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Hi all, In my continuous transition of GLIM to R I try to make a glm with binomial errors. The data file have 3 vectors: h -> the factor that is ajusted (have 3 levels) d -> number of animais alive (the response) n -> total number of animals To test proportion of alive, make d/n. In GLIM: $yvar d$ $error binomial n$ $fit +h$ scale deviance = 25.730 (change = -9.138) at cycle 4 d.f. = 15 (change = -2) factor h is significant by chisq? with 2df fron tables = 5.99...

...tabela <- table(du[,c(2,4)]) tabela colnames(tabela) <- c('Normal','Aguda','Cr?nica') 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 ar...

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