Re: [R] post-hoc comparisons following glmm
Dear Mr Graves,
this seems work on my data. Thank you very much for your help.
Sincerely
Michael Coeurdassier
Spencer Graves a écrit :
> 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
> > anova(fit)
> numDF denDF F-value p-value
> factor(week) 5 166 10.821682 <.0001
> trt 248 1.889473 0.1622
> > (denDF.week <- anova(fit)$denDF[1])
> [1] 166
> > (denDF.week <- anova(fit)$denDF[1])
> [1] 166
> > (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)
> + }
> > CM
> [,1] [,2] [,3] [,4] [,5]
> [1,] -11000
> [2,] -10100
> [3,] -10010
> [4,] -10001
> [5,]0 -1100
> [6,]0 -1010
> [7,]0 -1001
> [8,]00 -110
> [9,]00 -101
> [10,]000 -11
> > 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,] -11000
> [2,] -10100
> [3,] -10010
> [4,] -10001
> [5,]0 -1100
> [6,]0 -1010
> [7,]0 -1001
> [8,]00 -110
> [9,]00 -101
> [10,]000 -11
>
> 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
> > sessionInfo()
> 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:
> multcompmvtnorm MASSstatmod 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),
> PLEASE do read the posting guide!
> "www.R-project.org/posting-guide.html". I'd prefer not to have to
> guess whether you would think the example I chose was relevant.
>
> hope this helps,
> spencer graves
>
> Michaël Coeurdassier wrote:
>
>> Dear R community,
>>
>> I performed a generalized linear mixed model using glmmPQL (MASS
>> library) to analyse my data i.e : y is the response with a poisson
>> distribution, t and Trait are the independent variables which are
>> continuous and categorical (3 categories C, M and F) respectively,
>> ind is the random variable.
>>
>> mydata<-glmmP
Re: [R] post-hoc comparisons following glmm
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
> anova(fit)
numDF denDF F-value p-value
factor(week) 5 166 10.821682 <.0001
trt 248 1.889473 0.1622
> (denDF.week <- anova(fit)$denDF[1])
[1] 166
> (denDF.week <- anova(fit)$denDF[1])
[1] 166
> (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)
+ }
> CM
[,1] [,2] [,3] [,4] [,5]
[1,] -11000
[2,] -10100
[3,] -10010
[4,] -10001
[5,]0 -1100
[6,]0 -1010
[7,]0 -1001
[8,]00 -110
[9,]00 -101
[10,]000 -11
> 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,] -11000
[2,] -10100
[3,] -10010
[4,] -10001
[5,]0 -1100
[6,]0 -1010
[7,]0 -1001
[8,]00 -110
[9,]00 -101
[10,]000 -11
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
> sessionInfo()
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:
multcompmvtnorm MASSstatmod 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), PLEASE do
read the posting guide! "www.R-project.org/posting-guide.html". I'd
prefer not to have to guess whether you would think the example I chose
was relevant.
hope this helps,
spencer graves
Michaël Coeurdassier wrote:
> Dear R community,
>
> I performed a generalized linear mixed model using glmmPQL (MASS
> library) to analyse my data i.e : y is the response with a poisson
> distribution, t and Trait are the independent variables which are
> continuous and categorical (3 categories C, M and F) respectively, ind
> is the random variable.
>
> mydata<-glmmPQL(y~t+Trait,random=~1|ind,family=poisson,data=tab)
> Do you think it is OK?
>
> Trait is significant (p < 0.0001) and I would like to perform post-hoc
> comparisons to check where the difference among Trait categories but
> I did not find a solution in R help list or others.
>
> Thank you in advance for your help
>
> M
[R] post-hoc comparisons following glmm
Dear R community, I performed a generalized linear mixed model using glmmPQL (MASS library) to analyse my data i.e : y is the response with a poisson distribution, t and Trait are the independent variables which are continuous and categorical (3 categories C, M and F) respectively, ind is the random variable. mydata<-glmmPQL(y~t+Trait,random=~1|ind,family=poisson,data=tab) Do you think it is OK? Trait is significant (p < 0.0001) and I would like to perform post-hoc comparisons to check where the difference among Trait categories but I did not find a solution in R help list or others. Thank you in advance for your help Michael __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
