How to run post hoc with GLMM-cross validated?
I’ve run a GLMM with Gamma distribution and found a statistically significant effect of both of my independent variables. How do I run post hoc tests to examine these effects further? The design is: I have two factors stimulation and temperature. The outcome is time until a response. I found a statistically significant effect for both factors.
Which is post hoc test for your for glmer?
I’m analysing my binomial dataset with R using a generalized linear mixed model (glmer, lme4-package). I wanted to make the pairwise comparisons of a certain fixed effect (“Sound”) using a Tukey’s post-hoc test (glht, multcomp-package).
Is there an alternative to post hoc testing?
A theoretically sound alternative to testing, and post-hoc testing, is to just look at the 95% confidence intervals for effects and comment on their direction and magnitude. You can even plot the predicted distribution of wait-times for multiple exposure levels and comment on the their shapes and scales.
What does 95% CI mean in cross validated?
Conversely, showing that the 95% CI just barely misses the “null” threshold appropriately shows readers that, while statistically significant, the results are consistent with a mild to low range of effects and the study lacks power. Thanks for contributing an answer to Cross Validated!
Which is the best post hoc summary function?
The summary function is not the best method to get post-hoc results. It is better to use something made for the task, like the emmeans package. The following is a toy example. It uses the glm.nb function from the MASS package. MASS::glm.nb is supported by emmeans. I don’t know if pscl::glm.nb would work as well.
What is the function glm.nb in PSCL?
I am running a GLM, using the function glm.nb (pscl package) trying to figure how what could influence a particular trait in several locations and years. The output as follow (with slight modification and removing the things beyond this question)