NELS 23 Schools and HLM
Carolyn J. Anderson
10/9/2021
These examples use the NELS data from 23 (non-random) sample from the full 1003 schools in the data set (See Kreft & de Leeuw text). lmer from the lme4 package will be used to fit model used REML and runjags will be used to fit Bayesian models. I'm an only using MLE for comparison, which some of you are familar with.
Some of these take a "long" time to run relative to the GIBBS we've done so far.
The variables in the data set are
| Variable | Description | Type | Level |
|---|---|---|---|
| SCHOOL | SCHOOL ID | integer | school |
| STUDENT | STUDENT ID | integer | student |
| SEX | STUDENT SEX | factor | student |
| RACE | STUDENT RACE | factor | student |
| HOMEW | TIME ON MATH HOMEWORK | numeric | student |
| factor | student | ||
| SCHTYPE | SCHOOL TYPE | factor | student |
| SES | SOCIOECONOMIC STATUS | numeric | student |
| PARED | PARENTAL EDUCATION | factor | student |
| numeric | student | ||
| MATH | MATH SCORE | numeric | student |
| WHITE | WHITE RACE BINARY | factor | student |
| CLASSSTR | CLASS STRUCTURE | factor | school |
| SCHSIZE | SCHOOL SIZE | numeric | school |
| URBAN | URBANICITY | factor | school |
| GEO | GEOGRAPHIC REGION | factor | school |
| MINORITY | PERCENT MINORITY | factor | school |
| RATIO | STUDENT-TEACHER RATIO | numeric | school |
| SCHTYPE | School type | factor | school |
| PUBLIC | PUBLIC SCHOOL BINARY | factor | school |
Set up
These are libraries that we'll use (at least most of them)
library(lme4) # MLE and REML of HLM
library(lmerTest) # test fixied effects
library(coda)
library(rjags)
library(runjags)
library(jagsUI)
library(mvtnorm) # For correlated random effectsSet working directory and read in the data
setwd("D:/Dropbox/edps 590BAY/Lectures/8 Multilevel models")
nels <- read.table("school23_data.txt",header=TRUE)
head(nels)## school student sex race homew schtype ses pared math classtr schsize urban
## 1 7472 3 2 4 1 1 -0.13 2 48 2 3 2
## 2 7472 8 1 4 0 1 -0.39 2 48 2 3 2
## 3 7472 13 1 4 0 1 -0.80 2 53 2 3 2
## 4 7472 17 1 4 1 1 -0.72 2 42 2 3 2
## 5 7472 27 2 4 2 1 -0.74 2 43 2 3 2
## 6 7472 28 2 4 1 1 -0.58 2 57 2 3 2
## geo minority ratio
## 1 2 0 19
## 2 2 0 19
## 3 2 0 19
## 4 2 0 19
## 5 2 0 19
## 6 2 0 19tail(nels)## school student sex race homew schtype ses pared math classtr schsize
## 514 72991 62 2 4 1 1 -1.01 3 55 4 7
## 515 72991 63 2 1 5 1 0.63 5 63 4 7
## 516 72991 70 2 4 4 1 0.56 4 67 4 7
## 517 72991 77 2 4 1 1 0.46 3 53 4 7
## 518 72991 87 1 4 1 1 0.06 3 56 4 7
## 519 72991 96 2 4 2 1 -0.05 2 53 4 7
## urban geo minority ratio
## 514 2 1 2 13
## 515 2 1 2 13
## 516 2 1 2 13
## 517 2 1 2 13
## 518 2 1 2 13
## 519 2 1 2 13nels$white[nels$race==4] <- 1
nels$white[nels$race<=3] <- 0
nels$white[nels$race>=5] <- 0
#
# ReCode schtype to private
#
nels$private <- NA
nels$private[nels$schtype==1] <- 0
nels$private[nels$schtype>=2] <- 1Various statistics
There are some that we need to set
# sort by schools:
nels <- nels[order(nels$school,nels$student) , ]
# Get information on number schools & create consecutive integer school.id
school <- unique(nels$school) #
N <- length(school) # need N and school.id
school.int <- as.data.frame(cbind(school,seq(1:N))) # for dataList & model
names(school.int) <- c("school", "school.id") #
nels <- merge(nels,school.int,by="school") #
# total number of students
n <- length(nels$math) # need n for dataList & model## Double-check set-up
head(nels,n=50)# You don't need this but might want it
nj <- matrix(999,nrow=N,ncol=1)
for (j in 1:N) {
nj[j] <- nrow(subset(nels,nels$school.id==j))
} Descriptive Statistics
Should compute and examine to learn more about the variables.
Graphing Data
We now have everything need to create a graph showing the variability between schools.
# Graph to illustrate need for random intercept and slope
plot(nels$homew,nels$math,
type="n",
col="blue",
lwd=2,
main="NELS: Linear Regression by School \n(for where there is data)",
xlab="Time Spent Doing Homework",
ylab="Math Scores"
)
for (j in 1:N) {
sub <- subset(nels,nels$school.id==j)
fitted <- fitted(lm(math~homew,sub))
lines(sub$homew,fitted,col=j)
} 
It looks like different intercepts and difference slopes for time spent doing homework.
Model 1: Simple Random intercept, i.e., : math ~ 1 + homew + (1 |school.id)
Now for our first model:
###################################################################
# Random intercept math~ 1 + homew + (1 |school.id) #
###################################################################
#
# MLE
#
ri1.lmer <- lmer(math~ 1 + homew + (1 |school.id), data=nels, REML=TRUE)We'll look at ri1.lmer later after we fit the model using Bayesian methods
#
# Bayesian
#
N = 23
n = length(nels$math)
dataList <- list(
y = nels$math,
hmwk = nels$homew,
school.id = nels$school.id,
N = N,
n = n,
sdY = sd(nels$math)
)
ri.mod1 <- "model {
for (i in 1:n) {
y[i] ~ dnorm(mu[i],precision)
mu[i] <- g0 + U0j[school.id[i]] + g1*hmwk[i]
}
for (j in 1:N) {
U0j[j] ~ dnorm(0,ptau)
}
g0 ~ dnorm(0,1/(100*sdY^2))
g1 ~ dnorm(0,1/(100*sdY^2))
tau ~ dunif(0.0001,200)
ptau <- 1/tau^2
sigma ~ dunif(0.0001,2000)
precision <- 1/sigma^2
}"
writeLines(ri.mod1, con="ri.mod1.txt")
start1 = list("g0"=mean(nels$math), "g1"=rnorm(1,1,3), "sigma"=sd(nels$math), "tau"=.5,
.RNG.name="base::Wichmann-Hill", .RNG.seed=523)
start2 = list("g0"=rnorm(1,0,3), "g1"=rnorm(1,-2,3), "sigma"=runif(1,.001,10), "tau"=runif(1,0.0001,10),
.RNG.name="base::Marsaglia-Multicarry", .RNG.seed=57)
start3 = list("g0"=rnorm(1,3,4), "g1"=rnorm(1,0,3), "sigma"=runif(1,.001,10), "tau"=runif(1,0.0001,10),
.RNG.name="base::Super-Duper", .RNG.seed=24)
start4 = list("g0"=rnorm(1,-3,10), "g1"=rnorm(1,5,3), "sigma"=runif(1,.001,10), "tau"=runif(1,0.0001,10),
.RNG.name="base::Mersenne-Twister", .RNG.seed=72100)
start <- list(start1,start2,start3,start4)When writing the model, it is easy to make mistakes. Rather than running code that could take a long time, a quick check of your code can save time. The following is a fast check.
##############################################################
# Fast way to check your model: #
# rjags and then run for sampling jagsUI with parallel. #
##############################################################
###################################################################################Since I have already run this model and know the code is OK, I did not run the above. So, let's use runjags (I'm lazy and runjags will compute statistics I want automatically, as well as able to do parallel).
ri.mod1.runjags <- run.jags(model=ri.mod1,
method="parallel",
monitor=c("g0","g1","sigma", "tau"),
data=dataList,
sample=10000,
n.chains=4,
inits=start)## Calling 4 simulations using the parallel method...
## Following the progress of chain 1 (the program will wait for all chains
## to finish before continuing):
## Welcome to JAGS 4.3.0 on Fri Oct 21 12:59:47 2022
## JAGS is free software and comes with ABSOLUTELY NO WARRANTY
## Loading module: basemod: ok
## Loading module: bugs: ok
## . . Reading data file data.txt
## . Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 519
## Unobserved stochastic nodes: 27
## Total graph size: 1726
## . Reading parameter file inits1.txt
## . Initializing model
## . Adapting 1000
## -------------------------------------------------| 1000
## ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
## Adaptation successful
## . Updating 4000
## -------------------------------------------------| 4000
## ************************************************** 100%
## . . . . . Updating 10000
## -------------------------------------------------| 10000
## ************************************************** 100%
## . . . . Updating 0
## . Deleting model
## .
## All chains have finished
## Simulation complete. Reading coda files...
## Coda files loaded successfully
## Calculating summary statistics...
## Calculating the Gelman-Rubin statistic for 4 variables....
## Finished running the simulationplot(ri.mod1.runjags)## Generating plots...
add.summary(ri.mod1.runjags)## Calculating summary statistics...
## Calculating the Gelman-Rubin statistic for 4 variables....##
## JAGS model summary statistics from 40000 samples (chains = 4; adapt+burnin = 5000):
##
## Lower95 Median Upper95 Mean SD Mode MCerr MC%ofSD SSeff
## g0 44.013 46.353 48.747 46.358 1.2107 -- 0.026071 2.2 2157
## g1 1.8415 2.396 2.918 2.3974 0.27629 -- 0.0032863 1.2 7068
## sigma 7.9238 8.4562 8.9841 8.4641 0.2701 -- 0.0017613 0.7 23517
## tau 3.2934 4.8073 6.81 4.9142 0.92902 -- 0.0080863 0.9 13199
##
## AC.10 psrf
## g0 0.32483 1.001
## g1 0.027204 1.0004
## sigma -0.0023297 1.0001
## tau 0.013877 1.0007
##
## Total time taken: 11.9 secondssummary(ri1.lmer) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: math ~ 1 + homew + (1 | school.id)
## Data: nels
##
## REML criterion at convergence: 3729.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5940 -0.7066 0.0053 0.6613 3.2075
##
## Random effects:
## Groups Name Variance Std.Dev.
## school.id (Intercept) 21.34 4.620
## Residual 71.28 8.443
## Number of obs: 519, groups: school.id, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 46.3558 1.1628 33.0125 39.866 <2e-16 ***
## homew 2.3999 0.2772 512.8988 8.658 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## homew -0.437Some graphics
#-------------
# Graph to illustrate need for random intercept and slope
par(mfrow=c(2,2))
plot(nels$homew,nels$math, type="n", lwd=2,
main="NELS: Linear Regression by School \n(for where there is data)",
xlab="Time Spent Doing Homework", ylab="Math Scores" )
for (j in 1:N) {
sub <- subset(nels,nels$school.id==j)
fitted <- fitted(lm(math~homew,sub))
lines(sub$homew,fitted,col=j)
}
# Compute conditional means (regressions)
S<-1
math.post <- matrix(999,nrow=S,ncol=n)
homework <- matrix(nels$homew,nrow=n,ncol=1)
for (s in 1:S) {
pointer <- 1
for (j in 1:N) {
U0 <-rnorm(1, 46.32, sd=4.9156)
for (i in 1:nj[j]) {
math.post[s,pointer] = U0 + 2.4006*homework[pointer]
pointer <- pointer + 1
}
}
}
nels$math.ri1 <- matrix(math.post,nrow=n,ncol=1)
plot(nels$homew,nels$math, type="n", lwd=2,
main="Fitted from Random Intercept \nmath~ 1 + homew + (1 |school.id)",
xlab="Time Spent Doing Homework", ylab="Math Scores" )
for (j in 1:N) {
sub <- subset(nels,nels$school.id==j)
lines(sub$homew,sub$math.ri1,col=j)
} 
We knew that we needed more than just a random intercept and the graph also shows this...data (left) versus conditional fitted values (right).
Model 2: Add random Slope
So we add a random slope, but we'll start with uncorrelated random effects.
Mone Carlo to obtain estmates of the random effects
Note coefficients used should match what you just got...
#### Better to estimate U within jags, but to get idea across ######
# Compute conditional regressions
S<-1
math.post <- matrix(999,nrow=S,ncol=n)
homework <- matrix(nels$homew,nrow=n,ncol=1)
for (s in 1:S) {
pointer <- 1
for (j in 1:N) {
U0 <-rnorm(1, 46.443, sd=7.5859)
U1 <-rnorm(1, 1.9677, sd=3.9674)
for (i in 1:nj[j]) { # for estimates include + rnorm(1,g0,sigma)
math.post[s,pointer] = U0 + U1*homework[pointer]
pointer <- pointer + 1
}
}
} Now we can plot stuff
nels$math.ris12 <- matrix(math.post,nrow=n,ncol=1)
plot(nels$homew,nels$math, type="n", lwd=2,
main="Fitted from Random Intercept & Slope \nmath~1+homew+(1|school.id)+(0+homew|school.id)",
xlab="Time Spent Doing Homework", ylab="Math Scores",
ylim=c(30,70) )
for (j in 1:N) {
sub <- subset(nels,nels$school.id==j)
lines(sub$homew,sub$math.ris1,col=j)
} 
This look a lot like the data, but we can do better by allowing random intercept and random slope to be correlated.
Model 3: correlated random effects
#############################################################
# Random intercept & slope Wishart#
# math~ 1 + homew + (1 + homew|school.id) #
# with correlated random effects: use wishart #
#############################################################
ris2.lmer <- lmer(math~ 1 + homew + (1 + homew|school.id), data=nels, REML=TRUE)
summary(ris2.lmer)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: math ~ 1 + homew + (1 + homew | school.id)
## Data: nels
##
## REML criterion at convergence: 3635.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.22684 -0.70441 0.00352 0.65894 2.75155
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## school.id (Intercept) 62.42 7.901
## homew 17.73 4.210 -0.83
## Residual 53.29 7.300
## Number of obs: 519, groups: school.id, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 46.3256 1.7589 22.0002 26.338 <2e-16 ***
## homew 1.9802 0.9284 19.9178 2.133 0.0456 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## homew -0.824dataList <- list(
y = nels$math,
hmwk = nels$homew,
school.id=nels$school.id,
n = n,
N = N,
sdY = sd(nels$math)
)
re.mod2 <- "model {
# Likelihood: the data model
for (i in 1:n) {
y[i] ~ dnorm(meanY[i],precision)
meanY[i] <- betaj[school.id[i],1] + betaj[school.id[i],2]*hmwk[i]
}
# Random Effects -- note multivariate normal density is used
for (j in 1:N) {
betaj[j,1:2] ~ dmnorm(mu[1:2],Omega[1:2,1:2])
}
# Priors
precision ~ dgamma(0.01,0.01)
sigma <- 1/sqrt(precision)
mu[1] ~ dnorm(0,1/(100*sdY^2))# or mu[i]<- 0 and add g0 & g1 to meanY
mu[2] ~ dnorm(0,1/(100*sdY^2))# and add g0<- dnorm(0,1,(100*sdY^2))
# & for g1. This will make it easier
# to add models for intercept & slopes.
# See next model.
Omega[1:2,1:2] ~ dwish(R[,],2.1)
R[1,1] <- 1/2.1
R[1,2] <- 0 # Un-correlated--there are alternative
R[2,1] <- 0 # ways of setting this up by just adding
R[2,2] <- 1/2.1 # uni-variate distribution for each tau
Tau <- inverse(Omega) # This turn into our taus.
}"
writeLines(re.mod2, con="re.mod2.txt")
start1 = list("precision"=1, .RNG.name="base::Wichmann-Hill", .RNG.seed=523)
start2 = list("precision"=1, .RNG.name="base::Marsaglia-Multicarry", .RNG.seed=57)
start3 = list("precision"=1, .RNG.name="base::Super-Duper", .RNG.seed=24)
start4 = list("precision"=1, .RNG.name="base::Mersenne-Twister", .RNG.seed=72100)
start <- list(start1,start2,start3,start4)
##########################################################################
# Fast way to check your model:
# rjags and then run for sampling runjags with parallel.
##########################################################################
re.mod2x <- jags.model(file="re.mod2.txt", # compiles and initializes model
data=dataList,
inits=start1,
n.chains=4,
n.adapt=500) ## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 519
## Unobserved stochastic nodes: 27
## Total graph size: 1882
##
## Initializing model#########################################################################
re.mod2.runjags <- run.jags(model=re.mod2,
method="parallel",
monitor=c("mu","sigma","Tau"),
data=dataList,
sample=20000,
n.chains=4,
inits=start)## Calling 4 simulations using the parallel method...
## Following the progress of chain 1 (the program will wait for all chains
## to finish before continuing):
## Welcome to JAGS 4.3.0 on Fri Oct 21 13:01:09 2022
## JAGS is free software and comes with ABSOLUTELY NO WARRANTY
## Loading module: basemod: ok
## Loading module: bugs: ok
## . . Reading data file data.txt
## . Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 519
## Unobserved stochastic nodes: 27
## Total graph size: 1882
## . Reading parameter file inits1.txt
## . Initializing model
## . Adaptation skipped: model is not in adaptive mode.
## . Updating 4000
## -------------------------------------------------| 4000
## ************************************************** 100%
## . . . . Updating 20000
## -------------------------------------------------| 20000
## ************************************************** 100%
## . . . . Updating 0
## . Deleting model
## .
## All chains have finished
## Note: the model did not require adaptation
## Simulation complete. Reading coda files...
## Coda files loaded successfully
## Calculating summary statistics...
## Calculating the Gelman-Rubin statistic for 7 variables....
## Note: Unable to calculate the multivariate psrf
## Finished running the simulationadd.summary(re.mod2.runjags)## Calculating summary statistics...
## Calculating the Gelman-Rubin statistic for 7 variables....
## Note: Unable to calculate the multivariate psrf##
## JAGS model summary statistics from 80000 samples (chains = 4; adapt+burnin = 5000):
##
## Lower95 Median Upper95 Mean SD Mode MCerr MC%ofSD SSeff
## mu[1] 42.763 46.315 49.771 46.297 1.7731 -- 0.016651 0.9 11339
## mu[2] 0.17778 2.0086 3.8683 2.0082 0.93417 -- 0.0087361 0.9 11434
## sigma 6.869 7.3201 7.8037 7.3259 0.23938 -- 0.00093184 0.4 65994
## Tau[1,1] 25.819 58.765 108.79 62.992 23.233 -- 0.11 0.5 44609
## Tau[2,1] -50.177 -25.674 -9.5123 -27.737 11.38 -- 0.055013 0.5 42791
## Tau[1,2] -50.177 -25.674 -9.5123 -27.737 11.38 -- 0.055013 0.5 42791
## Tau[2,2] 7.1918 16.574 31.316 17.825 6.7644 -- 0.033606 0.5 40516
##
## AC.10 psrf
## mu[1] 0.062008 1.0004
## mu[2] 0.058839 1.0003
## sigma 0.0022849 0.99999
## Tau[1,1] -0.0037175 1.0001
## Tau[2,1] 0.002518 1.0001
## Tau[1,2] 0.002518 1.0001
## Tau[2,2] 0.0056721 1.0001
##
## Total time taken: 10.9 secondsplot(re.mod2.runjags)## Generating plots...
summary(ris2.lmer)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: math ~ 1 + homew + (1 + homew | school.id)
## Data: nels
##
## REML criterion at convergence: 3635.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.22684 -0.70441 0.00352 0.65894 2.75155
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## school.id (Intercept) 62.42 7.901
## homew 17.73 4.210 -0.83
## Residual 53.29 7.300
## Number of obs: 519, groups: school.id, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 46.3256 1.7589 22.0002 26.338 <2e-16 ***
## homew 1.9802 0.9284 19.9178 2.133 0.0456 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## homew -0.824The frequentist estimated more 'failed to converge'. It doesn't look that bad and we might be able to get it to converge if we change the optimization method.
Let's look at what fitted values look like conditional on random effects
## Some Monte Carlo
############################################################
# Better to estimate U within jags, but to get idea across #
# Compute conditional regressions #
############################################################
S<- 1
math.post <- matrix(999,nrow=S,ncol=n)
homework <- matrix(nels$homew,nrow=n,ncol=1)
Tau <- matrix(c(63.01,-27.717,-27.717,17.804),nrow=2,ncol=2)
for (s in 1:S) {
pointer <- 1
for (j in 1:N) {
Uj <- rmvnorm(1,mean=c(46.296,2.0062),sigma=Tau)
for (i in 1:nj[j]) {
math.post[s,pointer] = Uj[1,1] + Uj[1,2]*homework[pointer] # for estimates include + rnorm(1,g0,sigma)
pointer <- pointer + 1
}
}
}
nels$math.ris2 <- matrix(math.post,nrow=n,ncol=1)
plot(nels$homew,nels$math,
type="n",
col="blue",
lwd=2,
main="Fitted from Random Intercept \nmath~ 1 + homew + (1 + homew|school.id)",
xlab="Time Spent Doing Homework",
ylab="Math Scores",
ylim=c(30,70)
)
for (j in 1:N) {
sub <- subset(nels,nels$school.id==j)
lines(sub$homew,sub$math.ris2,col=j)
} 
Model 4: Cross-level Interaction
Now for model 3:
#############################################################################
# Random intercept & slope with more predictors including a cross-level #
# interaction #
# math~ 1 + homew + ses + public + homew*public +(1 + homew|school.id) #
# #
#############################################################################
# Turn into dummy codes
nels$public <- ifelse(nels$schtype==1,1,0)
ris3.lmer <- lmer(math~ 1 + homew + ses + public + homew*public
+ (1 + homew|school.id), data=nels, REML=TRUE)
summary(ris3.lmer)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: math ~ 1 + homew + ses + public + homew * public + (1 + homew |
## school.id)
## Data: nels
##
## REML criterion at convergence: 3598.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3958 -0.6789 -0.0247 0.6389 2.9930
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## school.id (Intercept) 53.67 7.326
## homew 15.49 3.936 -0.87
## Residual 51.40 7.169
## Number of obs: 519, groups: school.id, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 47.5428 2.8214 21.5142 16.851 7.09e-14 ***
## homew 2.3253 1.4742 18.6157 1.577 0.132
## ses 2.6621 0.5071 496.4473 5.250 2.26e-07 ***
## public -0.8167 3.4978 21.7740 -0.233 0.818
## homew:public -0.7524 1.8288 18.8461 -0.411 0.685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) homew ses public
## homew -0.856
## ses -0.068 0.000
## public -0.812 0.691 0.135
## homew:publc 0.692 -0.806 -0.026 -0.856dataList <- list(
math = nels$math,
hmwk = nels$homew,
ses = nels$ses,
public=nels$public,
school.id=nels$school.id,
n = n,
N = N,
sdY = sd(nels$math)
)
re.mod3 <- "model {
# Likelihood: the data model
for (i in 1:n) {
math[i] ~ dnorm(mmath[i],precision)
mmath[i] <- g0 + Uj[school.id[i],1] + g3*public[i]
+ (g10 + Uj[school.id[i],2] + g11*public[i])*hmwk[i] + g2*ses[i]
}
# Random Effects -- note multivariate normal density is used
for (j in 1:N) {
Uj[j,1:2] ~ dmnorm(mu[1:2],Omega[1:2,1:2])
}
# Priors
precision ~ dgamma(0.01,0.01)
sigma <- 1/sqrt(precision)
g0 ~ dnorm(0,1/(100*sdY^2))
g2 ~ dnorm(0,1/(100*sdY^2))
g3 ~ dnorm(0,1/(100*sdY^2))
g10 ~ dnorm(0,1/(100*sdY^2))
g11 ~ dnorm(0,1/(100*sdY^2))
mu[1] <- 0 # This identifies the model
mu[2] <- 0 #
Omega[1:2,1:2] ~ dwish(R[,],2.1)
R[1,1] <- 1/2.1
R[1,2] <- 0
R[2,1] <- 0
R[2,2] <- 1/2.1
Tau <- inverse(Omega)
}"
writeLines(re.mod3, con="re.mod3.txt")
start1 = list("precision"=1, "g0"=rnorm(1,0,10),"g2"=rnorm(1,0,10), "g3"=rnorm(1,0,10),"g10"=rnorm(1,0,10), "g11"=rnorm(1,0,10), .RNG.name="base::Wichmann-Hill", .RNG.seed=523)
start2 = list("precision"=0.5, "g0"=rnorm(1,0,10),"g2"=rnorm(1,0,10), "g3"=rnorm(1,0,10),"g10"=rnorm(1,0,10), "g11"=rnorm(1,0,10), .RNG.name="base::Marsaglia-Multicarry", .RNG.seed=57)
start3 = list("precision"=0.05,"g0"=rnorm(1,0,10),"g2"=rnorm(1,0,10), "g3"=rnorm(1,0,10),"g10"=rnorm(1,0,10), "g11"=rnorm(1,0,10),.RNG.name="base::Super-Duper", .RNG.seed=24)
start4 = list("precision"=.002,"g0"=rnorm(1,0,10),"g2"=rnorm(1,0,10), "g3"=rnorm(1,0,10),"g10"=rnorm(1,0,10), "g11"=rnorm(1,0,10),.RNG.name="base::Mersenne-Twister", .RNG.seed=72100)
start <- list(start1,start2,start3,start4)If you want to check whether model is OK, then run
##################################################################################
# Fast way to check your model: rjags and then run for sampling runjags with parallel.
re.mod3 <- jags.model(file="re.mod3.txt", # compiles and initializes model
data=dataList,
inits=start1,
n.chains=4,
n.adapt=500)
###################################################################################If model is OK, then run jags
re.mod3.runjags <- run.jags(model=re.mod3,
method="parallel",
monitor=c("g0", "g10", "g11","g2","g3","sigma","Tau"),
data=dataList,
sample=10000,
n.chains=4,
inits=start,
thin=10)## Calling 4 simulations using the parallel method...
## Following the progress of chain 1 (the program will wait for all chains
## to finish before continuing):
## Welcome to JAGS 4.3.0 on Fri Oct 21 13:01:34 2022
## JAGS is free software and comes with ABSOLUTELY NO WARRANTY
## Loading module: basemod: ok
## Loading module: bugs: ok
## . . Reading data file data.txt
## . Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 519
## Unobserved stochastic nodes: 30
## Total graph size: 3589
## . Reading parameter file inits1.txt
## . Initializing model
## . Adaptation skipped: model is not in adaptive mode.
## . Updating 4000
## -------------------------------------------------| 4000
## ************************************************** 100%
## . . . . . . . . Updating 100000
## -------------------------------------------------| 100000
## ************************************************** 100%
## . . . . Updating 0
## . Deleting model
## .
## All chains have finished
## Note: the model did not require adaptation
## Simulation complete. Reading coda files...
## Coda files loaded successfully
## Calculating summary statistics...
## Calculating the Gelman-Rubin statistic for 10 variables....
## Finished running the simulation#
# run these in class but not for Rmd
plot(re.mod3.runjags) ## Generating plots...
add.summary(re.mod3.runjags)## Calculating summary statistics...
## Calculating the Gelman-Rubin statistic for 10 variables....##
## JAGS model summary statistics from 40000 samples (thin = 10; chains = 4; adapt+burnin = 5000):
##
## Lower95 Median Upper95 Mean SD Mode MCerr MC%ofSD
## g0 41.799 47.527 53.035 47.521 2.8278 -- 0.075818 2.7
## g10 -0.60935 2.3746 5.284 2.3585 1.4907 -- 0.042137 2.8
## g11 -4.3822 -0.78675 2.8987 -0.77688 1.8603 -- 0.049439 2.7
## g2 1.7472 2.7164 3.7672 2.7183 0.51747 -- 0.0025916 0.5
## g3 -7.8445 -0.78208 6.0633 -0.77442 3.5154 -- 0.090794 2.6
## sigma 6.7393 7.1925 7.6652 7.1988 0.2368 -- 0.0011791 0.5
## Tau[1,1] 21.382 49.65 95.028 53.556 20.86 -- 0.14535 0.7
## Tau[2,1] -45.65 -23.198 -8.6363 -25.182 10.503 -- 0.079098 0.8
## Tau[1,2] -45.65 -23.198 -8.6363 -25.182 10.503 -- 0.079098 0.8
## Tau[2,2] 5.9112 14.312 27.535 15.502 6.1596 -- 0.047023 0.8
##
## SSeff AC.100 psrf
## g0 1391 0.47969 1.0029
## g10 1252 0.53656 1.0028
## g11 1416 0.48663 1.003
## g2 39869 0.00027972 1
## g3 1499 0.4476 1.003
## sigma 40331 0.0051787 1
## Tau[1,1] 20596 0.019269 1
## Tau[2,1] 17632 0.027593 1.0001
## Tau[1,2] 17632 0.027593 1.0001
## Tau[2,2] 17159 0.030199 0.99997
##
## Total time taken: 41 secondssummary(ris3.lmer)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: math ~ 1 + homew + ses + public + homew * public + (1 + homew |
## school.id)
## Data: nels
##
## REML criterion at convergence: 3598.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3958 -0.6789 -0.0247 0.6389 2.9930
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## school.id (Intercept) 53.67 7.326
## homew 15.49 3.936 -0.87
## Residual 51.40 7.169
## Number of obs: 519, groups: school.id, 23
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 47.5428 2.8214 21.5142 16.851 7.09e-14 ***
## homew 2.3253 1.4742 18.6157 1.577 0.132
## ses 2.6621 0.5071 496.4473 5.250 2.26e-07 ***
## public -0.8167 3.4978 21.7740 -0.233 0.818
## homew:public -0.7524 1.8288 18.8461 -0.411 0.685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) homew ses public
## homew -0.856
## ses -0.068 0.000
## public -0.812 0.691 0.135
## homew:publc 0.692 -0.806 -0.026 -0.856Model evaluation: Mdel 4
I will just put in large chunks of code and we'll discuss in class what they do.
dataList <- list( math = nels$math,
hmwk = nels$homew,
ses = nels$ses,
public=nels$public,
school.id=nels$school.id,
n = n,
N = N,
sdY = sd(nels$math)
)
re.mod4 <- "model {
# Likelihood: the data model
for (i in 1:n) {
math[i] ~ dnorm(mmath[i],precision)
mmath[i] <- g00 + Uj[school.id[i],1] + g3*public[i]
+ (g10 + Uj[school.id[i],2] + g11*public[i])*hmwk[i] + g2*ses[i]
intercept.blup[i] <- Uj[school.id[i],1] # intercept random effect(posterior)
slope.blup[i] <- Uj[school.id[i],2] # slope random effect
marg.post[i] ~ dnorm(mmath[i],precision)
}
# Random Effects -- note multivariate normal density is used
for (j in 1:N) {
Uj[j,1:2] ~ dmnorm(mu[1:2],Omega[1:2,1:2])
}
# Priors
precision ~ dgamma(0.01,0.01)
sigma <- 1/sqrt(precision)
g00 ~ dnorm(0,1/(100*sdY^2))
g2 ~ dnorm(0,1/(100*sdY^2))
g3 ~ dnorm(0,1/(100*sdY^2))
g10 ~ dnorm(0,1/(100*sdY^2))
g11 ~ dnorm(0,1/(100*sdY^2))
mu[1] <- 0 # This identifies the model
mu[2] <- 0 #
Omega[1:2,1:2] ~ dwish(R[,],2.1)
R[1,1] <- 1/2.1
R[1,2] <- 0
R[2,1] <- 0
R[2,2] <- 1/2.1
Tau <- inverse(Omega)
}"
writeLines(re.mod4, con="re.mod4.txt")
start1 = list("precision"=1, "g00"=rnorm(1,0,10),"g2"=rnorm(1,0,10), "g3"=rnorm(1,0,10),"g10"=rnorm(1,0,10), "g11"=rnorm(1,0,10), .RNG.name="base::Wichmann-Hill", .RNG.seed=523)
start2 = list("precision"=0.5, "g00"=rnorm(1,0,10),"g2"=rnorm(1,0,10), "g3"=rnorm(1,0,10),"g10"=rnorm(1,0,10), "g11"=rnorm(1,0,10), .RNG.name="base::Marsaglia-Multicarry", .RNG.seed=57)
start3 = list("precision"=0.05,"g00"=rnorm(1,0,10),"g2"=rnorm(1,0,10), "g3"=rnorm(1,0,10),"g10"=rnorm(1,0,10), "g11"=rnorm(1,0,10),.RNG.name="base::Super-Duper", .RNG.seed=24)
start4 = list("precision"=.002,"g00"=rnorm(1,0,10),"g2"=rnorm(1,0,10), "g3"=rnorm(1,0,10),"g10"=rnorm(1,0,10), "g11"=rnorm(1,0,10),.RNG.name="base::Mersenne-Twister", .RNG.seed=72100)
start <- list(start1,start2,start3,start4)Now get samples but monitor main effects (otherwise I have 1567 variables x 15000 samples).
re.mod4.runjags <- run.jags(model=re.mod4,
method="parallel",
monitor=c("g00", "g10", "g11","g2","g3","sigma","Tau"),
data=dataList,
sample=15000,
n.chains=4,
inits=start,
thin=10)## Calling 4 simulations using the parallel method...
## Following the progress of chain 1 (the program will wait for all chains
## to finish before continuing):
## Welcome to JAGS 4.3.0 on Fri Oct 21 13:02:31 2022
## JAGS is free software and comes with ABSOLUTELY NO WARRANTY
## Loading module: basemod: ok
## Loading module: bugs: ok
## . . Reading data file data.txt
## . Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 519
## Unobserved stochastic nodes: 549
## Total graph size: 4108
## . Reading parameter file inits1.txt
## . Initializing model
## . Adaptation skipped: model is not in adaptive mode.
## . Updating 4000
## -------------------------------------------------| 4000
## ************************************************** 100%
## . . . . . . . . Updating 150000
## -------------------------------------------------| 150000
## ************************************************** 100%
## . . . . Updating 0
## . Deleting model
## .
## All chains have finished
## Note: the model did not require adaptation
## Simulation complete. Reading coda files...
## Coda files loaded successfully
## Calculating summary statistics...
## Calculating the Gelman-Rubin statistic for 10 variables....
## Note: Unable to calculate the multivariate psrf
## Finished running the simulationplot(re.mod4.runjags)## Generating plots...
gelman.plot(re.mod4.runjags)
geweke.diag(re.mod4.runjags, frac1=.10, frac2=.50)## Warning in as.mcmc.runjags(x): Combining the 4 mcmc chains together##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## g00 g10 g11 g2 g3 sigma Tau[1,1] Tau[2,1]
## -0.3012 0.2295 -0.8119 0.2843 0.8332 -1.0569 0.2790 -0.1111
## Tau[1,2] Tau[2,2]
## -0.1111 0.0416add.summary(re.mod4.runjags) ## Calculating summary statistics...
## Calculating the Gelman-Rubin statistic for 10 variables....
## Note: Unable to calculate the multivariate psrf##
## JAGS model summary statistics from 60000 samples (thin = 10; chains = 4; adapt+burnin = 5000):
##
## Lower95 Median Upper95 Mean SD Mode MCerr MC%ofSD
## g00 41.795 47.539 53.041 47.536 2.8344 -- 0.061109 2.2
## g10 -0.59582 2.3654 5.3033 2.3497 1.4974 -- 0.034698 2.3
## g11 -4.412 -0.78888 2.904 -0.77363 1.8666 -- 0.040551 2.2
## g2 1.7138 2.7151 3.7298 2.7173 0.51662 -- 0.0021245 0.4
## g3 -7.9939 -0.77124 5.9611 -0.77739 3.5257 -- 0.07378 2.1
## sigma 6.7375 7.1929 7.6655 7.1994 0.237 -- 0.0009638 0.4
## Tau[1,1] 21.425 49.782 94.932 53.616 20.784 -- 0.11942 0.6
## Tau[2,1] -45.636 -23.233 -8.6917 -25.218 10.482 -- 0.065445 0.6
## Tau[1,2] -45.636 -23.233 -8.6917 -25.218 10.482 -- 0.065445 0.6
## Tau[2,2] 5.9099 14.337 27.569 15.52 6.1643 -- 0.038944 0.6
##
## SSeff AC.100 psrf
## g00 2151 0.47562 1.001
## g10 1862 0.53461 1.0011
## g11 2119 0.48597 1.0015
## g2 59134 0.00089906 1
## g3 2284 0.44322 1.0015
## sigma 60467 0.001504 1
## Tau[1,1] 30289 0.020009 0.99999
## Tau[2,1] 25652 0.028027 1.0001
## Tau[1,2] 25652 0.028027 1.0001
## Tau[2,2] 25055 0.031423 1
##
## Total time taken: 1 minutesNow monitor all
re.mod4.runjags <- run.jags(model=re.mod4,
method="parallel",
monitor=c("g00", "g10", "g11", "g2", "g3", "sigma", "Tau", "marg.post", "intercept.blup", "slope.blup"),
data=dataList,
sample=15000,
n.chains=4,
inits=start,
thin=10)## Calling 4 simulations using the parallel method...
## Following the progress of chain 1 (the program will wait for all chains
## to finish before continuing):
## Welcome to JAGS 4.3.0 on Fri Oct 21 13:03:52 2022
## JAGS is free software and comes with ABSOLUTELY NO WARRANTY
## Loading module: basemod: ok
## Loading module: bugs: ok
## . . Reading data file data.txt
## . Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 519
## Unobserved stochastic nodes: 549
## Total graph size: 4108
## . Reading parameter file inits1.txt
## . Initializing model
## . Adaptation skipped: model is not in adaptive mode.
## . Updating 4000
## -------------------------------------------------| 4000
## ************************************************** 100%
## . . . . . . . . . . . Updating 150000
## -------------------------------------------------| 150000
## ************************************************** 100%
## . . . . Updating 0
## . Deleting model
## .
## All chains have finished
## Note: the model did not require adaptation
## Simulation complete. Reading coda files...
## Coda files loaded successfully
## Note: Summary statistics were not produced as there are >50 monitored
## variables
## [To override this behaviour see ?add.summary and ?runjags.options]
## FALSEFinished running the simulationadd.summary(re.mod4.runjags) ## Calculating summary statistics...
## Calculating the Gelman-Rubin statistic for 1567 variables....
## Note: Unable to calculate the multivariate psrf##
## JAGS model summary statistics from 60000 samples (thin = 10; chains = 4; adapt+burnin = 5000):
##
## Lower95 Median Upper95 Mean SD Mode
## g00 41.815 47.519 52.968 47.485 2.8097 --
## g10 -0.60784 2.3602 5.1994 2.3766 1.4631 --
## g11 -4.6068 -0.7703 2.7047 -0.79523 1.8264 --
## g2 1.6986 2.7134 3.7157 2.7153 0.5162 --
## g3 -7.9198 -0.77065 6.1481 -0.73417 3.518 --
## sigma 6.7351 7.193 7.6547 7.1992 0.23578 --
## Tau[1,1] 21.036 49.563 94.504 53.485 20.84 --
## Tau[2,1] -45.337 -23.192 -8.4883 -25.114 10.417 --
## Tau[1,2] -45.337 -23.192 -8.4883 -25.114 10.417 --
## Tau[2,2] 5.8449 14.287 27.371 15.438 6.0872 --
## marg.post[1] 39.399 53.825 68.123 53.834 7.3159 --
## marg.post[2] 38.357 52.701 66.951 52.684 7.3063 --
## marg.post[3] 39.55 54.345 68.273 54.313 7.3163 --
## marg.post[4] 39.919 54.292 68.535 54.273 7.3302 --
## marg.post[5] 39.522 53.751 68.194 53.748 7.2983 --
## marg.post[6] 40.83 54.997 69.567 55.022 7.3203 --
## marg.post[7] 37.02 51.178 65.624 51.16 7.3216 --
## marg.post[8] 46.201 60.758 75.154 60.774 7.3806 --
## marg.post[9] 39.023 52.941 67.418 52.947 7.2703 --
## marg.post[10] 49.702 64.684 79.126 64.696 7.5029 --
## marg.post[11] 40.691 54.929 69.236 54.935 7.2884 --
## marg.post[12] 37.68 52.047 66.686 52.037 7.41 --
## marg.post[13] 45.613 59.739 74.417 59.738 7.3521 --
## marg.post[14] 40.328 54.792 69.066 54.797 7.324 --
## marg.post[15] 39.042 53.745 67.492 53.728 7.2875 --
## marg.post[16] 39.295 53.68 68.058 53.717 7.325 --
## marg.post[17] 38.839 53.029 67.573 53.05 7.3174 --
## marg.post[18] 43.694 58.228 72.148 58.214 7.2839 --
## marg.post[19] 37.787 51.938 66.49 51.948 7.3208 --
## marg.post[20] 43.645 57.672 72.256 57.679 7.2851 --
## marg.post[21] 39.283 53.835 67.91 53.809 7.3098 --
## marg.post[22] 38.289 52.551 66.942 52.564 7.3154 --
## marg.post[23] 41.115 55.712 69.573 55.663 7.2568 --
## marg.post[24] 48.96 63.768 78.976 63.756 7.6732 --
## marg.post[25] 37.932 52.534 66.631 52.53 7.3416 --
## marg.post[26] 45.91 60.391 75.176 60.398 7.4532 --
## marg.post[27] 41.747 56.547 70.815 56.512 7.4112 --
## marg.post[28] 40.516 54.97 69.222 55.009 7.3218 --
## marg.post[29] 39.297 53.446 68.039 53.437 7.3067 --
## marg.post[30] 47.647 62.125 76.575 62.113 7.4056 --
## marg.post[31] 38.655 52.863 67.197 52.855 7.3206 --
## marg.post[32] 38.564 52.854 67.251 52.836 7.3207 --
## marg.post[33] 43.618 57.97 72.162 57.973 7.2929 --
## marg.post[34] 52.999 68.81 84.303 68.821 7.9678 --
## marg.post[35] 43.957 59.029 72.993 59.006 7.3697 --
## marg.post[36] 41.331 55.798 70.265 55.748 7.3598 --
## marg.post[37] 45.525 60.183 74.457 60.174 7.3711 --
## marg.post[38] 45.504 59.783 74.393 59.791 7.3913 --
## marg.post[39] 41.084 55.28 69.707 55.259 7.3125 --
## marg.post[40] 39.886 53.939 68.391 53.937 7.2855 --
## marg.post[41] 46.762 61.149 76.253 61.162 7.5167 --
## marg.post[42] 40.028 54.487 68.52 54.52 7.284 --
## marg.post[43] 39.094 53.223 67.463 53.206 7.2514 --
## marg.post[44] 41.409 55.969 70.09 55.97 7.294 --
## marg.post[45] 42.331 57.047 71.861 57.071 7.529 --
## marg.post[46] 41.028 55.864 70.591 55.884 7.5359 --
## marg.post[47] 40.265 55.101 69.783 55.11 7.5223 --
## marg.post[48] 44.82 60.498 75.503 60.501 7.8472 --
## marg.post[49] 45.895 61.413 76.49 61.444 7.8273 --
## marg.post[50] 27.877 44.746 61.783 44.734 8.6406 --
## marg.post[51] 46.568 61.848 77.066 61.858 7.819 --
## marg.post[52] 45.148 60.678 75.816 60.658 7.8096 --
## marg.post[53] 43.593 59.341 74.483 59.348 7.8686 --
## marg.post[54] 26.344 43.142 59.259 43.13 8.4221 --
## marg.post[55] 43.39 58.804 74.315 58.823 7.8825 --
## marg.post[56] 51.636 68.721 85.104 68.744 8.5497 --
## marg.post[57] 31.262 46.463 61.83 46.437 7.8101 --
## marg.post[58] 32.715 47.064 61.414 47.074 7.3658 --
## marg.post[59] 37.631 52.358 66.65 52.38 7.4009 --
## marg.post[60] 33.581 48.014 62.479 48.034 7.3707 --
## marg.post[61] 30.148 44.963 60.003 44.979 7.6342 --
## marg.post[62] 35.109 49.424 64.075 49.437 7.3956 --
## marg.post[63] 32.607 46.93 61.338 46.944 7.3537 --
## marg.post[64] 32.637 47.149 61.485 47.121 7.3834 --
## marg.post[65] 35.806 50.355 64.529 50.357 7.3838 --
## marg.post[66] 34.796 49.125 63.464 49.142 7.3299 --
## marg.post[67] 35.347 49.536 64.15 49.521 7.3417 --
## marg.post[68] 35.008 49.958 64.952 49.98 7.6509 --
## marg.post[69] 38.075 53.048 68.094 53.093 7.6521 --
## marg.post[70] 35.637 49.984 64.338 49.989 7.3167 --
## marg.post[71] 33.629 48.483 62.469 48.5 7.3381 --
## marg.post[72] 27.858 44.078 59.87 44.113 8.1733 --
## marg.post[73] 36.072 50.931 66.003 50.949 7.6572 --
## marg.post[74] 34.814 49.002 63.666 49.053 7.3792 --
## marg.post[75] 36.155 50.694 65.124 50.649 7.3824 --
## marg.post[76] 31.959 46.747 60.755 46.702 7.3533 --
## marg.post[77] 29.599 44.56 59.517 44.548 7.6461 --
## marg.post[78] 35.095 49.19 63.866 49.188 7.3675 --
## marg.post[79] 31.597 45.968 60.397 45.971 7.3346 --
## marg.post[80] 32.716 47.256 61.638 47.269 7.3723 --
## marg.post[81] 34.502 48.661 63.311 48.695 7.3828 --
## marg.post[82] 33.399 48.234 62.228 48.215 7.3589 --
## marg.post[83] 35.373 50.048 65.06 50.063 7.5705 --
## marg.post[84] 34.119 49.012 63.757 48.994 7.561 --
## marg.post[85] 31.864 46.607 60.749 46.623 7.3613 --
## marg.post[86] 29.296 44.014 58.295 43.965 7.4037 --
## marg.post[87] 32.668 47.024 61.526 47.03 7.3726 --
## marg.post[88] 18.614 35.945 52.713 35.914 8.6924 --
## marg.post[89] 33.026 47.247 61.816 47.255 7.3574 --
## marg.post[90] 32.722 47.057 61.562 47.058 7.3777 --
## marg.post[91] 32.133 46.569 61.065 46.557 7.4045 --
## marg.post[92] 32.474 46.659 61.305 46.677 7.3678 --
## marg.post[93] 33.351 48.063 62.185 48.058 7.3535 --
## marg.post[94] 30.218 44.924 59.2 44.926 7.3726 --
## marg.post[95] 27.395 41.701 56.211 41.727 7.3594 --
## marg.post[96] 33.925 48.449 62.522 48.463 7.3292 --
## marg.post[97] 23.629 39.468 55.32 39.465 8.0898 --
## marg.post[98] 33.516 47.867 62.211 47.879 7.3455 --
## marg.post[99] 28.746 43.424 57.618 43.433 7.3691 --
## marg.post[100] 32.903 47.306 61.78 47.333 7.3696 --
## marg.post[101] 32.804 47.43 61.642 47.485 7.3725 --
## marg.post[102] 35.464 50.062 64.337 50.09 7.3743 --
## marg.post[103] 35.644 49.891 64.563 49.862 7.3887 --
## marg.post[104] 32.218 46.739 60.932 46.704 7.34 --
## marg.post[105] 24.52 43.061 61.905 43.098 9.5045 --
## marg.post[106] 37.898 52.704 67.212 52.675 7.4666 --
## marg.post[107] 35.551 49.938 64.5 49.939 7.4105 --
## marg.post[108] 41.765 56.209 70.882 56.178 7.4453 --
## marg.post[109] 42.996 57.724 72.272 57.695 7.5041 --
## marg.post[110] 36.739 51.319 65.825 51.304 7.4208 --
## marg.post[111] 36.876 51.258 65.791 51.272 7.3868 --
## marg.post[112] 39.859 54.384 69.036 54.402 7.4392 --
## marg.post[113] 38.566 52.955 67.416 52.919 7.3362 --
## marg.post[114] 39.149 53.69 68.291 53.705 7.4392 --
## marg.post[115] 41.286 56.346 70.643 56.371 7.473 --
## marg.post[116] 35.489 50.008 64.394 50.008 7.3979 --
## marg.post[117] 36.576 51.002 65.528 50.99 7.4174 --
## marg.post[118] 40.067 54.689 68.936 54.714 7.4127 --
## marg.post[119] 40.673 55.301 69.865 55.319 7.4554 --
## marg.post[120] 35.802 50.296 64.841 50.308 7.4115 --
## marg.post[121] 40.015 54.81 69.212 54.785 7.4756 --
## marg.post[122] 34.388 48.791 63.455 48.795 7.4081 --
## marg.post[123] 37.395 51.777 66.462 51.808 7.4309 --
## marg.post[124] 25.037 42.483 59.975 42.469 8.8935 --
## marg.post[125] 38.476 52.985 67.648 52.991 7.4268 --
## marg.post[126] 33.222 47.845 62.192 47.855 7.3903 --
## marg.post[127] 34.331 48.687 63.12 48.711 7.362 --
## marg.post[128] 36.266 50.607 65.21 50.59 7.3677 --
## marg.post[129] 34.413 49.082 63.505 49.057 7.3923 --
## marg.post[130] 34.089 48.55 63.089 48.565 7.3633 --
## marg.post[131] 35.661 50.197 64.735 50.139 7.413 --
## marg.post[132] 37.785 52.096 66.55 52.101 7.3568 --
## marg.post[133] 35.213 49.521 64.079 49.551 7.3486 --
## marg.post[134] 35.741 50.459 64.874 50.458 7.3704 --
## marg.post[135] 34.597 49.286 63.58 49.258 7.3882 --
## marg.post[136] 37.034 51.212 65.95 51.201 7.3939 --
## marg.post[137] 35.951 50.568 64.93 50.552 7.4084 --
## marg.post[138] 36.983 51.28 65.871 51.282 7.3825 --
## marg.post[139] 31.357 46.185 61.28 46.214 7.6352 --
## marg.post[140] 38.935 53.489 68.068 53.486 7.4309 --
## marg.post[141] 31.521 46.166 60.517 46.158 7.3695 --
## marg.post[142] 34.761 49.46 63.864 49.469 7.4129 --
## marg.post[143] 40.923 55.725 71.066 55.69 7.6736 --
## marg.post[144] 28.706 42.64 57.483 42.653 7.3595 --
## marg.post[145] 27.36 41.786 56.325 41.821 7.4035 --
## marg.post[146] 31.414 45.914 60.719 45.897 7.4865 --
## marg.post[147] 34.257 48.774 63.572 48.752 7.4963 --
## marg.post[148] 17.824 33.176 49.419 33.163 8.0432 --
## marg.post[149] 30.756 44.957 59.457 44.964 7.3191 --
## marg.post[150] 31.163 45.938 60.567 45.917 7.4966 --
## marg.post[151] 24.791 40.339 54.879 40.347 7.6553 --
## marg.post[152] 25.334 40.712 55.498 40.686 7.6803 --
## marg.post[153] 28.792 43.519 57.92 43.548 7.4354 --
## marg.post[154] 27.485 41.485 56.385 41.494 7.3686 --
## marg.post[155] 29.799 44.324 59.325 44.321 7.5198 --
## marg.post[156] 33.181 48.884 63.73 48.855 7.8029 --
## marg.post[157] 31.145 46.078 60.528 46.087 7.51 --
## marg.post[158] 24.991 40.09 54.288 40.06 7.4482 --
## marg.post[159] 27.616 42.476 56.854 42.491 7.4223 --
## marg.post[160] 25.138 39.683 54.203 39.663 7.4206 --
## marg.post[161] 31.951 46.088 60.813 46.083 7.3536 --
## marg.post[162] 25.871 40.267 54.912 40.267 7.3987 --
## marg.post[163] 22.267 37.437 52.327 37.435 7.6577 --
## marg.post[164] 54.631 69.746 85.417 69.768 7.8361 --
## marg.post[165] 46.658 61.116 75.933 61.05 7.4724 --
## marg.post[166] 32.558 47.793 61.775 47.795 7.4092 --
## marg.post[167] 53.591 68.907 84.062 68.879 7.7957 --
## marg.post[168] 39.015 53.551 67.918 53.554 7.3598 --
## marg.post[169] 52.544 68.096 83.379 68.094 7.8601 --
## marg.post[170] 32.135 46.345 61.023 46.35 7.3714 --
## marg.post[171] 37.318 51.542 66.254 51.548 7.3823 --
## marg.post[172] 31.758 46.57 60.806 46.53 7.3955 --
## marg.post[173] 35.727 50.139 64.906 50.124 7.4316 --
## marg.post[174] 33.996 48.623 63.165 48.619 7.4324 --
## marg.post[175] 30.306 44.422 59.135 44.449 7.3816 --
## marg.post[176] 23.135 37.775 53.352 37.782 7.6832 --
## marg.post[177] 36.332 50.474 65.606 50.497 7.4503 --
## marg.post[178] 49.537 64.34 79.152 64.324 7.5265 --
## marg.post[179] 36.884 51.456 65.784 51.46 7.3265 --
## marg.post[180] 40.518 55.348 69.347 55.295 7.3345 --
## marg.post[181] 32.013 46.508 61.066 46.479 7.4272 --
## marg.post[182] 45.674 60.415 74.932 60.435 7.4914 --
## marg.post[183] 32.382 46.596 61.346 46.544 7.4093 --
## marg.post[184] 42.064 56.513 70.985 56.485 7.3823 --
## marg.post[185] 33.434 47.673 62.324 47.653 7.3636 --
## marg.post[186] 31.247 45.883 60.373 45.923 7.4094 --
## marg.post[187] 35.016 49.397 64.054 49.37 7.4053 --
## marg.post[188] 29.689 44.431 58.601 44.478 7.4112 --
## marg.post[189] 38.224 52.856 67.135 52.87 7.3886 --
## marg.post[190] 36.846 51.574 66.074 51.587 7.451 --
## marg.post[191] 27.015 41.398 56.104 41.389 7.4409 --
## marg.post[192] 30.887 45.302 60.204 45.322 7.4402 --
## marg.post[193] 30.019 44.261 59.029 44.268 7.4204 --
## marg.post[194] 49.041 66.086 83.205 66.117 8.7174 --
## marg.post[195] 32.977 47.565 61.762 47.553 7.3432 --
## marg.post[196] 32.686 46.953 61.435 46.923 7.3327 --
## marg.post[197] 27.702 42.372 56.989 42.399 7.4574 --
## marg.post[198] 44.257 59.545 74.41 59.541 7.7017 --
## marg.post[199] 41.31 55.569 70.427 55.558 7.455 --
## marg.post[200] 34.007 48.793 63.14 48.781 7.4526 --
## marg.post[201] 29.529 43.998 58.726 43.976 7.4512 --
## marg.post[202] 38.661 53.31 67.564 53.315 7.4022 --
## marg.post[203] 28.524 43.358 57.619 43.363 7.417 --
## marg.post[204] 41.888 57.094 72.005 57.076 7.7161 --
## marg.post[205] 29.341 43.863 58.4 43.827 7.4358 --
## marg.post[206] 26.866 41.205 55.855 41.2 7.397 --
## marg.post[207] 29.088 43.301 58.075 43.314 7.4089 --
## marg.post[208] 24.351 38.386 53.14 38.411 7.397 --
## marg.post[209] 21.816 36.558 51.324 36.568 7.5401 --
## marg.post[210] 27.477 41.669 56.296 41.677 7.3619 --
## marg.post[211] 25.819 40.06 54.611 40.02 7.3534 --
## marg.post[212] 17.913 32.424 47.461 32.409 7.5415 --
## marg.post[213] 28.515 43.137 57.451 43.144 7.3686 --
## marg.post[214] 23.017 37.732 52.736 37.792 7.5721 --
## marg.post[215] 25.033 39.641 54.004 39.661 7.3859 --
## marg.post[216] 28.236 42.64 57.425 42.651 7.4084 --
## marg.post[217] 27.168 41.919 56.044 41.911 7.3604 --
## marg.post[218] 45.732 61.707 77.555 61.749 8.11 --
## marg.post[219] 25.614 40.096 54.483 40.106 7.3519 --
## marg.post[220] 23.935 38.787 52.793 38.791 7.35 --
## marg.post[221] 28.276 42.609 57.136 42.616 7.342 --
## marg.post[222] 30.933 45.441 60.044 45.473 7.4233 --
## marg.post[223] 30.283 44.534 59.014 44.538 7.3426 --
## marg.post[224] 24.784 39.379 53.711 39.362 7.3997 --
## marg.post[225] 46.947 63.005 78.631 62.991 8.0902 --
## marg.post[226] 41.158 56.35 71.432 56.336 7.7076 --
## marg.post[227] 42.701 57.592 72.945 57.609 7.7211 --
## marg.post[228] 27.975 42.434 56.643 42.442 7.3435 --
## marg.post[229] 32.783 47.255 61.561 47.256 7.3503 --
## marg.post[230] 39.291 54.198 68.731 54.184 7.521 --
## marg.post[231] 30.364 45.377 59.995 45.367 7.5619 --
## marg.post[232] 33.16 47.81 61.958 47.795 7.3497 --
## marg.post[233] 37.102 51.446 65.894 51.463 7.3629 --
## marg.post[234] 37.9 52.53 67.261 52.503 7.5037 --
## marg.post[235] 38.146 52.468 67.591 52.483 7.5437 --
## marg.post[236] 32.682 47.541 62.294 47.551 7.5625 --
## marg.post[237] 31.474 46.231 61.147 46.232 7.5744 --
## marg.post[238] 37.852 52.655 67.241 52.668 7.5198 --
## marg.post[239] 36.381 51.015 65.295 51.017 7.3659 --
## marg.post[240] 34.628 48.914 63.355 48.883 7.3076 --
## marg.post[241] 34.082 48.304 62.821 48.318 7.3559 --
## marg.post[242] 37.253 52.459 66.895 52.47 7.5615 --
## marg.post[243] 32.309 46.826 61.977 46.813 7.5813 --
## marg.post[244] 41.271 55.908 70.871 55.935 7.5301 --
## marg.post[245] 32.472 46.718 61.348 46.727 7.3722 --
## marg.post[246] 36.38 50.796 65.058 50.819 7.3312 --
## marg.post[247] 36.87 51.516 66.296 51.496 7.5108 --
## marg.post[248] 34.376 48.774 63.187 48.782 7.3591 --
## marg.post[249] 35.935 50.835 64.699 50.856 7.3543 --
## marg.post[250] 36.76 51.27 66.34 51.21 7.5535 --
## marg.post[251] 32.97 47.8 62.595 47.785 7.565 --
## marg.post[252] 34.984 49.52 63.957 49.526 7.3696 --
## marg.post[253] 29.566 44.012 58.423 44.037 7.3542 --
## marg.post[254] 32.186 46.316 60.816 46.332 7.3277 --
## marg.post[255] 32.963 47.381 61.751 47.385 7.355 --
## marg.post[256] 32.779 47.217 61.803 47.231 7.3947 --
## marg.post[257] 28.905 43.243 57.885 43.207 7.3966 --
## marg.post[258] 33.806 48.549 63.092 48.543 7.4973 --
## marg.post[259] 31.512 45.873 60.297 45.873 7.3695 --
## marg.post[260] 28.1 43.529 59.404 43.506 8.0089 --
## marg.post[261] 31.674 46.047 60.606 46.049 7.414 --
## marg.post[262] 32.917 48.451 63.093 48.436 7.674 --
## marg.post[263] 32.925 47.683 62.975 47.734 7.6615 --
## marg.post[264] 35.04 50.027 64.993 49.995 7.6386 --
## marg.post[265] 31.851 46.159 60.591 46.204 7.3461 --
## marg.post[266] 27.748 42.774 57.238 42.829 7.5227 --
## marg.post[267] 32.477 47.05 61.365 47.062 7.3535 --
## marg.post[268] 29.331 45.476 60.712 45.463 7.9837 --
## marg.post[269] 34.653 49.274 63.454 49.277 7.3913 --
## marg.post[270] 29.178 43.686 58.053 43.671 7.3759 --
## marg.post[271] 34.721 48.942 63.571 48.921 7.3619 --
## marg.post[272] 32.814 47.296 62.221 47.327 7.5144 --
## marg.post[273] 49.962 65.583 80.656 65.563 7.8391 --
## marg.post[274] 50.096 65.275 80.704 65.247 7.8161 --
## marg.post[275] 49.266 64.653 79.733 64.686 7.8061 --
## marg.post[276] 35.486 50.035 64.878 50.036 7.511 --
## marg.post[277] 41.472 55.983 70.466 55.979 7.4207 --
## marg.post[278] 40.454 55.156 69.379 55.143 7.3892 --
## marg.post[279] 45.922 60.763 75.397 60.76 7.5262 --
## marg.post[280] 36.676 51.309 66.234 51.276 7.5468 --
## marg.post[281] 36.425 51.248 65.977 51.232 7.5241 --
## marg.post[282] 50.578 65.703 81.146 65.694 7.824 --
## marg.post[283] 35.983 50.698 65.612 50.695 7.5341 --
## marg.post[284] 43.538 58.152 72.62 58.124 7.4373 --
## marg.post[285] 37.465 52.348 66.952 52.336 7.5252 --
## marg.post[286] 36.817 51.664 66.433 51.64 7.5267 --
## marg.post[287] 48.036 62.736 77.744 62.758 7.5882 --
## marg.post[288] 52.077 68.474 84.495 68.5 8.2662 --
## marg.post[289] 37.327 51.635 66.211 51.636 7.3985 --
## marg.post[290] 42.412 56.917 71.092 56.92 7.3517 --
## marg.post[291] 51.79 67.094 82.374 67.032 7.8153 --
## marg.post[292] 38.796 53.19 67.901 53.205 7.4244 --
## marg.post[293] 40.189 55.208 69.478 55.21 7.4455 --
## marg.post[294] 35.352 49.552 64.348 49.509 7.3951 --
## marg.post[295] 44.855 59.283 74.055 59.272 7.4879 --
## marg.post[296] 36.644 51.244 65.531 51.227 7.3874 --
## marg.post[297] 36.939 51.072 65.937 51.098 7.4184 --
## marg.post[298] 37.847 52.335 66.822 52.327 7.3842 --
## marg.post[299] 37.457 51.955 66.417 51.922 7.4007 --
## marg.post[300] 42.5 57.284 72.033 57.251 7.5348 --
## marg.post[301] 36.428 50.852 65.274 50.874 7.3648 --
## marg.post[302] 37.188 51.698 65.975 51.701 7.3751 --
## marg.post[303] 35.265 49.842 64.352 49.803 7.4447 --
## marg.post[304] 43.206 57.928 72.701 57.907 7.5045 --
## marg.post[305] 42.779 57.012 71.482 57.027 7.3429 --
## marg.post[306] 36.365 50.949 65.266 50.941 7.3806 --
## marg.post[307] 36.987 51.411 65.908 51.425 7.3792 --
## marg.post[308] 41.335 55.596 70.104 55.612 7.3568 --
## marg.post[309] 34.279 48.687 63.294 48.732 7.4238 --
## marg.post[310] 49.413 64.281 80.034 64.325 7.8241 --
## marg.post[311] 38.469 52.954 67.475 52.937 7.3901 --
## marg.post[312] 30.489 45.302 59.735 45.27 7.4743 --
## marg.post[313] 41.541 56.178 70.826 56.176 7.4796 --
## marg.post[314] 40.675 55.627 70.882 55.655 7.721 --
## marg.post[315] 35.974 50.359 65.009 50.377 7.4225 --
## marg.post[316] 40.118 54.75 69.269 54.737 7.4324 --
## marg.post[317] 34.132 48.569 63.154 48.618 7.3794 --
## marg.post[318] 35.13 49.397 63.963 49.384 7.3658 --
## marg.post[319] 33.961 48.55 62.719 48.521 7.3739 --
## marg.post[320] 31.634 45.957 60.913 45.973 7.466 --
## marg.post[321] 36.012 50.544 64.948 50.494 7.3805 --
## marg.post[322] 36.906 51.581 66.033 51.579 7.4157 --
## marg.post[323] 43.284 58.311 72.733 58.271 7.5204 --
## marg.post[324] 33.663 47.928 62.682 47.928 7.3855 --
## marg.post[325] 30.468 45.108 59.756 45.101 7.499 --
## marg.post[326] 34.176 48.548 63.147 48.509 7.3888 --
## marg.post[327] 32.167 47.059 61.38 47.048 7.4365 --
## marg.post[328] 36.298 50.442 65.138 50.441 7.3814 --
## marg.post[329] 49.127 66.024 82.299 65.974 8.4073 --
## marg.post[330] 45.364 61.406 76.52 61.433 7.9517 --
## marg.post[331] 27.619 42.063 56.805 42.092 7.448 --
## marg.post[332] 38.703 56.284 73.97 56.294 9.0292 --
## marg.post[333] 28.402 43.102 57.494 43.075 7.4256 --
## marg.post[334] 29.619 43.779 58.756 43.798 7.4183 --
## marg.post[335] 29.048 43.519 58.196 43.542 7.4738 --
## marg.post[336] 27.856 42.588 57.099 42.588 7.4802 --
## marg.post[337] 26.51 41.093 55.564 41.082 7.4354 --
## marg.post[338] 32.037 46.199 61.139 46.23 7.4323 --
## marg.post[339] 27.685 42.372 56.852 42.364 7.4548 --
## marg.post[340] 27.352 42.076 56.553 42.035 7.4694 --
## marg.post[341] 31.811 46.138 60.999 46.138 7.4264 --
## marg.post[342] 30.843 46.034 61.096 46.048 7.7011 --
## marg.post[343] 26.092 40.295 55.235 40.325 7.462 --
## marg.post[344] 22.413 37.763 53.142 37.766 7.8064 --
## marg.post[345] 28.3 43.03 57.632 43.053 7.4514 --
## marg.post[346] 28.67 42.92 57.703 42.892 7.3887 --
## marg.post[347] 24.548 38.826 53.599 38.814 7.4508 --
## marg.post[348] 28.177 42.391 57.17 42.411 7.4208 --
## marg.post[349] 24.986 39.999 54.166 40.015 7.4414 --
## marg.post[350] 50.376 64.878 78.933 64.863 7.2862 --
## marg.post[351] 48.723 63.108 77.409 63.129 7.3191 --
## marg.post[352] 49.1 63.71 77.878 63.705 7.3238 --
## marg.post[353] 49.243 63.392 77.838 63.417 7.2862 --
## marg.post[354] 45.703 59.835 74.169 59.825 7.2713 --
## marg.post[355] 46.604 61.003 75.217 61.008 7.2978 --
## marg.post[356] 48.008 62.286 76.62 62.295 7.2891 --
## marg.post[357] 45.047 59.525 73.842 59.501 7.3371 --
## marg.post[358] 48.541 62.836 77.103 62.843 7.2774 --
## marg.post[359] 42.691 57.149 71.852 57.155 7.442 --
## marg.post[360] 50.379 64.57 78.957 64.595 7.2902 --
## marg.post[361] 46.996 61.356 75.686 61.331 7.3449 --
## marg.post[362] 47.193 61.651 75.734 61.661 7.3079 --
## marg.post[363] 47.256 61.463 75.898 61.479 7.3232 --
## marg.post[364] 51.31 65.826 80.386 65.848 7.401 --
## marg.post[365] 44.856 59.153 73.477 59.136 7.3421 --
## marg.post[366] 46.767 61.163 75.391 61.152 7.2892 --
## marg.post[367] 44.441 58.732 73.185 58.75 7.363 --
## marg.post[368] 44.249 58.348 72.798 58.347 7.2834 --
## marg.post[369] 51.504 65.856 80.051 65.867 7.2847 --
## marg.post[370] 49.766 63.812 78.069 63.869 7.2712 --
## marg.post[371] 49.282 63.673 77.522 63.681 7.2244 --
## marg.post[372] 43.33 58.175 72.06 58.177 7.3325 --
## marg.post[373] 49.504 63.555 77.818 63.562 7.2514 --
## marg.post[374] 46.801 61.418 75.618 61.415 7.3636 --
## marg.post[375] 49.769 64.093 78.231 64.11 7.2768 --
## marg.post[376] 43.877 58.312 72.606 58.311 7.3341 --
## marg.post[377] 49.944 64.822 78.672 64.832 7.3264 --
## marg.post[378] 51.345 65.639 80.055 65.617 7.3264 --
## marg.post[379] 44.294 58.593 73.165 58.577 7.3443 --
## marg.post[380] 51.649 66.186 80.258 66.181 7.3004 --
## marg.post[381] 49.406 63.24 77.894 63.266 7.2666 --
## marg.post[382] 44.31 58.82 73.411 58.871 7.4186 --
## marg.post[383] 46.923 61.28 75.475 61.262 7.2719 --
## marg.post[384] 48.994 63.364 77.783 63.366 7.3556 --
## marg.post[385] 48.817 63.264 77.268 63.274 7.2597 --
## marg.post[386] 47.264 61.313 75.617 61.314 7.255 --
## marg.post[387] 51.031 65.126 79.718 65.115 7.3404 --
## marg.post[388] 46.009 60.307 74.873 60.312 7.3494 --
## marg.post[389] 47.88 61.955 76.078 61.927 7.2589 --
## marg.post[390] 49.962 64.001 78.618 64.011 7.289 --
## marg.post[391] 47.385 61.717 75.828 61.761 7.2597 --
## marg.post[392] 49.708 63.961 78.213 63.982 7.2705 --
## marg.post[393] 49.97 64.695 79.358 64.732 7.5313 --
## marg.post[394] 51.75 65.816 80.296 65.836 7.3028 --
## marg.post[395] 40.87 55.481 69.621 55.503 7.3359 --
## marg.post[396] 43.214 57.57 72.216 57.539 7.3687 --
## marg.post[397] 47.305 61.432 75.763 61.432 7.2813 --
## marg.post[398] 48.37 62.82 76.824 62.795 7.2722 --
## marg.post[399] 48.131 62.092 76.791 62.102 7.3019 --
## marg.post[400] 47.564 61.852 76.133 61.841 7.2834 --
## marg.post[401] 42.564 57.165 71.244 57.164 7.3561 --
## marg.post[402] 48.783 63.134 77.341 63.122 7.2863 --
## marg.post[403] 51.424 65.877 80.148 65.888 7.3262 --
## marg.post[404] 47.627 61.713 75.914 61.694 7.2386 --
## marg.post[405] 44.573 59.196 73.436 59.185 7.3334 --
## marg.post[406] 52.228 66.057 80.915 66.035 7.3201 --
## marg.post[407] 49.832 63.927 78.228 63.942 7.2813 --
## marg.post[408] 53.366 67.734 82.435 67.714 7.3983 --
## marg.post[409] 48.678 62.756 77.05 62.759 7.2481 --
## marg.post[410] 50.244 64.689 78.878 64.666 7.3308 --
## marg.post[411] 52.157 66.671 80.99 66.695 7.3344 --
## marg.post[412] 49.379 63.633 78.122 63.635 7.3295 --
## marg.post[413] 48.059 62.477 76.506 62.443 7.272 --
## marg.post[414] 47.017 61.086 75.483 61.097 7.2743 --
## marg.post[415] 50.47 64.613 79.028 64.583 7.2977 --
## marg.post[416] 49.496 63.709 77.773 63.712 7.2225 --
## marg.post[417] 41.919 57.093 71.254 57.08 7.4976 --
## marg.post[418] 28.684 43.047 57.756 43.03 7.4462 --
## marg.post[419] 33.958 48.437 62.718 48.427 7.3345 --
## marg.post[420] 30.321 44.915 59.672 44.942 7.4897 --
## marg.post[421] 46.715 62.33 77.236 62.329 7.8132 --
## marg.post[422] 33.317 47.636 62.789 47.647 7.5148 --
## marg.post[423] 29.934 44.644 59.204 44.637 7.4745 --
## marg.post[424] 35.159 49.52 63.876 49.554 7.3538 --
## marg.post[425] 37.877 52.441 67.15 52.456 7.4655 --
## marg.post[426] 39.736 54.117 68.747 54.157 7.4176 --
## marg.post[427] 42.433 57.314 71.562 57.282 7.4273 --
## marg.post[428] 29.03 43.434 58.312 43.444 7.4658 --
## marg.post[429] 35.848 49.889 64.498 49.904 7.3198 --
## marg.post[430] 28.551 43.136 57.731 43.151 7.455 --
## marg.post[431] 36.376 50.454 65.195 50.46 7.3391 --
## marg.post[432] 28.217 42.49 57.502 42.494 7.4985 --
## marg.post[433] 29.497 44.433 58.688 44.406 7.4158 --
## marg.post[434] 37.738 52.217 66.912 52.226 7.4471 --
## marg.post[435] 24.974 39.672 54.237 39.682 7.482 --
## marg.post[436] 51.139 67.634 83.944 67.646 8.3659 --
## marg.post[437] 39.248 53.959 68.521 53.936 7.5115 --
## marg.post[438] 33.359 48.088 62.384 48.099 7.4108 --
## marg.post[439] 28.576 42.977 57.288 42.976 7.3693 --
## marg.post[440] 37.303 51.617 66.267 51.665 7.398 --
## marg.post[441] 41.175 57.935 74.282 57.905 8.4807 --
## marg.post[442] 25.395 39.913 55.421 39.91 7.6489 --
## marg.post[443] 30.744 45.18 59.476 45.181 7.3563 --
## marg.post[444] 35.641 50.109 64.699 50.099 7.4081 --
## marg.post[445] 27.6 42.134 56.511 42.138 7.3821 --
## marg.post[446] 26.119 40.526 55.143 40.491 7.42 --
## marg.post[447] 28.681 43.13 57.714 43.11 7.3967 --
## marg.post[448] 34.829 49.442 63.837 49.421 7.4155 --
## marg.post[449] 35.105 49.387 64.217 49.387 7.4283 --
## marg.post[450] 33.435 47.933 62.361 47.927 7.3806 --
## marg.post[451] 27.82 42.442 56.691 42.421 7.3755 --
## marg.post[452] 40.014 55.505 70.58 55.491 7.7911 --
## marg.post[453] 24.476 39.29 54.504 39.316 7.646 --
## marg.post[454] 29.945 44.27 58.761 44.216 7.342 --
## marg.post[455] 28.531 42.798 57.513 42.826 7.3934 --
## marg.post[456] 38.42 53.161 67.492 53.146 7.4304 --
## marg.post[457] 29.423 43.649 58.306 43.669 7.3547 --
## marg.post[458] 30.523 44.925 59.463 44.975 7.3536 --
## marg.post[459] 42.929 57.343 72.084 57.31 7.4769 --
## marg.post[460] 39.334 54.216 68.014 54.227 7.3139 --
## marg.post[461] 46.19 61.449 77.169 61.443 7.9086 --
## marg.post[462] 32.006 46.559 60.751 46.534 7.3523 --
## marg.post[463] 32.206 46.75 61.067 46.772 7.3974 --
## marg.post[464] 31.692 46.279 60.442 46.276 7.3491 --
## marg.post[465] 34.933 49.122 63.74 49.121 7.3686 --
## marg.post[466] 40.219 54.769 68.865 54.759 7.3065 --
## marg.post[467] 30.954 45.517 59.82 45.475 7.3865 --
## marg.post[468] 27.965 42.668 57.477 42.658 7.5325 --
## marg.post[469] 45.919 60.918 76.75 60.951 7.8814 --
## marg.post[470] 35.915 50.764 64.909 50.763 7.382 --
## marg.post[471] 38.01 52.113 66.793 52.111 7.3204 --
## marg.post[472] 42.062 57.081 71.441 57.043 7.5062 --
## marg.post[473] 42.495 57.381 72.113 57.356 7.5365 --
## marg.post[474] 34.576 49.351 63.612 49.359 7.3743 --
## marg.post[475] 32.146 46.262 60.832 46.268 7.3227 --
## marg.post[476] 35.183 49.505 63.876 49.487 7.3273 --
## marg.post[477] 32.172 46.639 60.929 46.66 7.3455 --
## marg.post[478] 34.134 48.604 62.915 48.595 7.3575 --
## marg.post[479] 34.886 49.533 63.804 49.53 7.3829 --
## marg.post[480] 26.607 42.047 56.612 42.03 7.6337 --
## marg.post[481] 35.903 50.401 64.501 50.384 7.3068 --
## marg.post[482] 31.087 45.684 59.872 45.729 7.3447 --
## marg.post[483] 40.693 55.42 69.63 55.423 7.3598 --
## marg.post[484] 34.819 49.488 63.643 49.49 7.3681 --
## marg.post[485] 44.185 58.742 73.545 58.761 7.5158 --
## marg.post[486] 49.582 66.145 82.041 66.127 8.2943 --
## marg.post[487] 41.249 56.071 70.72 56.049 7.5163 --
## marg.post[488] 30.06 44.574 59.168 44.546 7.4218 --
## marg.post[489] 36.511 50.962 65.497 50.97 7.3843 --
## marg.post[490] 22.945 37.305 52.446 37.295 7.5448 --
## marg.post[491] 47.676 62.873 78.527 62.901 7.8193 --
## marg.post[492] 29.289 43.815 58.345 43.824 7.4174 --
## marg.post[493] 33.581 48.017 62.404 48.03 7.3368 --
## marg.post[494] 22.977 37.855 52.495 37.882 7.5296 --
## marg.post[495] 24.023 38.798 53.744 38.806 7.5531 --
## marg.post[496] 35.258 49.6 64.213 49.635 7.3749 --
## marg.post[497] 42.827 57.405 72.171 57.443 7.4913 --
## marg.post[498] 42.015 56.61 71.227 56.619 7.5067 --
## marg.post[499] 31.192 45.368 60.147 45.403 7.3951 --
## marg.post[500] 23.671 38.521 53.456 38.55 7.5947 --
## marg.post[501] 36.272 50.824 65.195 50.835 7.386 --
## marg.post[502] 22.465 37.099 52.234 37.121 7.5756 --
## marg.post[503] 30.116 45.002 59.043 44.986 7.3613 --
## marg.post[504] 28.039 42.625 57.038 42.566 7.4122 --
## marg.post[505] 32.799 46.994 61.744 46.999 7.3846 --
## marg.post[506] 28.138 43.308 58.185 43.354 7.6958 --
## marg.post[507] 27.591 42.67 57.621 42.68 7.6466 --
## marg.post[508] 42.303 56.914 71.431 56.925 7.4322 --
## marg.post[509] 52.234 67.968 83.939 67.928 8.1024 --
## marg.post[510] 33.447 48.094 62.493 48.127 7.4113 --
## marg.post[511] 35.625 50.301 64.872 50.313 7.463 --
## marg.post[512] 50.182 65.558 80.674 65.571 7.7794 --
## marg.post[513] 32.587 46.871 61.695 46.862 7.4791 --
## marg.post[514] 31.437 46.07 60.72 46.06 7.4558 --
## marg.post[515] 54.456 70.441 86.311 70.425 8.1358 --
## marg.post[516] 49.855 65.248 80.105 65.218 7.7468 --
## marg.post[517] 35.649 50.047 64.921 50.085 7.4749 --
## marg.post[518] 34.223 48.953 63.501 48.938 7.4966 --
## marg.post[519] 39.372 53.659 68.329 53.667 7.397 --
## intercept.blup[1] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[2] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[3] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[4] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[5] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[6] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[7] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[8] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[9] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[10] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[11] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[12] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[13] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[14] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[15] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[16] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[17] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[18] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[19] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[20] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[21] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[22] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[23] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[24] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[25] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[26] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[27] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[28] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[29] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[30] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[31] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[32] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[33] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[34] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[35] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[36] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[37] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[38] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[39] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[40] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[41] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[42] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[43] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[44] -4.486 1.7724 7.905 1.7995 3.1475 --
## intercept.blup[45] 3.4812 10.94 18.741 11.026 3.8761 --
## intercept.blup[46] 3.4812 10.94 18.741 11.026 3.8761 --
## intercept.blup[47] 3.4812 10.94 18.741 11.026 3.8761 --
## intercept.blup[48] 3.4812 10.94 18.741 11.026 3.8761 --
## intercept.blup[49] 3.4812 10.94 18.741 11.026 3.8761 --
## intercept.blup[50] 3.4812 10.94 18.741 11.026 3.8761 --
## intercept.blup[51] 3.4812 10.94 18.741 11.026 3.8761 --
## intercept.blup[52] 3.4812 10.94 18.741 11.026 3.8761 --
## intercept.blup[53] -12.597 -3.5393 5.8009 -3.573 4.6816 --
## intercept.blup[54] -12.597 -3.5393 5.8009 -3.573 4.6816 --
## intercept.blup[55] -12.597 -3.5393 5.8009 -3.573 4.6816 --
## intercept.blup[56] -12.597 -3.5393 5.8009 -3.573 4.6816 --
## intercept.blup[57] -12.597 -3.5393 5.8009 -3.573 4.6816 --
## intercept.blup[58] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[59] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[60] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[61] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[62] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[63] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[64] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[65] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[66] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[67] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[68] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[69] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[70] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[71] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[72] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[73] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[74] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[75] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[76] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[77] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[78] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[79] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[80] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[81] -0.43526 5.6749 11.842 5.685 3.1252 --
## intercept.blup[82] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[83] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[84] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[85] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[86] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[87] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[88] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[89] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[90] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[91] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[92] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[93] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[94] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[95] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[96] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[97] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[98] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[99] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[100] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[101] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[102] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[103] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[104] -1.3459 4.4314 10.169 4.4357 2.941 --
## intercept.blup[105] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[106] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[107] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[108] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[109] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[110] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[111] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[112] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[113] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[114] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[115] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[116] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[117] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[118] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[119] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[120] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[121] 3.9557 10.553 17.341 10.612 3.4239 --
## intercept.blup[122] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[123] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[124] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[125] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[126] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[127] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[128] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[129] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[130] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[131] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[132] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[133] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[134] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[135] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[136] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[137] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[138] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[139] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[140] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[141] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[142] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[143] 1.6119 7.6419 13.984 7.6796 3.1667 --
## intercept.blup[144] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[145] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[146] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[147] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[148] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[149] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[150] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[151] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[152] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[153] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[154] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[155] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[156] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[157] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[158] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[159] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[160] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[161] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[162] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[163] -1.7484 4.8561 11.721 4.869 3.4139 --
## intercept.blup[164] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[165] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[166] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[167] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[168] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[169] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[170] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[171] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[172] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[173] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[174] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[175] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[176] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[177] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[178] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[179] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[180] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[181] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[182] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[183] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[184] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[185] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[186] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[187] -12.777 -6.6205 -0.32715 -6.6322 3.1801 --
## intercept.blup[188] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[189] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[190] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[191] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[192] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[193] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[194] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[195] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[196] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[197] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[198] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[199] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[200] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[201] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[202] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[203] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[204] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[205] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[206] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[207] -11.836 -5.5887 0.58983 -5.6012 3.1607 --
## intercept.blup[208] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[209] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[210] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[211] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[212] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[213] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[214] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[215] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[216] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[217] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[218] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[219] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[220] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[221] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[222] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[223] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[224] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[225] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[226] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[227] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[228] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[229] -14.351 -8.7596 -3.107 -8.7926 2.8715 --
## intercept.blup[230] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[231] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[232] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[233] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[234] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[235] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[236] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[237] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[238] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[239] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[240] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[241] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[242] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[243] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[244] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[245] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[246] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[247] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[248] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[249] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[250] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[251] 0.73531 6.2422 11.971 6.2678 2.8681 --
## intercept.blup[252] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[253] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[254] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[255] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[256] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[257] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[258] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[259] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[260] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[261] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[262] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[263] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[264] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[265] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[266] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[267] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[268] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[269] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[270] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[271] -1.6137 4.6224 10.804 4.6561 3.1547 --
## intercept.blup[272] -10.297 -3.1077 4.2952 -3.1025 3.7093 --
## intercept.blup[273] -10.297 -3.1077 4.2952 -3.1025 3.7093 --
## intercept.blup[274] -10.297 -3.1077 4.2952 -3.1025 3.7093 --
## intercept.blup[275] -10.297 -3.1077 4.2952 -3.1025 3.7093 --
## intercept.blup[276] -10.297 -3.1077 4.2952 -3.1025 3.7093 --
## intercept.blup[277] -10.297 -3.1077 4.2952 -3.1025 3.7093 --
## intercept.blup[278] -10.297 -3.1077 4.2952 -3.1025 3.7093 --
## intercept.blup[279] -10.297 -3.1077 4.2952 -3.1025 3.7093 --
## intercept.blup[280] -10.297 -3.1077 4.2952 -3.1025 3.7093 --
## intercept.blup[281] -10.297 -3.1077 4.2952 -3.1025 3.7093 --
## intercept.blup[282] -10.297 -3.1077 4.2952 -3.1025 3.7093 --
## intercept.blup[283] -10.297 -3.1077 4.2952 -3.1025 3.7093 --
## intercept.blup[284] -10.297 -3.1077 4.2952 -3.1025 3.7093 --
## intercept.blup[285] -10.297 -3.1077 4.2952 -3.1025 3.7093 --
## intercept.blup[286] -10.297 -3.1077 4.2952 -3.1025 3.7093 --
## intercept.blup[287] -10.297 -3.1077 4.2952 -3.1025 3.7093 --
## intercept.blup[288] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[289] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[290] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[291] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[292] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[293] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[294] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[295] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[296] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[297] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[298] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[299] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[300] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[301] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[302] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[303] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[304] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[305] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[306] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[307] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[308] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[309] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[310] -6.437 0.54401 7.5255 0.58004 3.5477 --
## intercept.blup[311] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[312] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[313] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[314] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[315] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[316] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[317] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[318] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[319] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[320] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[321] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[322] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[323] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[324] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[325] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[326] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[327] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[328] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[329] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[330] -10.341 -3.1185 3.8826 -3.1166 3.6342 --
## intercept.blup[331] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[332] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[333] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[334] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[335] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[336] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[337] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[338] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[339] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[340] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[341] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[342] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[343] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[344] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[345] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[346] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[347] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[348] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[349] -18.293 -10.747 -3.4225 -10.781 3.7793 --
## intercept.blup[350] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[351] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[352] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[353] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[354] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[355] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[356] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[357] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[358] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[359] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[360] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[361] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[362] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[363] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[364] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[365] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[366] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[367] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[368] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[369] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[370] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[371] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[372] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[373] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[374] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[375] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[376] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[377] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[378] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[379] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[380] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[381] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[382] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[383] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[384] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[385] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[386] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[387] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[388] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[389] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[390] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[391] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[392] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[393] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[394] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[395] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[396] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[397] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[398] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[399] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[400] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[401] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[402] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[403] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[404] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[405] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[406] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[407] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[408] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[409] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[410] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[411] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[412] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[413] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[414] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[415] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[416] 1.3827 7.5692 14.102 7.6208 3.2239 --
## intercept.blup[417] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[418] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[419] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[420] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[421] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[422] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[423] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[424] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[425] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[426] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[427] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[428] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[429] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[430] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[431] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[432] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[433] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[434] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[435] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[436] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[437] -13.561 -6.6625 -0.073929 -6.7137 3.4507 --
## intercept.blup[438] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[439] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[440] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[441] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[442] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[443] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[444] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[445] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[446] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[447] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[448] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[449] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[450] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[451] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[452] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[453] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[454] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[455] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[456] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[457] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[458] -11.722 -5.5473 0.61211 -5.5901 3.1566 --
## intercept.blup[459] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[460] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[461] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[462] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[463] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[464] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[465] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[466] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[467] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[468] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[469] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[470] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[471] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[472] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[473] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[474] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[475] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[476] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[477] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[478] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[479] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[480] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[481] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[482] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[483] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[484] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[485] -7.2255 -1.5173 4.5374 -1.5229 2.9913 --
## intercept.blup[486] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[487] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[488] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[489] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[490] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[491] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[492] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[493] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[494] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[495] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[496] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[497] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[498] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[499] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[500] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[501] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[502] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[503] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[504] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[505] -12.301 -6.599 -0.80146 -6.621 2.9362 --
## intercept.blup[506] -9.233 -2.8962 3.2911 -2.8987 3.1864 --
## intercept.blup[507] -9.233 -2.8962 3.2911 -2.8987 3.1864 --
## intercept.blup[508] -9.233 -2.8962 3.2911 -2.8987 3.1864 --
## intercept.blup[509] -9.233 -2.8962 3.2911 -2.8987 3.1864 --
## intercept.blup[510] -9.233 -2.8962 3.2911 -2.8987 3.1864 --
## intercept.blup[511] -9.233 -2.8962 3.2911 -2.8987 3.1864 --
## intercept.blup[512] -9.233 -2.8962 3.2911 -2.8987 3.1864 --
## intercept.blup[513] -9.233 -2.8962 3.2911 -2.8987 3.1864 --
## intercept.blup[514] -9.233 -2.8962 3.2911 -2.8987 3.1864 --
## intercept.blup[515] -9.233 -2.8962 3.2911 -2.8987 3.1864 --
## intercept.blup[516] -9.233 -2.8962 3.2911 -2.8987 3.1864 --
## intercept.blup[517] -9.233 -2.8962 3.2911 -2.8987 3.1864 --
## intercept.blup[518] -9.233 -2.8962 3.2911 -2.8987 3.1864 --
## intercept.blup[519] -9.233 -2.8962 3.2911 -2.8987 3.1864 --
## slope.blup[1] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[2] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[3] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[4] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[5] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[6] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[7] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[8] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[9] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[10] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[11] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[12] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[13] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[14] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[15] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[16] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[17] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[18] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[19] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[20] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[21] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[22] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[23] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[24] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[25] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[26] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[27] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[28] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[29] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[30] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[31] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[32] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[33] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[34] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[35] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[36] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[37] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[38] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[39] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[40] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[41] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[42] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[43] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[44] -3.2387 -0.15692 2.8888 -0.17176 1.555 --
## slope.blup[45] -11.313 -6.5018 -1.9159 -6.5633 2.3938 --
## slope.blup[46] -11.313 -6.5018 -1.9159 -6.5633 2.3938 --
## slope.blup[47] -11.313 -6.5018 -1.9159 -6.5633 2.3938 --
## slope.blup[48] -11.313 -6.5018 -1.9159 -6.5633 2.3938 --
## slope.blup[49] -11.313 -6.5018 -1.9159 -6.5633 2.3938 --
## slope.blup[50] -11.313 -6.5018 -1.9159 -6.5633 2.3938 --
## slope.blup[51] -11.313 -6.5018 -1.9159 -6.5633 2.3938 --
## slope.blup[52] -11.313 -6.5018 -1.9159 -6.5633 2.3938 --
## slope.blup[53] -1.2356 3.0235 7.3345 3.0362 2.1832 --
## slope.blup[54] -1.2356 3.0235 7.3345 3.0362 2.1832 --
## slope.blup[55] -1.2356 3.0235 7.3345 3.0362 2.1832 --
## slope.blup[56] -1.2356 3.0235 7.3345 3.0362 2.1832 --
## slope.blup[57] -1.2356 3.0235 7.3345 3.0362 2.1832 --
## slope.blup[58] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[59] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[60] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[61] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[62] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[63] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[64] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[65] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[66] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[67] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[68] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[69] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[70] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[71] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[72] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[73] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[74] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[75] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[76] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[77] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[78] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[79] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[80] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[81] -6.9956 -3.5826 -0.2721 -3.5921 1.7114 --
## slope.blup[82] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[83] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[84] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[85] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[86] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[87] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[88] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[89] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[90] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[91] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[92] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[93] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[94] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[95] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[96] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[97] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[98] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[99] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[100] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[101] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[102] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[103] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[104] -7.3679 -4.1689 -0.99658 -4.1856 1.6185 --
## slope.blup[105] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[106] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[107] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[108] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[109] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[110] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[111] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[112] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[113] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[114] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[115] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[116] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[117] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[118] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[119] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[120] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[121] -7.2276 -3.8399 -0.56809 -3.8524 1.6975 --
## slope.blup[122] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[123] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[124] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[125] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[126] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[127] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[128] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[129] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[130] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[131] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[132] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[133] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[134] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[135] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
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## slope.blup[139] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[140] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[141] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[142] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[143] -7.4504 -4.0412 -0.68672 -4.0507 1.7234 --
## slope.blup[144] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[145] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[146] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[147] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[148] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[149] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[150] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[151] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[152] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[153] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[154] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[155] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[156] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[157] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[158] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[159] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[160] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[161] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[162] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[163] -7.4059 -4.3819 -1.4351 -4.3886 1.5193 --
## slope.blup[164] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[165] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[166] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[167] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[168] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[169] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[170] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[171] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[172] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[173] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[174] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[175] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[176] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[177] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[178] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[179] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[180] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[181] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[182] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[183] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[184] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[185] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[186] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[187] 2.0585 5.1411 8.3129 5.1598 1.5964 --
## slope.blup[188] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[189] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[190] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[191] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[192] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[193] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[194] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[195] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[196] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[197] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[198] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[199] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[200] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[201] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[202] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[203] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[204] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[205] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[206] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[207] -0.25111 2.6964 5.6474 2.7067 1.5038 --
## slope.blup[208] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[209] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[210] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[211] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[212] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[213] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[214] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[215] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[216] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[217] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[218] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[219] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[220] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[221] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[222] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[223] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[224] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[225] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[226] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[227] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[228] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[229] 0.66615 3.4292 6.2749 3.4326 1.4305 --
## slope.blup[230] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[231] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[232] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[233] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[234] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[235] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[236] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[237] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[238] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[239] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[240] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
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## slope.blup[242] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[243] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[244] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[245] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[246] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[247] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[248] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[249] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[250] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
## slope.blup[251] -8.1487 -4.3669 -0.56325 -4.3965 1.9293 --
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## slope.blup[253] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[254] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[255] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[256] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[257] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[258] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[259] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[260] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[261] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[262] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[263] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[264] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[265] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[266] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[267] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[268] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[269] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[270] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[271] -7.0925 -3.2652 0.56926 -3.2738 1.95 --
## slope.blup[272] -2.2037 1.1335 4.446 1.1282 1.6837 --
## slope.blup[273] -2.2037 1.1335 4.446 1.1282 1.6837 --
## slope.blup[274] -2.2037 1.1335 4.446 1.1282 1.6837 --
## slope.blup[275] -2.2037 1.1335 4.446 1.1282 1.6837 --
## slope.blup[276] -2.2037 1.1335 4.446 1.1282 1.6837 --
## slope.blup[277] -2.2037 1.1335 4.446 1.1282 1.6837 --
## slope.blup[278] -2.2037 1.1335 4.446 1.1282 1.6837 --
## slope.blup[279] -2.2037 1.1335 4.446 1.1282 1.6837 --
## slope.blup[280] -2.2037 1.1335 4.446 1.1282 1.6837 --
## slope.blup[281] -2.2037 1.1335 4.446 1.1282 1.6837 --
## slope.blup[282] -2.2037 1.1335 4.446 1.1282 1.6837 --
## slope.blup[283] -2.2037 1.1335 4.446 1.1282 1.6837 --
## slope.blup[284] -2.2037 1.1335 4.446 1.1282 1.6837 --
## slope.blup[285] -2.2037 1.1335 4.446 1.1282 1.6837 --
## slope.blup[286] -2.2037 1.1335 4.446 1.1282 1.6837 --
## slope.blup[287] -2.2037 1.1335 4.446 1.1282 1.6837 --
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## slope.blup[290] -1.766 1.8068 5.2949 1.8047 1.7896 --
## slope.blup[291] -1.766 1.8068 5.2949 1.8047 1.7896 --
## slope.blup[292] -1.766 1.8068 5.2949 1.8047 1.7896 --
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## slope.blup[309] -1.766 1.8068 5.2949 1.8047 1.7896 --
## slope.blup[310] -1.766 1.8068 5.2949 1.8047 1.7896 --
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## slope.blup[312] -2.9924 0.23023 3.529 0.22462 1.6536 --
## slope.blup[313] -2.9924 0.23023 3.529 0.22462 1.6536 --
## slope.blup[314] -2.9924 0.23023 3.529 0.22462 1.6536 --
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## slope.blup[380] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[381] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[382] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[383] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[384] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[385] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[386] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[387] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[388] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[389] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[390] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[391] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[392] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[393] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[394] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[395] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[396] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[397] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[398] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[399] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[400] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[401] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[402] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[403] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[404] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[405] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[406] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[407] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[408] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[409] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[410] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[411] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[412] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[413] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[414] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[415] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[416] -4.1069 -1.014 1.9308 -1.0338 1.526 --
## slope.blup[417] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[418] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[419] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[420] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[421] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[422] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[423] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[424] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[425] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[426] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[427] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[428] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[429] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[430] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[431] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[432] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[433] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[434] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[435] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[436] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[437] 0.23435 3.3563 6.6836 3.3741 1.6356 --
## slope.blup[438] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[439] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[440] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[441] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[442] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[443] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[444] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[445] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[446] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[447] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[448] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[449] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[450] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[451] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[452] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[453] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[454] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[455] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[456] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[457] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[458] -0.34341 3.2689 6.8422 3.2796 1.8346 --
## slope.blup[459] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[460] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[461] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[462] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[463] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[464] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[465] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[466] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[467] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[468] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[469] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[470] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[471] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[472] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[473] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[474] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[475] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[476] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[477] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[478] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[479] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[480] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[481] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[482] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[483] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[484] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[485] -0.66189 2.3669 5.5149 2.3735 1.5789 --
## slope.blup[486] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[487] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[488] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[489] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[490] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[491] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[492] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[493] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[494] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[495] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[496] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[497] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[498] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[499] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[500] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[501] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[502] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[503] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[504] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[505] 1.1493 4.0808 7.0013 4.0925 1.4914 --
## slope.blup[506] 0.53532 3.3727 6.2745 3.3889 1.4665 --
## slope.blup[507] 0.53532 3.3727 6.2745 3.3889 1.4665 --
## slope.blup[508] 0.53532 3.3727 6.2745 3.3889 1.4665 --
## slope.blup[509] 0.53532 3.3727 6.2745 3.3889 1.4665 --
## slope.blup[510] 0.53532 3.3727 6.2745 3.3889 1.4665 --
## slope.blup[511] 0.53532 3.3727 6.2745 3.3889 1.4665 --
## slope.blup[512] 0.53532 3.3727 6.2745 3.3889 1.4665 --
## slope.blup[513] 0.53532 3.3727 6.2745 3.3889 1.4665 --
## slope.blup[514] 0.53532 3.3727 6.2745 3.3889 1.4665 --
## slope.blup[515] 0.53532 3.3727 6.2745 3.3889 1.4665 --
## slope.blup[516] 0.53532 3.3727 6.2745 3.3889 1.4665 --
## slope.blup[517] 0.53532 3.3727 6.2745 3.3889 1.4665 --
## slope.blup[518] 0.53532 3.3727 6.2745 3.3889 1.4665 --
## slope.blup[519] 0.53532 3.3727 6.2745 3.3889 1.4665 --
##
## MCerr MC%ofSD SSeff AC.100 psrf
## g00 0.061071 2.2 2117 0.47326 1.0015
## g10 0.032963 2.3 1970 0.52111 1.0016
## g11 0.039109 2.1 2181 0.47174 1.0016
## g2 0.0021893 0.4 55591 -0.00031027 1
## g3 0.072205 2.1 2374 0.43102 1.0014
## sigma 0.00096067 0.4 60236 -0.0006277 1
## Tau[1,1] 0.11326 0.5 33854 0.021506 1
## Tau[2,1] 0.058528 0.6 31680 0.022325 1
## Tau[1,2] 0.058528 0.6 31680 0.022325 1
## Tau[2,2] 0.033375 0.5 33264 0.020865 1
## marg.post[1] 0.029867 0.4 60000 0.0082758 0.99999
## marg.post[2] 0.029828 0.4 60000 -0.00067984 1
## marg.post[3] 0.029868 0.4 60000 -0.0019692 1.0001
## marg.post[4] 0.029711 0.4 60868 -0.0076886 0.99998
## marg.post[5] 0.029538 0.4 61049 -0.007466 0.99999
## marg.post[6] 0.030282 0.4 58437 -0.0049604 1
## marg.post[7] 0.029558 0.4 61359 -0.000073153 1
## marg.post[8] 0.030131 0.4 60000 -0.0026119 1.0002
## marg.post[9] 0.029933 0.4 58992 -0.0028655 0.99998
## marg.post[10] 0.030495 0.4 60532 -0.0023439 1
## marg.post[11] 0.029847 0.4 59632 -0.000082403 0.99998
## marg.post[12] 0.030303 0.4 59796 -0.0035568 1.0001
## marg.post[13] 0.029619 0.4 61614 -0.003913 1
## marg.post[14] 0.029745 0.4 60626 -0.0049085 1
## marg.post[15] 0.029873 0.4 59510 0.0061913 1.0001
## marg.post[16] 0.029837 0.4 60271 -0.0027633 1.0001
## marg.post[17] 0.029861 0.4 60050 -0.0017257 1
## marg.post[18] 0.029569 0.4 60683 0.0036743 0.99998
## marg.post[19] 0.029887 0.4 60000 0.0043804 0.99998
## marg.post[20] 0.029741 0.4 60000 -0.0011175 1
## marg.post[21] 0.029842 0.4 60000 0.00050134 1
## marg.post[22] 0.030187 0.4 58726 0.0061065 1
## marg.post[23] 0.029831 0.4 59179 0.0059101 1
## marg.post[24] 0.031326 0.4 60000 0.011902 1
## marg.post[25] 0.030273 0.4 58810 -0.00036485 1.0001
## marg.post[26] 0.030295 0.4 60529 0.003086 0.99998
## marg.post[27] 0.030148 0.4 60429 0.0006257 1
## marg.post[28] 0.029682 0.4 60848 0.0074047 1
## marg.post[29] 0.030006 0.4 59295 0.0010687 1
## marg.post[30] 0.030223 0.4 60039 -0.00028846 1.0002
## marg.post[31] 0.030202 0.4 58751 0.0001086 0.99997
## marg.post[32] 0.029887 0.4 60000 -0.001149 1
## marg.post[33] 0.029773 0.4 60000 -0.00033361 0.99999
## marg.post[34] 0.032529 0.4 60000 -0.0006666 1
## marg.post[35] 0.030216 0.4 59488 -0.0027494 0.99999
## marg.post[36] 0.030226 0.4 59290 -0.0025263 0.99998
## marg.post[37] 0.030536 0.4 58267 -0.000028313 1.0001
## marg.post[38] 0.030462 0.4 58876 -0.0017421 1
## marg.post[39] 0.029644 0.4 60849 -0.0033639 1.0001
## marg.post[40] 0.029807 0.4 59743 0.0011623 1
## marg.post[41] 0.030574 0.4 60444 -0.0092331 1
## marg.post[42] 0.029737 0.4 60000 -0.0036112 1.0001
## marg.post[43] 0.029604 0.4 60000 0.0021822 1
## marg.post[44] 0.02967 0.4 60437 0.0012361 1
## marg.post[45] 0.030737 0.4 60000 0.0021551 1
## marg.post[46] 0.030913 0.4 59427 -0.0022006 0.99999
## marg.post[47] 0.03071 0.4 60000 -0.005501 1
## marg.post[48] 0.032036 0.4 60000 0.00069986 1
## marg.post[49] 0.032631 0.4 57538 0.004536 1
## marg.post[50] 0.036776 0.4 55202 0.0025312 0.99998
## marg.post[51] 0.031921 0.4 60000 0.0075444 1
## marg.post[52] 0.032197 0.4 58836 0.001176 1.0001
## marg.post[53] 0.032255 0.4 59513 0.00019617 1
## marg.post[54] 0.036058 0.4 54553 -0.0078337 1.0001
## marg.post[55] 0.032311 0.4 59515 0.00037123 1
## marg.post[56] 0.035535 0.4 57887 0.0026786 0.99998
## marg.post[57] 0.032539 0.4 57612 0.0039469 1
## marg.post[58] 0.029969 0.4 60410 -0.0047524 1
## marg.post[59] 0.030393 0.4 59297 -0.0041065 1
## marg.post[60] 0.029983 0.4 60433 0.0012523 0.99998
## marg.post[61] 0.03157 0.4 58475 -0.0021662 1
## marg.post[62] 0.030203 0.4 59959 0.0010352 1
## marg.post[63] 0.030022 0.4 60000 0.0033491 1
## marg.post[64] 0.030111 0.4 60126 0.0047337 1.0001
## marg.post[65] 0.029937 0.4 60832 -0.0056211 1
## marg.post[66] 0.029924 0.4 60000 0.0020304 1
## marg.post[67] 0.029972 0.4 60000 0.0012688 1
## marg.post[68] 0.031128 0.4 60410 -0.003151 1
## marg.post[69] 0.03124 0.4 60000 0.0060562 1
## marg.post[70] 0.03007 0.4 59207 -0.0056742 1
## marg.post[71] 0.029958 0.4 60000 0.0077042 1.0001
## marg.post[72] 0.033473 0.4 59621 0.0022773 1
## marg.post[73] 0.031048 0.4 60821 -0.0019097 1.0001
## marg.post[74] 0.029785 0.4 61379 0.0026549 1
## marg.post[75] 0.030138 0.4 60000 0.00031486 1
## marg.post[76] 0.03002 0.4 60000 0.0016154 0.99999
## marg.post[77] 0.031215 0.4 60000 -0.0015027 0.99998
## marg.post[78] 0.030204 0.4 59498 0.0041801 0.99999
## marg.post[79] 0.029933 0.4 60040 0.0009204 1
## marg.post[80] 0.030097 0.4 60000 0.0049728 0.99999
## marg.post[81] 0.02986 0.4 61129 -0.0027419 0.99998
## marg.post[82] 0.030171 0.4 59491 -0.0067099 1.0001
## marg.post[83] 0.031017 0.4 59571 0.00578 1
## marg.post[84] 0.030599 0.4 61056 0.0087284 1.0001
## marg.post[85] 0.029983 0.4 60279 -0.0025457 1
## marg.post[86] 0.030097 0.4 60513 -0.0072897 1
## marg.post[87] 0.029806 0.4 61183 0.0018622 1
## marg.post[88] 0.035511 0.4 59920 -0.0016982 0.99999
## marg.post[89] 0.029898 0.4 60556 -0.0040478 1
## marg.post[90] 0.029969 0.4 60605 0.0082358 1.0001
## marg.post[91] 0.030644 0.4 58386 -0.000015048 1.0001
## marg.post[92] 0.030168 0.4 59644 0.0034525 1
## marg.post[93] 0.03002 0.4 60000 -0.003472 1
## marg.post[94] 0.029964 0.4 60541 0.0014428 1
## marg.post[95] 0.030042 0.4 60010 -0.00068123 1.0001
## marg.post[96] 0.029659 0.4 61065 -0.0019622 1.0001
## marg.post[97] 0.033027 0.4 60000 0.0022646 1
## marg.post[98] 0.030091 0.4 59591 -0.004957 1
## marg.post[99] 0.030084 0.4 60000 0.0018799 1
## marg.post[100] 0.029978 0.4 60435 -0.00020821 0.99998
## marg.post[101] 0.030098 0.4 60000 0.0033958 1
## marg.post[102] 0.030105 0.4 60000 -0.0004785 1
## marg.post[103] 0.029977 0.4 60753 -0.0013912 0.99999
## marg.post[104] 0.029965 0.4 60000 -0.0013361 1
## marg.post[105] 0.0391 0.4 59089 -0.012214 1
## marg.post[106] 0.030614 0.4 59487 -0.0029372 1
## marg.post[107] 0.03034 0.4 59656 -0.010483 1
## marg.post[108] 0.030493 0.4 59617 -0.0071203 1
## marg.post[109] 0.030513 0.4 60481 -0.0021721 0.99999
## marg.post[110] 0.030447 0.4 59405 -0.0012192 0.99999
## marg.post[111] 0.030252 0.4 59623 -0.0033281 1.0001
## marg.post[112] 0.030389 0.4 59928 -0.00094997 0.99999
## marg.post[113] 0.029551 0.4 61633 -0.0092983 1
## marg.post[114] 0.030245 0.4 60499 0.0090765 1
## marg.post[115] 0.0307 0.4 59254 0.000018363 1
## marg.post[116] 0.03024 0.4 59847 -0.0043975 1
## marg.post[117] 0.030281 0.4 60000 -0.00046354 0.99997
## marg.post[118] 0.030341 0.4 59690 0.0058492 1
## marg.post[119] 0.030437 0.4 60000 -0.0073778 1
## marg.post[120] 0.030167 0.4 60360 -0.0078312 1
## marg.post[121] 0.030659 0.4 59452 0.0072618 1
## marg.post[122] 0.030244 0.4 60000 0.000063707 1
## marg.post[123] 0.030337 0.4 60000 -0.0011791 0.99998
## marg.post[124] 0.036424 0.4 59617 -0.0067472 1.0001
## marg.post[125] 0.030204 0.4 60460 -0.0007306 0.99999
## marg.post[126] 0.030171 0.4 60000 0.0009913 1
## marg.post[127] 0.0301 0.4 59823 -0.0012515 1
## marg.post[128] 0.030341 0.4 58967 0.0076172 1
## marg.post[129] 0.030179 0.4 60000 0.0063402 1
## marg.post[130] 0.030073 0.4 59948 -0.0038798 1.0001
## marg.post[131] 0.030557 0.4 58853 -0.000092501 1
## marg.post[132] 0.030131 0.4 59613 -0.0018628 1
## marg.post[133] 0.029984 0.4 60065 0.0023423 1
## marg.post[134] 0.029971 0.4 60476 -0.0063314 0.99998
## marg.post[135] 0.030336 0.4 59312 -0.0034697 1
## marg.post[136] 0.029708 0.4 61944 -0.0044302 1
## marg.post[137] 0.030243 0.4 60007 0.0044239 1.0001
## marg.post[138] 0.030249 0.4 59563 -0.0025781 1.0001
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## marg.post[140] 0.030378 0.4 59835 0.0019513 0.99998
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## marg.post[142] 0.030392 0.4 59492 0.0042539 1.0001
## marg.post[143] 0.031327 0.4 60000 -0.0047429 0.99998
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## marg.post[150] 0.031164 0.4 57868 0.007369 0.99997
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## marg.post[457] 0.030527 0.4 58043 -0.0040533 1
## marg.post[458] 0.030117 0.4 59618 0.0098425 1
## marg.post[459] 0.030631 0.4 59582 0.0070775 1.0001
## marg.post[460] 0.029974 0.4 59539 0.00093983 1
## marg.post[461] 0.032869 0.4 57895 0.012396 1
## marg.post[462] 0.030083 0.4 59734 0.0010912 1.0001
## marg.post[463] 0.030083 0.4 60467 0.0060365 1
## marg.post[464] 0.029897 0.4 60425 -0.00035248 1
## marg.post[465] 0.029841 0.4 60975 -0.0048997 1
## marg.post[466] 0.029945 0.4 59536 0.0008198 1
## marg.post[467] 0.030269 0.4 59548 -0.000027998 1
## marg.post[468] 0.030642 0.4 60429 0.00353 1
## marg.post[469] 0.032176 0.4 60000 -0.0058322 1
## marg.post[470] 0.030227 0.4 59645 0.0054251 1
## marg.post[471] 0.029876 0.4 60038 0.0025872 1
## marg.post[472] 0.030302 0.4 61364 -0.0049677 1
## marg.post[473] 0.030676 0.4 60360 -0.003027 0.99998
## marg.post[474] 0.030106 0.4 60000 -0.0050596 1.0001
## marg.post[475] 0.029659 0.4 60959 -0.00091851 1
## marg.post[476] 0.029913 0.4 60000 0.00045291 0.99998
## marg.post[477] 0.029916 0.4 60290 0.0055011 1
## marg.post[478] 0.030185 0.4 59414 -0.0020811 1.0001
## marg.post[479] 0.030006 0.4 60537 0.0030331 1
## marg.post[480] 0.031187 0.4 59911 -0.0075706 1
## marg.post[481] 0.029533 0.4 61211 -0.00037914 1
## marg.post[482] 0.029823 0.4 60652 0.0018873 1.0001
## marg.post[483] 0.030046 0.4 60000 -0.0041591 1
## marg.post[484] 0.030472 0.4 58469 -0.0065073 1
## marg.post[485] 0.030602 0.4 60316 -0.0020147 0.99998
## marg.post[486] 0.034229 0.4 58716 0.0073889 0.99998
## marg.post[487] 0.030775 0.4 59652 -0.0021545 0.99999
## marg.post[488] 0.030286 0.4 60054 -0.0041093 0.99999
## marg.post[489] 0.030331 0.4 59271 0.0030461 1
## marg.post[490] 0.030857 0.4 59786 0.00095699 1
## marg.post[491] 0.031922 0.4 60000 0.0079151 1
## marg.post[492] 0.030151 0.4 60519 0.0021547 0.99999
## marg.post[493] 0.029766 0.4 60756 -0.00049291 1.0001
## marg.post[494] 0.03074 0.4 60000 0.0039241 0.99998
## marg.post[495] 0.031861 0.4 56201 0.00021531 0.99997
## marg.post[496] 0.03018 0.4 59715 -0.0021169 1
## marg.post[497] 0.030419 0.4 60649 -0.00086749 1.0001
## marg.post[498] 0.030646 0.4 60000 -0.0064501 1
## marg.post[499] 0.030442 0.4 59013 0.0015514 1
## marg.post[500] 0.031005 0.4 60000 0.0020873 1
## marg.post[501] 0.03017 0.4 59932 0.0017976 1
## marg.post[502] 0.030927 0.4 60000 0.00097225 1
## marg.post[503] 0.030053 0.4 60000 -0.00060274 0.99999
## marg.post[504] 0.030018 0.4 60970 0.0023967 1
## marg.post[505] 0.030148 0.4 60000 -0.0046951 1.0001
## marg.post[506] 0.031418 0.4 60000 -0.0057041 1
## marg.post[507] 0.031213 0.4 60016 0.0025996 1
## marg.post[508] 0.030218 0.4 60494 0.0032392 1
## marg.post[509] 0.033216 0.4 59502 0.0039138 1
## marg.post[510] 0.030505 0.4 59028 0.0026123 1
## marg.post[511] 0.030468 0.4 60000 -0.0022936 1.0001
## marg.post[512] 0.031605 0.4 60588 -0.0074387 1
## marg.post[513] 0.030533 0.4 60000 0.0018126 1.0001
## marg.post[514] 0.030452 0.4 59945 0.0018164 0.99998
## marg.post[515] 0.033214 0.4 60000 -0.0014206 1
## marg.post[516] 0.031395 0.4 60888 0.0011305 0.99999
## marg.post[517] 0.030516 0.4 60000 -0.0013641 1
## marg.post[518] 0.031259 0.4 57515 0.0025833 1
## marg.post[519] 0.029263 0.4 63898 -0.0041738 1.0001
## intercept.blup[1] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[2] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[3] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[4] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[5] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[6] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[7] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[8] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[9] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[10] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[11] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[12] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[13] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[14] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[15] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[16] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[17] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[18] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[19] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[20] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[21] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[22] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[23] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[24] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[25] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[26] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[27] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[28] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[29] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[30] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[31] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[32] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[33] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[34] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[35] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[36] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[37] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[38] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[39] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[40] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[41] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[42] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[43] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[44] 0.056865 1.8 3064 0.33745 1.0009
## intercept.blup[45] 0.052402 1.4 5471 0.18615 1.0006
## intercept.blup[46] 0.052402 1.4 5471 0.18615 1.0006
## intercept.blup[47] 0.052402 1.4 5471 0.18615 1.0006
## intercept.blup[48] 0.052402 1.4 5471 0.18615 1.0006
## intercept.blup[49] 0.052402 1.4 5471 0.18615 1.0006
## intercept.blup[50] 0.052402 1.4 5471 0.18615 1.0006
## intercept.blup[51] 0.052402 1.4 5471 0.18615 1.0006
## intercept.blup[52] 0.052402 1.4 5471 0.18615 1.0006
## intercept.blup[53] 0.04417 0.9 11234 0.081182 1.0003
## intercept.blup[54] 0.04417 0.9 11234 0.081182 1.0003
## intercept.blup[55] 0.04417 0.9 11234 0.081182 1.0003
## intercept.blup[56] 0.04417 0.9 11234 0.081182 1.0003
## intercept.blup[57] 0.04417 0.9 11234 0.081182 1.0003
## intercept.blup[58] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[59] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[60] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[61] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[62] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[63] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[64] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[65] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[66] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[67] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[68] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[69] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[70] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[71] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[72] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[73] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[74] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[75] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[76] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[77] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[78] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[79] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[80] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[81] 0.020658 0.7 22886 0.011186 1.0001
## intercept.blup[82] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[83] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[84] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[85] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[86] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[87] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[88] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[89] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[90] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[91] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[92] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[93] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[94] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[95] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[96] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[97] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[98] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[99] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[100] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[101] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[102] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[103] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[104] 0.020163 0.7 21274 0.016978 1.0002
## intercept.blup[105] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[106] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[107] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[108] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[109] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[110] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[111] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[112] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[113] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[114] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[115] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[116] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[117] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[118] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[119] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[120] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[121] 0.020773 0.6 27168 -0.0017596 1
## intercept.blup[122] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[123] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[124] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[125] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[126] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[127] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[128] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[129] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[130] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[131] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[132] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[133] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[134] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[135] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[136] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[137] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[138] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[139] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[140] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[141] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[142] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[143] 0.020338 0.6 24243 0.0048947 0.99999
## intercept.blup[144] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[145] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[146] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[147] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[148] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[149] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[150] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[151] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[152] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[153] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[154] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[155] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[156] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[157] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[158] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[159] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[160] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[161] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[162] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[163] 0.020541 0.6 27621 0.0057713 1
## intercept.blup[164] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[165] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[166] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[167] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[168] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[169] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[170] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[171] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[172] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[173] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[174] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[175] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[176] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[177] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[178] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[179] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[180] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[181] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[182] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[183] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[184] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[185] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[186] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[187] 0.020787 0.7 23404 0.016017 1.0001
## intercept.blup[188] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[189] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[190] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[191] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[192] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[193] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[194] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[195] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[196] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[197] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[198] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[199] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[200] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[201] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[202] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[203] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[204] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[205] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[206] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[207] 0.020603 0.7 23536 0.011028 0.99999
## intercept.blup[208] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[209] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[210] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[211] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[212] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[213] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[214] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[215] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[216] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[217] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[218] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[219] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[220] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[221] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[222] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[223] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[224] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[225] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[226] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[227] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[228] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[229] 0.020508 0.7 19604 0.018153 1.0001
## intercept.blup[230] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[231] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[232] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[233] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[234] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[235] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[236] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[237] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[238] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[239] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[240] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[241] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[242] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[243] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[244] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[245] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[246] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[247] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[248] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[249] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[250] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[251] 0.020126 0.7 20309 0.022829 1.0001
## intercept.blup[252] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[253] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[254] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[255] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[256] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[257] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[258] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[259] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[260] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[261] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[262] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[263] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[264] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[265] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[266] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[267] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[268] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[269] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[270] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[271] 0.019873 0.6 25199 0.0098199 1
## intercept.blup[272] 0.053083 1.4 4883 0.20277 1.001
## intercept.blup[273] 0.053083 1.4 4883 0.20277 1.001
## intercept.blup[274] 0.053083 1.4 4883 0.20277 1.001
## intercept.blup[275] 0.053083 1.4 4883 0.20277 1.001
## intercept.blup[276] 0.053083 1.4 4883 0.20277 1.001
## intercept.blup[277] 0.053083 1.4 4883 0.20277 1.001
## intercept.blup[278] 0.053083 1.4 4883 0.20277 1.001
## intercept.blup[279] 0.053083 1.4 4883 0.20277 1.001
## intercept.blup[280] 0.053083 1.4 4883 0.20277 1.001
## intercept.blup[281] 0.053083 1.4 4883 0.20277 1.001
## intercept.blup[282] 0.053083 1.4 4883 0.20277 1.001
## intercept.blup[283] 0.053083 1.4 4883 0.20277 1.001
## intercept.blup[284] 0.053083 1.4 4883 0.20277 1.001
## intercept.blup[285] 0.053083 1.4 4883 0.20277 1.001
## intercept.blup[286] 0.053083 1.4 4883 0.20277 1.001
## intercept.blup[287] 0.053083 1.4 4883 0.20277 1.001
## intercept.blup[288] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[289] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[290] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[291] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[292] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[293] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[294] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[295] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[296] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[297] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[298] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[299] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[300] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[301] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[302] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[303] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[304] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[305] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[306] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[307] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[308] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[309] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[310] 0.05455 1.5 4230 0.23854 1.0007
## intercept.blup[311] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[312] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[313] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[314] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[315] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[316] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[317] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[318] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[319] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[320] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[321] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[322] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[323] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[324] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[325] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[326] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[327] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[328] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[329] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[330] 0.054555 1.5 4438 0.22624 1.0008
## intercept.blup[331] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[332] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[333] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[334] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[335] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[336] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[337] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[338] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[339] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[340] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[341] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[342] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[343] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[344] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[345] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[346] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[347] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[348] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[349] 0.052063 1.4 5269 0.18458 1.0005
## intercept.blup[350] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[351] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[352] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[353] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[354] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[355] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[356] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[357] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[358] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[359] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[360] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[361] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[362] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[363] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[364] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[365] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[366] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[367] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[368] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[369] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[370] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[371] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[372] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[373] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[374] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[375] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[376] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[377] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[378] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[379] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[380] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[381] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[382] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[383] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[384] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[385] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[386] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[387] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[388] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[389] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[390] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[391] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[392] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[393] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[394] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[395] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[396] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[397] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[398] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[399] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[400] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[401] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[402] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[403] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[404] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[405] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[406] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[407] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[408] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[409] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[410] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[411] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[412] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[413] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[414] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[415] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[416] 0.058002 1.8 3090 0.32037 1.0012
## intercept.blup[417] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[418] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[419] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[420] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[421] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[422] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[423] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[424] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[425] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[426] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[427] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[428] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[429] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[430] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[431] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[432] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[433] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[434] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[435] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[436] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[437] 0.020803 0.6 27513 0.008609 1.0001
## intercept.blup[438] 0.02043 0.6 23874 0.014132 1
## intercept.blup[439] 0.02043 0.6 23874 0.014132 1
## intercept.blup[440] 0.02043 0.6 23874 0.014132 1
## intercept.blup[441] 0.02043 0.6 23874 0.014132 1
## intercept.blup[442] 0.02043 0.6 23874 0.014132 1
## intercept.blup[443] 0.02043 0.6 23874 0.014132 1
## intercept.blup[444] 0.02043 0.6 23874 0.014132 1
## intercept.blup[445] 0.02043 0.6 23874 0.014132 1
## intercept.blup[446] 0.02043 0.6 23874 0.014132 1
## intercept.blup[447] 0.02043 0.6 23874 0.014132 1
## intercept.blup[448] 0.02043 0.6 23874 0.014132 1
## intercept.blup[449] 0.02043 0.6 23874 0.014132 1
## intercept.blup[450] 0.02043 0.6 23874 0.014132 1
## intercept.blup[451] 0.02043 0.6 23874 0.014132 1
## intercept.blup[452] 0.02043 0.6 23874 0.014132 1
## intercept.blup[453] 0.02043 0.6 23874 0.014132 1
## intercept.blup[454] 0.02043 0.6 23874 0.014132 1
## intercept.blup[455] 0.02043 0.6 23874 0.014132 1
## intercept.blup[456] 0.02043 0.6 23874 0.014132 1
## intercept.blup[457] 0.02043 0.6 23874 0.014132 1
## intercept.blup[458] 0.02043 0.6 23874 0.014132 1
## intercept.blup[459] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[460] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[461] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[462] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[463] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[464] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[465] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[466] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[467] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[468] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[469] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[470] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[471] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[472] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[473] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[474] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[475] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[476] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[477] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[478] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[479] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[480] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[481] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[482] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[483] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[484] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[485] 0.020224 0.7 21877 0.010563 1.0001
## intercept.blup[486] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[487] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[488] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[489] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[490] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[491] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[492] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[493] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[494] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[495] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[496] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[497] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[498] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[499] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[500] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[501] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[502] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[503] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[504] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[505] 0.020326 0.7 20867 0.017086 1.0001
## intercept.blup[506] 0.020739 0.7 23605 0.0067581 1.0001
## intercept.blup[507] 0.020739 0.7 23605 0.0067581 1.0001
## intercept.blup[508] 0.020739 0.7 23605 0.0067581 1.0001
## intercept.blup[509] 0.020739 0.7 23605 0.0067581 1.0001
## intercept.blup[510] 0.020739 0.7 23605 0.0067581 1.0001
## intercept.blup[511] 0.020739 0.7 23605 0.0067581 1.0001
## intercept.blup[512] 0.020739 0.7 23605 0.0067581 1.0001
## intercept.blup[513] 0.020739 0.7 23605 0.0067581 1.0001
## intercept.blup[514] 0.020739 0.7 23605 0.0067581 1.0001
## intercept.blup[515] 0.020739 0.7 23605 0.0067581 1.0001
## intercept.blup[516] 0.020739 0.7 23605 0.0067581 1.0001
## intercept.blup[517] 0.020739 0.7 23605 0.0067581 1.0001
## intercept.blup[518] 0.020739 0.7 23605 0.0067581 1.0001
## intercept.blup[519] 0.020739 0.7 23605 0.0067581 1.0001
## slope.blup[1] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[2] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[3] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[4] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[5] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[6] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[7] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[8] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[9] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[10] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[11] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[12] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[13] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[14] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[15] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[16] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[17] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[18] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[19] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[20] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[21] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[22] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[23] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[24] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[25] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[26] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[27] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[28] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[29] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[30] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[31] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[32] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[33] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[34] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[35] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[36] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[37] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[38] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[39] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[40] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[41] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[42] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[43] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[44] 0.031975 2.1 2365 0.43357 1.0011
## slope.blup[45] 0.025905 1.1 8539 0.1227 1.0005
## slope.blup[46] 0.025905 1.1 8539 0.1227 1.0005
## slope.blup[47] 0.025905 1.1 8539 0.1227 1.0005
## slope.blup[48] 0.025905 1.1 8539 0.1227 1.0005
## slope.blup[49] 0.025905 1.1 8539 0.1227 1.0005
## slope.blup[50] 0.025905 1.1 8539 0.1227 1.0005
## slope.blup[51] 0.025905 1.1 8539 0.1227 1.0005
## slope.blup[52] 0.025905 1.1 8539 0.1227 1.0005
## slope.blup[53] 0.025706 1.2 7213 0.13523 1.0004
## slope.blup[54] 0.025706 1.2 7213 0.13523 1.0004
## slope.blup[55] 0.025706 1.2 7213 0.13523 1.0004
## slope.blup[56] 0.025706 1.2 7213 0.13523 1.0004
## slope.blup[57] 0.025706 1.2 7213 0.13523 1.0004
## slope.blup[58] 0.011188 0.7 23399 0.0084296 1
## slope.blup[59] 0.011188 0.7 23399 0.0084296 1
## slope.blup[60] 0.011188 0.7 23399 0.0084296 1
## slope.blup[61] 0.011188 0.7 23399 0.0084296 1
## slope.blup[62] 0.011188 0.7 23399 0.0084296 1
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## slope.blup[64] 0.011188 0.7 23399 0.0084296 1
## slope.blup[65] 0.011188 0.7 23399 0.0084296 1
## slope.blup[66] 0.011188 0.7 23399 0.0084296 1
## slope.blup[67] 0.011188 0.7 23399 0.0084296 1
## slope.blup[68] 0.011188 0.7 23399 0.0084296 1
## slope.blup[69] 0.011188 0.7 23399 0.0084296 1
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## slope.blup[71] 0.011188 0.7 23399 0.0084296 1
## slope.blup[72] 0.011188 0.7 23399 0.0084296 1
## slope.blup[73] 0.011188 0.7 23399 0.0084296 1
## slope.blup[74] 0.011188 0.7 23399 0.0084296 1
## slope.blup[75] 0.011188 0.7 23399 0.0084296 1
## slope.blup[76] 0.011188 0.7 23399 0.0084296 1
## slope.blup[77] 0.011188 0.7 23399 0.0084296 1
## slope.blup[78] 0.011188 0.7 23399 0.0084296 1
## slope.blup[79] 0.011188 0.7 23399 0.0084296 1
## slope.blup[80] 0.011188 0.7 23399 0.0084296 1
## slope.blup[81] 0.011188 0.7 23399 0.0084296 1
## slope.blup[82] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[83] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[84] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[85] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[86] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[87] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[88] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[89] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[90] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[91] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[92] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[93] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[94] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[95] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[96] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[97] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[98] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[99] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[100] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[101] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[102] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[103] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[104] 0.011 0.7 21648 0.014482 1.0002
## slope.blup[105] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[106] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[107] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[108] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[109] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[110] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[111] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[112] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[113] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[114] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[115] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[116] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[117] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[118] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[119] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[120] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[121] 0.010903 0.6 24241 -0.0044989 1.0001
## slope.blup[122] 0.010956 0.6 24746 0.0018114 1
## slope.blup[123] 0.010956 0.6 24746 0.0018114 1
## slope.blup[124] 0.010956 0.6 24746 0.0018114 1
## slope.blup[125] 0.010956 0.6 24746 0.0018114 1
## slope.blup[126] 0.010956 0.6 24746 0.0018114 1
## slope.blup[127] 0.010956 0.6 24746 0.0018114 1
## slope.blup[128] 0.010956 0.6 24746 0.0018114 1
## slope.blup[129] 0.010956 0.6 24746 0.0018114 1
## slope.blup[130] 0.010956 0.6 24746 0.0018114 1
## slope.blup[131] 0.010956 0.6 24746 0.0018114 1
## slope.blup[132] 0.010956 0.6 24746 0.0018114 1
## slope.blup[133] 0.010956 0.6 24746 0.0018114 1
## slope.blup[134] 0.010956 0.6 24746 0.0018114 1
## slope.blup[135] 0.010956 0.6 24746 0.0018114 1
## slope.blup[136] 0.010956 0.6 24746 0.0018114 1
## slope.blup[137] 0.010956 0.6 24746 0.0018114 1
## slope.blup[138] 0.010956 0.6 24746 0.0018114 1
## slope.blup[139] 0.010956 0.6 24746 0.0018114 1
## slope.blup[140] 0.010956 0.6 24746 0.0018114 1
## slope.blup[141] 0.010956 0.6 24746 0.0018114 1
## slope.blup[142] 0.010956 0.6 24746 0.0018114 1
## slope.blup[143] 0.010956 0.6 24746 0.0018114 1
## slope.blup[144] 0.010814 0.7 19741 0.011708 1
## slope.blup[145] 0.010814 0.7 19741 0.011708 1
## slope.blup[146] 0.010814 0.7 19741 0.011708 1
## slope.blup[147] 0.010814 0.7 19741 0.011708 1
## slope.blup[148] 0.010814 0.7 19741 0.011708 1
## slope.blup[149] 0.010814 0.7 19741 0.011708 1
## slope.blup[150] 0.010814 0.7 19741 0.011708 1
## slope.blup[151] 0.010814 0.7 19741 0.011708 1
## slope.blup[152] 0.010814 0.7 19741 0.011708 1
## slope.blup[153] 0.010814 0.7 19741 0.011708 1
## slope.blup[154] 0.010814 0.7 19741 0.011708 1
## slope.blup[155] 0.010814 0.7 19741 0.011708 1
## slope.blup[156] 0.010814 0.7 19741 0.011708 1
## slope.blup[157] 0.010814 0.7 19741 0.011708 1
## slope.blup[158] 0.010814 0.7 19741 0.011708 1
## slope.blup[159] 0.010814 0.7 19741 0.011708 1
## slope.blup[160] 0.010814 0.7 19741 0.011708 1
## slope.blup[161] 0.010814 0.7 19741 0.011708 1
## slope.blup[162] 0.010814 0.7 19741 0.011708 1
## slope.blup[163] 0.010814 0.7 19741 0.011708 1
## slope.blup[164] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[165] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[166] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[167] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[168] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[169] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[170] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[171] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[172] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[173] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[174] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[175] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[176] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[177] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[178] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[179] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[180] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[181] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[182] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[183] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[184] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[185] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[186] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[187] 0.011034 0.7 20931 0.017837 1.0001
## slope.blup[188] 0.010966 0.7 18805 0.016855 1
## slope.blup[189] 0.010966 0.7 18805 0.016855 1
## slope.blup[190] 0.010966 0.7 18805 0.016855 1
## slope.blup[191] 0.010966 0.7 18805 0.016855 1
## slope.blup[192] 0.010966 0.7 18805 0.016855 1
## slope.blup[193] 0.010966 0.7 18805 0.016855 1
## slope.blup[194] 0.010966 0.7 18805 0.016855 1
## slope.blup[195] 0.010966 0.7 18805 0.016855 1
## slope.blup[196] 0.010966 0.7 18805 0.016855 1
## slope.blup[197] 0.010966 0.7 18805 0.016855 1
## slope.blup[198] 0.010966 0.7 18805 0.016855 1
## slope.blup[199] 0.010966 0.7 18805 0.016855 1
## slope.blup[200] 0.010966 0.7 18805 0.016855 1
## slope.blup[201] 0.010966 0.7 18805 0.016855 1
## slope.blup[202] 0.010966 0.7 18805 0.016855 1
## slope.blup[203] 0.010966 0.7 18805 0.016855 1
## slope.blup[204] 0.010966 0.7 18805 0.016855 1
## slope.blup[205] 0.010966 0.7 18805 0.016855 1
## slope.blup[206] 0.010966 0.7 18805 0.016855 1
## slope.blup[207] 0.010966 0.7 18805 0.016855 1
## slope.blup[208] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[209] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[210] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[211] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[212] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[213] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[214] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[215] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[216] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[217] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[218] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[219] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[220] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[221] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[222] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[223] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[224] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[225] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[226] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[227] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[228] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[229] 0.010979 0.8 16975 0.020699 1.0001
## slope.blup[230] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[231] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[232] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[233] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[234] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[235] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[236] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[237] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[238] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[239] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[240] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[241] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[242] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[243] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[244] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[245] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[246] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[247] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[248] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[249] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[250] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[251] 0.01169 0.6 27240 0.014112 1.0001
## slope.blup[252] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[253] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[254] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[255] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[256] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[257] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[258] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[259] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[260] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[261] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[262] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[263] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[264] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[265] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[266] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[267] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[268] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[269] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[270] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[271] 0.011316 0.6 29698 0.0087836 0.99999
## slope.blup[272] 0.030314 1.8 3085 0.33566 1.0013
## slope.blup[273] 0.030314 1.8 3085 0.33566 1.0013
## slope.blup[274] 0.030314 1.8 3085 0.33566 1.0013
## slope.blup[275] 0.030314 1.8 3085 0.33566 1.0013
## slope.blup[276] 0.030314 1.8 3085 0.33566 1.0013
## slope.blup[277] 0.030314 1.8 3085 0.33566 1.0013
## slope.blup[278] 0.030314 1.8 3085 0.33566 1.0013
## slope.blup[279] 0.030314 1.8 3085 0.33566 1.0013
## slope.blup[280] 0.030314 1.8 3085 0.33566 1.0013
## slope.blup[281] 0.030314 1.8 3085 0.33566 1.0013
## slope.blup[282] 0.030314 1.8 3085 0.33566 1.0013
## slope.blup[283] 0.030314 1.8 3085 0.33566 1.0013
## slope.blup[284] 0.030314 1.8 3085 0.33566 1.0013
## slope.blup[285] 0.030314 1.8 3085 0.33566 1.0013
## slope.blup[286] 0.030314 1.8 3085 0.33566 1.0013
## slope.blup[287] 0.030314 1.8 3085 0.33566 1.0013
## slope.blup[288] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[289] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[290] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[291] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[292] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[293] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[294] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[295] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[296] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[297] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[298] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[299] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[300] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[301] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[302] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[303] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[304] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[305] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[306] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[307] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[308] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[309] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[310] 0.030464 1.7 3451 0.29237 1.0007
## slope.blup[311] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[312] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[313] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[314] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[315] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[316] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[317] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[318] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[319] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[320] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[321] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[322] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[323] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[324] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[325] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[326] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[327] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[328] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[329] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[330] 0.030884 1.9 2867 0.35829 1.0011
## slope.blup[331] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[332] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[333] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[334] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[335] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[336] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[337] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[338] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[339] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[340] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[341] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[342] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[343] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[344] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[345] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[346] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[347] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[348] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[349] 0.028351 1.5 4746 0.20542 1.0006
## slope.blup[350] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[351] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[352] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[353] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[354] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[355] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[356] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[357] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[358] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[359] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[360] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[361] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[362] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[363] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[364] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[365] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[366] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[367] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[368] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[369] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[370] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[371] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[372] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[373] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[374] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[375] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[376] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[377] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[378] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[379] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[380] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[381] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[382] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[383] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[384] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[385] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[386] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[387] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[388] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[389] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[390] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[391] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[392] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[393] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[394] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[395] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[396] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[397] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[398] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[399] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[400] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[401] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[402] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[403] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[404] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[405] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[406] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[407] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[408] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[409] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[410] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[411] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[412] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[413] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[414] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[415] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[416] 0.032476 2.1 2208 0.46109 1.0014
## slope.blup[417] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[418] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[419] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[420] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[421] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[422] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[423] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[424] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[425] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[426] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[427] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[428] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[429] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[430] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[431] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[432] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[433] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[434] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[435] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[436] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[437] 0.01094 0.7 22355 0.012584 1.0001
## slope.blup[438] 0.011271 0.6 26494 0.015347 1
## slope.blup[439] 0.011271 0.6 26494 0.015347 1
## slope.blup[440] 0.011271 0.6 26494 0.015347 1
## slope.blup[441] 0.011271 0.6 26494 0.015347 1
## slope.blup[442] 0.011271 0.6 26494 0.015347 1
## slope.blup[443] 0.011271 0.6 26494 0.015347 1
## slope.blup[444] 0.011271 0.6 26494 0.015347 1
## slope.blup[445] 0.011271 0.6 26494 0.015347 1
## slope.blup[446] 0.011271 0.6 26494 0.015347 1
## slope.blup[447] 0.011271 0.6 26494 0.015347 1
## slope.blup[448] 0.011271 0.6 26494 0.015347 1
## slope.blup[449] 0.011271 0.6 26494 0.015347 1
## slope.blup[450] 0.011271 0.6 26494 0.015347 1
## slope.blup[451] 0.011271 0.6 26494 0.015347 1
## slope.blup[452] 0.011271 0.6 26494 0.015347 1
## slope.blup[453] 0.011271 0.6 26494 0.015347 1
## slope.blup[454] 0.011271 0.6 26494 0.015347 1
## slope.blup[455] 0.011271 0.6 26494 0.015347 1
## slope.blup[456] 0.011271 0.6 26494 0.015347 1
## slope.blup[457] 0.011271 0.6 26494 0.015347 1
## slope.blup[458] 0.011271 0.6 26494 0.015347 1
## slope.blup[459] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[460] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[461] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[462] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[463] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[464] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[465] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[466] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[467] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[468] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[469] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[470] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[471] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[472] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[473] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[474] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[475] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[476] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[477] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[478] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[479] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[480] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[481] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[482] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[483] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[484] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[485] 0.011058 0.7 20387 0.017425 1.0001
## slope.blup[486] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[487] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[488] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[489] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[490] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[491] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[492] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[493] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[494] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[495] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[496] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[497] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[498] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[499] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[500] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[501] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[502] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[503] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[504] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[505] 0.011104 0.7 18038 0.017858 1.0001
## slope.blup[506] 0.010913 0.7 18056 0.017433 1.0001
## slope.blup[507] 0.010913 0.7 18056 0.017433 1.0001
## slope.blup[508] 0.010913 0.7 18056 0.017433 1.0001
## slope.blup[509] 0.010913 0.7 18056 0.017433 1.0001
## slope.blup[510] 0.010913 0.7 18056 0.017433 1.0001
## slope.blup[511] 0.010913 0.7 18056 0.017433 1.0001
## slope.blup[512] 0.010913 0.7 18056 0.017433 1.0001
## slope.blup[513] 0.010913 0.7 18056 0.017433 1.0001
## slope.blup[514] 0.010913 0.7 18056 0.017433 1.0001
## slope.blup[515] 0.010913 0.7 18056 0.017433 1.0001
## slope.blup[516] 0.010913 0.7 18056 0.017433 1.0001
## slope.blup[517] 0.010913 0.7 18056 0.017433 1.0001
## slope.blup[518] 0.010913 0.7 18056 0.017433 1.0001
## slope.blup[519] 0.010913 0.7 18056 0.017433 1.0001
##
## Total time taken: 5.7 minutesNow we can look at posterior starting with some data manipulation
ffit <- mcmc(re.mod4.runjags)
names(ffit) # lets you know what is available## [1] "mcmc" "deviance.table" "deviance.sum"
## [4] "pd" "end.state" "samplers"
## [7] "burnin" "sample" "thin"
## [10] "model" "data" "monitor"
## [13] "noread.monitor" "modules" "factories"
## [16] "response" "residual" "fitted"
## [19] "method" "method.options" "timetaken"
## [22] "runjags.version" "summary" "HPD"
## [25] "hpd" "mcse" "psrf"
## [28] "autocorr" "crosscorr" "stochastic"
## [31] "trace" "density" "hist"
## [34] "ecdfplot" "key" "acplot"
## [37] "ccplot" "summaries" "summary.available"
## [40] "summary.pars" "dic"# posterior samples are in ffit$mcmc ---> there are separate columns for each chain, i.e., 15000 x 4
g00 <- as.array(ffit$mcmc[,1])
g10 <- as.array(ffit$mcmc[,2])
g11 <- as.array(ffit$mcmc[,3])
g2 <- as.array(ffit$mcmc[,4])
g3 <- as.array(ffit$mcmc[,5])
sigma <- as.array(ffit$mcmc[,6])
tau11 <- as.array(ffit$mcmc[,7])
tau12 <- as.array(ffit$mcmc[,8])
tau22 <- as.array(ffit$mcmc[,10])Some more data manipulations
margin.post <- as.array(ffit$mcmc[,11:529]) # separate matrices for each chain, i.e. 10000 x 519 x 4
intercept.blup <- as.array(ffit$mcmc[,530:1048])
slope.blup <- as.array(ffit$mcmc[,1049:1567])
# just want fit and fit.new
# to get 40,000 x 519:
marginal.means <- rbind(margin.post[,1:519,1],margin.post[,1:519,2],margin.post[,1:519,3],margin.post[,1:519,4])
int.blup <- rbind(intercept.blup[,1:519,1],intercept.blup[,1:519,2],intercept.blup[,1:519,3],intercept.blup[,1:519,4])
slp.blup <- rbind(slope.blup[,1:519,1],slope.blup[,1:519,2],slope.blup[,1:519,3],slope.blup[,1:519,4])
# Sum over rows to get fitted values (marginal) for each student
nels$student.margin <- apply(marginal.means,2,"mean") Using computed values above, plot data and fitted marginal and conditional distributions
# Data & fitted margin
par(mfrow=c(2,2))
hist(nels$math,breaks=20,main="Data: Math margin")
hist(nels$student.margin,breaks=20,main="Marginal Distribution")
# to get the conditional margin:
cond.mean <- marginal.means + int.blup + slp.blup
nels$conditional.means <- apply(cond.mean,2,"mean")
hist(nels$conditional.mean,breaks=20,main="Conditional Means")
cor(nels$conditional.mean,nels$math)## [1] 0.7319399
Let's go the other way: collapse over students
# Compute statistics over columns (students)
ymeans <- apply(cond.mean,1,"mean")
ymed <- apply(cond.mean,1,"median")
ymax <- apply(cond.mean,1,"max")
ysd <- apply(cond.mean,1,"sd")
# Bayesian p-values
pmean <- mean(mean(nels$math) < ymeans)
pmed <- mean(median(nels$math) < ymed)
pmax <- mean(max(nels$math) < ymax)
psd <- mean(sd(nels$math) < ysd)And some graphs for these
# Graphs of descriptive statistics from posterior predictive distribution
par(mfrow=c(2,2))
hist(ymed,breaks=10,main=paste("Conditional Medians", "\nBayesian P-value =", round(pmed, 2)))
lines(c(median(nels$math),median(nels$math)),c(0,10000),col="blue",lwd=2)
hist(ymax,breaks=10,main=paste("Conditional Maximums", "\nBayesian P-value =", round(pmax, 2)))
lines(c(max(nels$math),max(nels$math)),c(0,10000),col="blue",lwd=2)
hist(ymeans,breaks=10,main=paste("Conditional Means", "\nBayesian P-value =", round(pmean, 2)))
lines(c(mean(nels$math),mean(nels$math)),c(0,10000),col="blue",lwd=2)
hist(ysd,breaks=10,main=paste("Conditional SDs", "\nBayesian P-value =", round(psd, 2)))
lines(c(sd(nels$math),sd(nels$math)),c(0,10000),col="blue",lwd=2)
And if you want to look at the estimated random effects
plot(nels$math,nels$conditional.means,type="p")
plot(nels$math,nels$student.margin,type="p")
hist(int.blup)
hist(slp.blup)