The following appears to be an answer to your question, though
I'd be pleased to receive critiques from others. Since your example
is NOT self contained, I modified an example in the "glmmPQL" help file:
(fit <- glmmPQL(y ~ factor(week)-1+trt, random = ~ 1 | ID,
+ family = binomial, data = bacteria))
iteration 1
iteration 2
iteration 3
iteration 4
iteration 5
iteration 6
Linear mixed-effects model fit by maximum likelihood
Data: bacteria
Log-likelihood: -551.1184
Fixed: y ~ factor(week) - 1 + trt
factor(week)0 factor(week)2 factor(week)4 factor(week)6
factor(week)11
3.3459650 3.5262521 1.9102037 1.7645881
1.7660845
trtdrug trtdrug+
-1.2527642 -0.7570441
Random effects:
Formula: ~1 | ID
(Intercept) Residual
StdDev: 1.426534 0.7747477
Variance function:
Structure: fixed weights
Formula: ~invwt
Number of Observations: 220
Number of Groups: 50
numDF denDF F-value p-value
factor(week) 5 166 10.821682 <.0001
trt 2 48 1.889473 0.1622
(denDF.week <- anova(fit)$denDF[1])
(denDF.week <- anova(fit)$denDF[1])
(par.week <- fixef(fit)[1:5])
factor(week)0 factor(week)2 factor(week)4 factor(week)6
factor(week)11
3.345965 3.526252 1.910204 1.764588
1.766085
(vc.week <- vcov(fit)[1:5, 1:5])
factor(week)0 factor(week)2 factor(week)4 factor(week)6
factor(week)0 0.3351649 0.1799365 0.1705898 0.1694884
factor(week)2 0.1799365 0.3709887 0.1683038 0.1684096
factor(week)4 0.1705898 0.1683038 0.2655072 0.1655673
factor(week)6 0.1694884 0.1684096 0.1655673 0.2674647
factor(week)11 0.1668450 0.1665177 0.1616748 0.1638169
factor(week)11
factor(week)0 0.1668450
factor(week)2 0.1665177
factor(week)4 0.1616748
factor(week)6 0.1638169
factor(week)11 0.2525962
CM <- array(0, dim=c(5*4/2, 5))
i1 <- 0
for(i in 1:4)for(j in (i+1):5){
+ i1 <- i1+1
+ CM[i1, c(i, j)] <- c(-1, 1)
+ }
[,1] [,2] [,3] [,4] [,5]
[1,] -1 1 0 0 0
[2,] -1 0 1 0 0
[3,] -1 0 0 1 0
[4,] -1 0 0 0 1
[5,] 0 -1 1 0 0
[6,] 0 -1 0 1 0
[7,] 0 -1 0 0 1
[8,] 0 0 -1 1 0
[9,] 0 0 -1 0 1
[10,] 0 0 0 -1 1
library(multcomp)
csimint(par.week, df=denDF.week, covm=vc.week,cmatrix=CM)
Simultaneous confidence intervals: user-defined contrasts
95 % confidence intervals
Estimate 2.5 % 97.5 %
[1,] 0.180 -1.439 1.800
[2,] -1.436 -2.838 -0.034
[3,] -1.581 -2.995 -0.168
[4,] -1.580 -2.967 -0.193
[5,] -1.616 -3.123 -0.109
[6,] -1.762 -3.273 -0.250
[7,] -1.760 -3.244 -0.277
[8,] -0.146 -1.382 1.091
[9,] -0.144 -1.359 1.070
[10,] 0.001 -1.206 1.209
csimtest(par.week, df=denDF.week, covm=vc.week,cmatrix=CM)
Simultaneous tests: user-defined contrasts
Contrast matrix:
[,1] [,2] [,3] [,4] [,5]
[1,] -1 1 0 0 0
[2,] -1 0 1 0 0
[3,] -1 0 0 1 0
[4,] -1 0 0 0 1
[5,] 0 -1 1 0 0
[6,] 0 -1 0 1 0
[7,] 0 -1 0 0 1
[8,] 0 0 -1 1 0
[9,] 0 0 -1 0 1
[10,] 0 0 0 -1 1
Adjusted P-Values
p adj
[1,] 0.011
[2,] 0.013
[3,] 0.014
[4,] 0.015
[5,] 0.020
[6,] 0.024
[7,] 0.985
[8,] 0.985
[9,] 0.985
[10,] 0.997
R version 2.2.1, 2005-12-20, i386-pc-mingw32
attached base packages:
[1] "methods" "stats" "graphics" "grDevices" "utils"
"datasets"
[7] "base"
other attached packages:
multcomp mvtnorm MASS statmod nlme
"0.4-8" "0.7-2" "7.2-24" "1.2.4" "3.1-68.1"
If this does NOT answer your question (or even if it does),