# Solved – GLM with a Poisson distribution, how to conduct a post-hoc test

I have a dataset in which I compare the total number (the summed number of birds over a couple of weeks) of birds across different distances (continuous factor). I hypothesize that the numbers of birds is the highest at the first distance and declines. The model I used was a GLM.

I now have a significant effect of the Distance, which is what I was expecting. But what can I use to see which of the distances differ from each other? I've tried this but that didn't work.

``post1 <- glht(m5, family = poisson()) summary(m5, test = adjusted()) ``

The emmeans package does not work.

This question can be deleted, the answer did not work and I did not conduct a post-hoc.

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You want the `emmeans` package. It is remarkably broad and effective. Below, I simulate data that might look like yours. Then I calculate all pairwise comparisons and then just the contrast of interest that you are interested in.
``# generate fake data that might look like what you want ------------------------ set.seed(1839) # for replicability n <- 400 # sample size Distance <- factor(sample(1:5, n, TRUE)) # make distance from 1 to 5 and factor # make rain and method covariates that are unrelated to outcome:  Rain <- rbeta(400, 4, 2) Method <- rbinom(n, 1, .4) # calculate number that distance 1 is higher than the other 4: Number <- rpois(n, ifelse(Distance == "1", 4, 2))  # fit model -------------------------------------------------------------------- model <- glm(Number ~ Distance + Rain + Method, family = "poisson")  # use emmeans ------------------------------------------------------------------ library(emmeans) # all post-hoc comparisons: pairs(emmeans(model, ~ Distance))  # just the contrast you wanted: contrast(emmeans(model, ~ Distance), method = "trt.vs.ctrl", ref = 1) # see ?contrast-methods for more information on types of methods of contrasts # as well as how to specify the reference (that is, it should be the level value # and not the label) ``
There are tons of great vignettes to help learn `emmeans`, e.g.: