Good afternoon, gentlemen! After several days studying and researching 
on categorical data (various forums with answers from the owner of the 
library - all incipient) how to interpret the output the function 
MCMCglmm, come to enlist the help of you, if someone has already worked 
with MCMCglmm function in the case of variables ordinal dependent. I've 
read and reread all the pdf's of the package, the coursenotes Jarrod, 
finally, I'm exhausted. To clarify the database, the treatment (called 
fases) consist of three levels (1-pre, 2-propolis and 3-vincris) and the ordinal variable response has three categories (1-normal, 2-agudo, 3 - 
cronico). See table! 

du <- transform(read.table('http://dl.dropbox.com/u/33619290/Dados/Dtest.txt',h=T),FASES=factor(FASES),ALT.RENAIS=ordered(ALT.RENAIS)) 
summary(du) 
library('MCMCglmm') 
du <- subset(du, ALT.RENAIS != 'NA')                                   

tabela <- table(du[,c(2,4)]) 
tabela 
colnames(tabela) <- c('Normal','Aguda','Crônica') 
rownames(tabela) <- c('Pre','Propolis','Vincr') 
tabela 

#the mixed model: 
set.seed(1) 
mod1 <- MCMCglmm(ALT.RENAIS ~-1+FASES, random= ~ ANIMAIS, 
    family='ordinal',pl=TRUE,data=du) 
summary(mod1) 

Then the pain starts, since the documentation is insufficient in
 this case. According to him Jarrod (forums), the a posteriori means of 
the coefficients of the covariates are the probit scale. According to my
 study, these coefficients are the scores of standard normal 
distribution. More scores should not correspond to cutpoints? In this 
case, we would have j (response variable categories) -1 cutpoints, ie, 
two cutpoints. The output shows me only one cutpoint. How can then 
calculate the probabilities with only one cutpoint? According to the 
documentation (Vignettes, page 22), if P (y = k) = F (yk | l (vlatente),
 1) - F (yk-1 | l, 1), this '1' would probably be the category '1' of 
the dependent variable? Anyway gentlemen, how can I extract the 
probabilities for the stages for each category of the dependent 
variable? I thank everyone's attention. 

#Absurd results! 
latentv <- mean(mod1$Liab) 
cutpoint <- mean(mod1$CP) 

pnorm(-(latentv), 0, sqrt(2)) 
pnorm(cutpoint - (latentv),0, sqrt(2)) - pnorm((latentv),0, sqrt(2)) 
1- pnorm(cutpoint - (latentv),0, sqrt(2)) 

#this would have a logical outcome to some extent, more would 
just be referring to category '1' of the dependent variable. And the 
other? 
bre <- c(mean(mod1$Liab),mean(mod1$Sol[,1]),mean(mod1$Sol[,2]),mean(mod1$Sol[,3])) 
pnorm(bre[2])-pnorm(bre[1]) 
pnorm(bre[3])-pnorm(bre[2]) 
pnorm(bre[4])-pnorm(bre[3])# negative probability? 
 

\begin{signature}
<<>>=
Prof. Dr. Ivan Bezerra Allaman
Universidade Estadual de Santa Cruz
Departamento de Cięncias Exatas e Tecnológicas
Ilhéus/BA - Brasil
Fone: +55 73 3680-5076
E-mail: ivanalaman@yahoo.com.br/ivanalaman@gmail.com
@
\end{signature}
	[[alternative HTML version deleted]]


