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...;- glm( z ~ group, family=binomial(link = "logit")) summary(gmod) # exclude the intercept # Should be the following, but does not work due to a confirmed # bug in the CRAN-binary version 5.10 #summary(csimtest(coef(gmod)[2:3], vcov(gmod)[2:3,2:3], # cmatrix=diag(2), df=27,asympt=T)) summary(csimtest(coef(gmod)[2:3], vcov(gmod)[2:3,2:3], cmatrix=diag(2), df=27)) ------- This works and can be extended to to lme, but only gives TWO of the three possible Tukey contrasts. Setting type="Tukey" does not work, as by assigning to contrasts(group) only tw...

I just realized that in the call to `csimint' the argument `asympt=TRUE' is missing since we need to compute the confidence intervals for a glm based on the normal approximation. Torsten --------------------------------------------------------------------- library(multcomp) set.seed(290875) # a factor at three levels group <- factor(c(rep(1,10), rep(2,...

I have run the following logistic regression model: options(contrasts=c("contr.treatment", "contr.poly")) m <- glm(wolf.cross ~ null.cross + feature, family = "binomial") where: wolf.cross = likelihood of wolves crossing a linear feature null.cross = proportion of random paths that crossed a linear feature feature = CATEGORY of linear feature with 5 levels:

I have a question concerning the Wilcoxon signed-rank test, and specifically, which R subroutine I should use for my particular dataset. There are three different commands in R (that I'm aware of) that calculate the Wilcoxon signed-rank test; wilcox.test, wilcox.exact, and wilcoxsign_test. When I run the three commands on the same dataset, I get different p-values. I'm hoping that