1.012 1.42e-04 0.01469
## 1515 1.78e-02 -0.172193 1.011 2.18e-04 0.02093
## 1534 1.96e-02 -0.129164 1.012 1.42e-04 0.01469
## 1535 -1.75e-05 -0.020419 1.041 7.84e-06 0.01485
## 1536 -2.00e-03 -0.193878 1.125 5.55e-04 0.09657 *
## 1540 1.14e-02 -0.137995 1.012 1.58e-04 0.01605
## 1551 5.32e-04 -0.054701 1.063 5.40e-05 0.03720
## 1555 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1557 1.14e-02 -0.137995 1.012 1.58e-04 0.01605
## 1566 1.14e-02 -0.141119 1.017 1.77e-04 0.01849
## 1567 -2.71e-03 -0.126991 1.021 1.59e-04 0.01790
## 1576 1.14e-02 -0.141119 1.017 1.77e-04 0.01849
## 1584 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1585 1.72e-02 -0.242962 1.006 3.32e-04 0.02955
## 1590 -4.74e-03 -0.159288 1.015 2.03e-04 0.02018
## 1594 1.48e-02 -0.159190 1.002 1.69e-04 0.01600
## 1595 1.48e-02 -0.159190 1.002 1.69e-04 0.01600
## 1603 1.96e-02 -0.129164 1.012 1.42e-04 0.01469
## 1608 7.79e-04 -0.138751 1.043 2.33e-04 0.03111
## 1609 3.33e-04 -0.050058 1.029 3.88e-05 0.00955
## 1615 -9.95e-05 -0.087743 1.032 1.03e-04 0.01695
## 1616 1.14e-02 -0.137995 1.012 1.58e-04 0.01605
## 1617 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 1620 -1.91e-04 -0.026141 1.045 1.28e-05 0.01910
## 1621 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 1637 -8.33e-04 -0.191988 1.024 2.98e-04 0.02919
## 1638 -2.22e-01 -0.246836 1.049 5.22e-04 0.05030
## 1650 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1654 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1665 2.26e-02 -0.194637 0.992 2.02e-04 0.01825
## 1670 3.33e-04 -0.050058 1.029 3.88e-05 0.00955
## 1671 2.20e-02 -0.140149 1.001 1.36e-04 0.01306
## 1675 2.65e-04 -0.045945 1.058 3.85e-05 0.03187
## 1688 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 1691 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 1695 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1698 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1704 1.05e-02 -0.209656 0.993 2.29e-04 0.02054
## 1705 1.05e-02 -0.209656 0.993 2.29e-04 0.02054
## 1711 2.26e-02 -0.194637 0.992 2.02e-04 0.01825
## 1719 9.42e-04 -0.078224 1.025 7.87e-05 0.01203
## 1723 -1.13e-03 -0.160613 1.038 2.72e-04 0.03122
## 1726 -1.52e-04 -0.050873 1.063 4.71e-05 0.03656
## 1749 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1752 6.13e-03 -0.128251 1.035 1.92e-04 0.02479
## 1754 -1.96e-03 -0.137209 1.029 1.99e-04 0.02333
## 1758 9.42e-04 -0.078224 1.025 7.87e-05 0.01203
## 1761 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1773 6.13e-03 -0.128251 1.035 1.92e-04 0.02479
## 1775 1.48e-02 -0.159190 1.002 1.69e-04 0.01600
## 1786 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1793 -3.59e-01 -0.443664 1.038 9.16e-04 0.07290
## 1799 1.14e-02 -0.141119 1.017 1.77e-04 0.01849
## 1803 1.97e-02 -0.220105 1.002 2.76e-04 0.02484
## 1806 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1807 9.30e-03 -0.168239 1.026 2.54e-04 0.02642
## 1808 1.96e-02 -0.129164 1.012 1.42e-04 0.01469
## 1814 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 1815 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1818 -2.19e-03 -0.095731 1.030 1.16e-04 0.01714
## 1827 9.30e-03 -0.168239 1.026 2.54e-04 0.02642
## 1834 1.17e-02 -0.077585 1.016 6.60e-05 0.00826
## 1835 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 1843 1.14e-02 -0.137995 1.012 1.58e-04 0.01605
## 1846 3.23e-02 -1.598541 1.281 7.06e-03 0.30807 *
## 1850 9.42e-04 -0.078224 1.025 7.87e-05 0.01203
## 1851 4.24e-03 -0.153693 1.015 1.94e-04 0.01957
## 1854 2.26e-02 -0.194637 0.992 2.02e-04 0.01825
## 1859 -9.95e-05 -0.087743 1.032 1.03e-04 0.01695
## 1861 2.20e-02 -0.140149 1.001 1.36e-04 0.01306
## 1866 9.42e-04 -0.078224 1.025 7.87e-05 0.01203
## 1873 -1.58e-03 -0.311513 1.174 1.23e-03 0.13965 *
## 1875 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1885 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 1892 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1895 2.20e-02 -0.140149 1.001 1.36e-04 0.01306
## 1896 -6.48e-03 -0.154266 1.034 2.47e-04 0.02827
## 1897 -3.80e-03 -0.185212 1.027 2.94e-04 0.02953
## 1899 -2.22e-01 -0.246836 1.049 5.22e-04 0.05030
## 1904 2.26e-02 -0.194637 0.992 2.02e-04 0.01825
## 1905 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 1908 2.20e-02 -0.140149 1.001 1.36e-04 0.01306
## 1916 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 1918 1.76e-02 -0.115138 1.017 1.29e-04 0.01441
## 1920 2.26e-02 -0.194637 0.992 2.02e-04 0.01825
## 1930 1.78e-02 -0.172193 1.011 2.18e-04 0.02093
## 1940 -2.71e-03 -0.126991 1.021 1.59e-04 0.01790
## 1947 1.20e-02 -0.151250 1.001 1.54e-04 0.01467
## 1949 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 1951 3.45e-04 -0.113731 1.022 1.37e-04 0.01636
## 1952 9.42e-04 -0.078224 1.025 7.87e-05 0.01203
## 1960 2.55e-03 -0.084629 1.028 9.29e-05 0.01434
## 9001 1.40e-02 -0.094784 1.008 7.99e-05 0.00856
## 9012 2.26e-02 -0.194637 0.992 2.02e-04 0.01825
## 9023 7.97e-03 -0.226631 1.001 2.85e-04 0.02558
## 9029 4.24e-03 -0.153693 1.015 1.94e-04 0.01957
## 6 5.70e-03 0.199665 1.010 5.49e-03 0.02447
## 12 3.93e-03 0.094112 1.048 6.35e-04 0.02844
## 43 -8.75e-03 0.093886 1.028 7.63e-04 0.01600
## 53 -1.61e-05 0.000972 1.060 3.66e-08 0.03201
## 67 -6.07e-05 0.448378 0.977 5.36e-02 0.04908
## 79 -3.22e-03 0.106776 1.020 1.16e-03 0.01434
## 122 -1.45e-02 0.155324 1.004 3.21e-03 0.01600
## 126 -7.26e-03 0.352119 0.904 3.75e-02 0.02094 *
## 133 -1.39e-04 0.000890 1.040 3.08e-08 0.01306
## 138 -1.90e-03 0.020368 1.042 1.94e-05 0.01600
## 154 4.34e-03 -0.046603 1.039 5.19e-05 0.01600
## 159 -1.49e-03 0.042367 1.051 9.42e-05 0.02558
## 174 -1.45e-02 0.155324 1.004 3.21e-03 0.01600
## 176 -8.03e-03 0.069287 1.038 3.30e-04 0.01825
## 181 -3.20e-03 0.064177 1.042 2.64e-04 0.02054
## 182 4.38e-04 0.100944 1.048 7.54e-04 0.02919
## 186 -2.56e-02 0.161160 1.016 3.11e-03 0.02111
## 189 9.13e-03 0.218794 1.013 6.65e-03 0.02844
## 204 -6.70e-03 0.044370 1.028 1.33e-04 0.00826
## 215 -6.70e-03 0.044370 1.028 1.33e-04 0.00826
## 232 5.48e-03 0.184352 1.004 4.76e-03 0.02018
## 233 -1.23e-03 0.008295 1.035 2.96e-06 0.00856
## 252 1.91e-02 0.419757 0.948 4.46e-02 0.03709
## 253 -1.45e-02 0.155324 1.004 3.21e-03 0.01600
## 274 -1.11e-02 0.075397 1.017 5.31e-04 0.00856
## 275 -3.68e-02 0.240227 0.941 1.35e-02 0.01441
## 287 6.28e-05 -0.015545 1.057 8.33e-06 0.02976
## 288 -1.64e-04 0.053964 1.038 1.83e-04 0.01636
## 325 3.39e-03 -0.032781 1.047 3.11e-05 0.02093
## 328 -1.11e-02 0.075397 1.017 5.31e-04 0.00856
## 344 -2.12e-05 0.002648 1.056 2.75e-07 0.02885
## 353 -1.05e-03 -0.115524 1.275 3.92e-04 0.19624 *
## 354 -6.70e-03 0.044370 1.028 1.33e-04 0.00826
## 367 -4.57e-03 0.051155 1.049 1.47e-04 0.02484
## 369 -1.51e-02 0.096374 1.021 8.86e-04 0.01306
## 390 -8.84e-03 0.097551 1.060 6.39e-04 0.03809
## 392 -4.02e-02 0.264713 0.923 1.84e-02 0.01469
## 423 5.55e-02 0.063199 1.088 2.07e-04 0.05836 *
## 432 -8.84e-03 0.097551 1.060 6.39e-04 0.03809
## 436 -1.14e-01 -0.140493 1.088 3.78e-04 0.06401 *
## 483 -3.77e-03 0.045502 1.039 1.21e-04 0.01605
## 513 8.14e-03 0.273551 0.954 1.65e-02 0.02018
## 516 -2.97e-04 0.006012 1.066 1.45e-06 0.03784
## 518 8.07e-04 0.037837 1.043 7.70e-05 0.01790
## 520 -2.60e-02 0.323064 0.907 3.05e-02 0.01849 *
## 526 -1.45e-02 0.155324 1.004 3.21e-03 0.01600
## 528 5.48e-03 0.184352 1.004 4.76e-03 0.02018
## 553 -3.45e-02 0.228236 0.882 1.68e-02 0.00826 *
## 576 8.47e-04 0.523015 1.118 4.08e-02 0.12610 *
## 611 -1.11e-02 0.075397 1.017 5.31e-04 0.00856
## 625 -2.42e-02 0.154614 0.993 3.52e-03 0.01306
## 635 9.61e-05 0.135395 1.045 1.59e-03 0.03216
## 646 -1.60e-03 0.044405 1.041 1.13e-04 0.01741
## 657 2.37e-01 0.279821 1.053 9.54e-03 0.05704
## 659 -6.67e-05 0.009609 1.053 3.83e-06 0.02546
## 666 -1.23e-03 0.008295 1.035 2.96e-06 0.00856
## 679 3.41e-03 -0.042798 1.038 4.46e-05 0.01467
## 729 -2.51e-03 0.021656 1.044 2.19e-05 0.01825
## 755 4.46e-04 0.019223 1.082 1.57e-05 0.05222 *
## 758 5.48e-03 0.184352 1.004 4.76e-03 0.02018
## 770 -2.26e-02 0.152557 0.966 4.31e-03 0.00856
## 786 4.38e-03 -0.061763 1.054 9.29e-05 0.02955
## 797 3.43e-03 0.084126 1.052 4.70e-04 0.03004
## 811 -7.90e-04 -0.071162 1.056 1.18e-04 0.03253
## 834 -2.26e-02 0.152557 0.966 4.31e-03 0.00856
## 858 5.27e-03 -0.137499 1.063 3.13e-04 0.04455
## 885 -8.75e-03 0.093886 1.028 7.63e-04 0.01600
## 893 -8.84e-03 0.097551 1.060 6.39e-04 0.03809
## 927 1.81e-03 -0.109400 1.050 2.07e-04 0.03201
## 928 -1.55e-01 -0.177764 1.083 4.97e-04 0.06405 *
## 933 2.77e-03 -0.043239 1.057 5.28e-05 0.03049
## 951 -8.03e-03 0.069287 1.038 3.30e-04 0.01825
## 968 -1.14e-03 0.095007 1.019 8.96e-04 0.01203
## 972 -1.75e-03 0.021924 1.040 2.30e-05 0.01467
## 975 -1.74e-02 0.226488 1.013 7.21e-03 0.02969
## 977 -5.18e-03 0.080982 1.053 4.26e-04 0.03049
## 981 -1.27e-02 0.133882 1.034 1.69e-03 0.02516
## 986 5.90e-03 -0.092331 1.051 1.65e-04 0.03049
## 1002 -1.33e-03 0.064366 1.043 2.66e-04 0.02094
## 1007 -1.45e-02 0.155324 1.004 3.21e-03 0.01600
## 1011 -7.41e-03 0.093096 1.026 7.70e-04 0.01467
## 1035 -3.38e-03 0.195037 1.045 3.98e-03 0.04032
## 1050 -9.87e-04 0.017858 1.053 1.40e-05 0.02642
## 1056 -2.37e-02 0.228839 0.984 9.10e-03 0.02093
## 1057 -6.70e-03 0.044370 1.028 1.33e-04 0.00826
## 1075 -8.03e-03 0.069287 1.038 3.30e-04 0.01825
## 1080 -1.65e-02 0.182464 1.044 3.38e-03 0.03809
## 1125 -1.90e-03 0.020368 1.042 1.94e-05 0.01600
## 1131 -4.23e-03 0.026984 1.038 3.71e-05 0.01306
## 1138 -2.29e-03 0.275785 1.296 5.04e-03 0.21424 *
## 1150 -1.22e-02 0.152645 1.000 3.19e-03 0.01467
## 1163 -1.32e-02 0.086174 1.027 6.30e-04 0.01441
## 1169 -6.70e-03 0.044370 1.028 1.33e-04 0.00826
## 1198 -7.51e-04 0.093763 1.048 6.25e-04 0.02885
## 1204 -4.49e-03 0.160144 1.008 3.45e-03 0.01787
## 1218 -1.61e-05 0.000972 1.060 3.66e-08 0.03201
## 1230 -8.03e-03 0.069287 1.038 3.30e-04 0.01825
## 1236 -7.41e-03 0.093096 1.026 7.70e-04 0.01467
## 1247 -8.03e-03 0.069287 1.038 3.30e-04 0.01825
## 1259 -3.20e-03 0.064177 1.042 2.64e-04 0.02054
## 1294 -3.45e-02 0.228236 0.882 1.68e-02 0.00826 *
## 1353 -3.24e-02 0.219067 0.898 1.42e-02 0.00856 *
## 1370 3.99e-03 -0.036305 1.058 3.94e-05 0.03164
## 1427 -9.92e-03 0.155057 1.038 2.36e-03 0.03049
## 1445 -4.82e-03 0.105025 1.046 8.38e-04 0.02888
## 1460 -1.51e-02 0.096374 1.021 8.86e-04 0.01306
## 1480 -1.39e-04 0.000890 1.040 3.08e-08 0.01306
## 1505 -2.97e-04 0.006012 1.066 1.45e-06 0.03784
## 1543 -5.18e-03 0.080982 1.053 4.26e-04 0.03049
## 1548 6.89e-04 -0.007397 1.043 1.96e-06 0.01600
## 1550 -1.03e-03 0.388245 0.909 4.52e-02 0.02554 *
## 1561 6.65e-03 -0.100773 1.050 1.84e-04 0.03063
## 1564 -1.01e-02 0.112737 1.039 1.06e-03 0.02484
## 1573 -1.45e-02 0.404231 0.826 6.91e-02 0.01741 *
## 1575 -1.40e-02 0.092774 1.007 9.74e-04 0.00826
## 1599 -6.67e-05 0.009609 1.053 3.83e-06 0.02546
## 1622 4.63e-01 0.514858 0.951 7.06e-02 0.05030 *
## 1629 2.32e-04 0.053393 1.054 1.58e-04 0.02919
## 1664 -4.57e-03 0.051155 1.049 1.47e-04 0.02484
## 1669 -7.23e-03 -0.197095 1.075 5.32e-04 0.06059
## 1674 5.08e-04 0.437233 1.262 1.72e-02 0.20284 *
## 1682 -2.26e-02 0.152557 0.966 4.31e-03 0.00856
## 1685 9.95e-04 0.229561 1.010 7.58e-03 0.02919
## 1697 -3.64e-04 0.119994 1.020 1.50e-03 0.01636
## 1716 -1.01e-03 -0.025184 1.062 2.07e-05 0.03426
## 1730 2.15e-03 -0.024084 1.051 1.85e-05 0.02484
## 1731 -1.23e-03 0.008295 1.035 2.96e-06 0.00856
## 1732 -3.20e-03 0.064177 1.042 2.64e-04 0.02054
## 1743 3.39e-03 -0.032781 1.047 3.11e-05 0.02093
## 1751 -7.90e-04 -0.071162 1.056 1.18e-04 0.03253
## 1757 -1.32e-02 0.086174 1.027 6.30e-04 0.01441
## 1763 -7.41e-03 0.093096 1.026 7.70e-04 0.01467
## 1766 -1.24e-02 0.259149 0.983 1.23e-02 0.02479
## 1772 -2.44e-04 -0.005971 1.058 1.32e-06 0.03004
## 1776 -1.40e-02 0.092774 1.007 9.74e-04 0.00826
## 1782 1.34e-01 0.172006 1.103 2.17e-03 0.07801 *
## 1784 -1.74e-02 0.226488 1.013 7.21e-03 0.02969
## 1791 -1.11e-02 0.075397 1.017 5.31e-04 0.00856
## 1831 -7.59e-04 0.015198 1.047 1.01e-05 0.02054
## 1840 -8.75e-03 0.093886 1.028 7.63e-04 0.01600
## 1844 -3.20e-03 0.064177 1.042 2.64e-04 0.02054
## 1856 -2.51e-03 0.021656 1.044 2.19e-05 0.01825
## 1876 -1.01e-02 0.112737 1.039 1.06e-03 0.02484
## 1929 -1.95e-02 0.127212 1.012 1.88e-03 0.01441
## 1935 -7.59e-04 0.015198 1.047 1.01e-05 0.02054
## 1938 5.15e-04 0.073018 1.055 3.29e-04 0.03122
## 1941 -1.51e-02 0.096374 1.021 8.86e-04 0.01306
## 1954 -2.58e-05 0.000272 1.052 2.85e-09 0.02516
## 1959 3.45e-03 -0.069108 1.041 9.79e-05 0.02054
## 9010 3.35e-04 -0.004038 1.043 6.04e-07 0.01605influencePlot(modefinal)
#a la luz de los criterios propuestos en la literatura no se observan datos que puedan considerarse influyentes para la base de datos Affairs
library(DescTools)
PseudoR2(modefinal,"Nagelkerke")## Nagelkerke
## 0.1349888#Existe una mala explicación del número de infedelidades por el modelo de regresión binomial negativa
round(exp(modefinal $coefficients),2)## (Intercept) ant not sli dsie tres
## 2.24 5.06 2.35 2.58 2.05 0.12
## cuat seis dosy trey very aver
## 0.06 0.01 0.23 0.55 0.18 0.28
## hapaver und ccer
## 0.37 393.56 2.301/0.12## [1] 8.3333331/0.06## [1] 16.666671/0.01## [1] 1001/0.23## [1] 4.3478261/0.55## [1] 1.8181821/0.18## [1] 5.5555561/0.28## [1] 3.5714291/0.37## [1] 2.702703#Se espera que una persona antireligiosa aumente el número de infelidads a razon de 5.06 #Se espera que una persona nada religiosa aumente el número de infelidads a razon de 2.35 #Se espera que una persona ligeramente religiosa aumente el número de infelidads a razon de 2.58 #Se espera que una persona con algun trabajo de pos grado aumente el número de infelidads a razon de 2.05 #Se espera que una persona con edad inferior a 20 años aumente el número de infelidads a razon de 393.56 #Se espera que una persona con edad entre 50-54 años aumente el número de infelidads a razon de 2.30 #Se espera que una persona con tres meses o menos de casado disminuya el número de infelidads a razon de 8.33 #Se espera que una persona con de 4 a 6 meses de casado disminuya el número de infelidades a razon de 16.6 #Se espera que una persona con de 6 meses a 1 año de casado disminuya el número de infelidades a razon de 100 # Se espera que una persona con entre 1 -2 años de casado disminuya el número de infelidades a razon de 4.34 # Se espera que una persona con entre 3 -5 años de casado disminuya el número de infelidades a razon de 1.81 # Se espera que una persona con autoevaluación del matrimonio muy feliz disminuya el número de infelidades a razon de 5.55 # Se espera que una persona con autoevaluación del matrimonio promedio disminuya el número de infelidades a razon de 3.57 ## Se espera que una persona con autoevaluación del matrimonio más feliz que el promedio disminuya el número de infelidades a razon de 2.7
Affairs#Estime el promedio esperado de infidelidades de un hombre con 37 a ̃nos, con 8 a ̃nos de casado, sin hijos, poco religioso, con un grado de escolaridad en el nivel de maestr ́ıa, muy feliz con su matrimonio y ocupaci ́on en el rango de 8.
#El promedio esperado de infidelidades de un hombre con 37 a ̃nos, con 8 a ̃nos de casado, sin hijos, poco religioso, con un grado de escolaridad en el nivel de maestr ́ıa, muy feliz con su matrimonio y ocupaci ́on en el rango de 8es de0.6329349 .