Dear all,

I'm running a model with one fixed factor which has four groups called
"species", and a clustering factor called "nest". My dependent variable
(timeto) is "ttm" (time to moult) which is number of days
perindividual<http://r.789695.n4.nabble.com/parfm-frailty-model-and-post-hoc-testing-td4672712.html#>,
and the Status-variable is called "moulted_final".
The code and its results are as follows.

library(parfm)
> Moult=read.table(file="HSBS R moult2.txt",header=T)
>
modelMoult=parfm(Surv(ttm,moulted_final)~species,cluster="nest",data=Moult,dist="weibull",frailty="possta")

Execution time: 12.72 second(s)
> anova(modelMoult)
Analysis of Deviance Table
Parametric frailty model: response is Surv(ttm, moulted_final)
Terms added sequentially (first to last)

         loglik  Chisq Df Pr(>|Chi|)
NULL    -346.61
species -341.35 10.514  1   0.001184 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

As you can see there are significant differences among species and I would
like to know how to obtain these. I'm used to using linear models in which
post hoc testing gives you pairwise p-values, but I'm not sure if that is
how parfm works.

On a side note, all my samples have moulted so "moulted_final" has the same
state (1) for all samples.


Thanks in advance,
Raoul

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