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When to use generalized LMM or generalized GLMM?
If you measured emergence as a continuous measure I would start with a mixed-effects model, but if the measure is dichotomous (e.g., yes or no) then generalized LMM is the way to start. I will assume your response variable is continuous. I would use the lmer () function from the lme4 package in R. Your base model could look something like this:
Which is the best generalized linear mixed model?
Generalized linear models (GLM) are for non-normal data and only model fixed effects. SAS procedures logistic, genmod1 and others fit these models. Generalized linear mixed models (GLMM) are for normal or non-normal data and can model random and / or repeated effects. The glimmix procedure fits these models.
How to plot A binomial GLMM in R?
How to plot (in R) a binomial GLMM with a proportional response variable [analyzed using cbind (Successes, Failures)], and a continuous fixed factor? Got a technical question? Get high-quality answers from experts.
How to run a glmer with paired data and repeated measures?
A site consist of 1 exclosure + 1 control plot. There are 15 sites (so 15 exclosures + 15 paired plots = 30 experimental units). Each experimental units has is unique “id”. The abundance (proportion : continuous value between 0 and 1) of fir has been monitored for 5 years (once every year = repeated measures).
How to get p-values from lmer function?
The lmer function from the lme4 package has a syntax like lm. Add something like + (1|subject) to the model for the random subject effect. To get p-values, use the car package.
When do you use repeated measures of day?
If so, a simpler approach may be to use the repeated measures of Day to calculate one response variable, such as rate of emergence or total emergence. Then, a simple anova will tell you if the Treatment type affects the rate of emergence, total emergence, or what have you.