Hi, everyone.  I don't know what is going wrong with my bootstrap.  Could
anyone help me find out what is wrong?

I am trying to bootstrap the variance component in a mixed linear model.

My statistic for the bootstrap is the following:

varcomp <- function ( formula, data, indices ) {
>     d <- data[indices,] #sample for boot
>     fit <- lmer(formula, data=d) #linear model
>     return (attr (VarCorr(fit), "sc")^2) #output random effects residual
> var
> }


The formula for the model is: log( y ) ~ ( 1 | a:b ) + a, where '*b*' is a
random effect nested within '*a';* which is a fixed effect.

When I run the linear model on its own it works fine, but when I try to run
the bootstrap I get warning messages of false convergence.

Warning messages:
> 1: In mer_finalize(ans) : false convergence (8)
> 2: In mer_finalize(ans) : false convergence (8)
> 3: In mer_finalize(ans) : false convergence (8)
> 4: In mer_finalize(ans) : false convergence (8)
> 5: In mer_finalize(ans) : false convergence (8)
> 6: In mer_finalize(ans) : false convergence (8)


The example data I used can be downloaded at:
http://www.mediafire.com/?77xb24rmul90k5d

The R code I actually did:

library(boot)
library(lme4)
#
#Load data
#
fp1 <- read.csv("desktop/p1f.csv")
#Set as factors
fp1$Cell.line <- as.factor(fp1$Cell.line)
fp1$DNA.extract <- as.factor(fp1$DNA.extract)
#test mixed model
lm1 <- lmer(formula=log(CNPC)~(1|Cell.line:DNA.extract)+Cell.line,data=fp1)
attr (VarCorr(lm1), "sc")
#
##Bootstrap statistic
#
varcomp <- function ( formula, data, indices ) {
    d <- data[indices,] #sample for boot
    fit <- lmer(formula, data=d) #linear model
    return (attr (VarCorr(fit), "sc")) #output random effects residual
standard deviation
}
##Bootstrap with 1000 replicates
fp1.boot <- boot ( data = fp1, statistic=varcomp, R=1000,
formula=log(CNPC)~(1|Cell.line:DNA.extract)+Cell.line)

	[[alternative HTML version deleted]]


