Dear R users
I am trying to obtain p-values for (quasi)poisson lmer models, including
Markov-chain Monte Carlo sampling and the command summary.
>
> My problems is that p values derived from both these methods are
totally different. My question is
(1) there a bug in my code and
>
(2) How can I proceed, left with these uncertainties in the estimations of
> the p-values?
>
> Below is the corresponding R code with some output:
> ##
> fit<-lmer(End~Treatment+offset(log(Area))+(1|Site/Treatment), family=poisson
> ## Results
> summary(fit)
AIC BIC loglik deviance
28.92 35.99 -8.46 16.92
Random effects
Groups Name Variance Std dev.
Treatment * Site Intercept 5e-10 2.2361e-05
Site Intercept 5e-10 2.2361e-05
number of obs 24 groups Treatment*Site 8 and Site 2
Fixed effects
Estimate Std error z value P
Intercept -3.8290 0.4995 -7.666 1.77e-14****
Treatment 2 3.7970 0.505 7.516 5.51e-14****
Treatment 3 0.2409 9.6704 0.359 0.719 Treatment 4 -0.2483 0.8661 -0.287 0.774
Correlation of fixed effects
Intra T2 T3
T2 -0.989
T3 -0.745 0.737
T4 -0.577 0.570 0.430
> The p-values from mcmc are:
>
mcmcpvalue<-function(samp)
{
std<-backsolve(chol(var(samp)),
cbind(0,t(samp))-colMeans(samp),
transpose=TRUE)
sqdist<-colSums(std*std)
sum(sqdist[-1]>sqdist[1]/nrow(samp)
}
fitSI<-mcmcsamp(fit,50000)
library(coda)
HPDinterval(fitSI)
lower upper
Intercept -4.0778905 -3.1366836
Treatment2 3.4455972 4.3196598
Treatment 3 0.399302 1.287747
Treatment 4 -1.7898933 -0.2980325
log(Treatment*Site.(in)) -22.2198233 -19.7342530 log(Site.(In))
-28.7857601 -23.0952939
mcmcpvalue(as.Matrix(fitSI[,1]))
etc
Intecept 0
Treatment 2 0
Treatment 3 0.0075
Treatment 4 0.00758
log(Treatment*Site.(in)) 0
log(Site.(In)) 0
Any help in explaining why these two results are completely different
would be much appreciated. I have tried the example and found the same
results as given.
Cheers
Beth
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