Why does the interaction term remain unchanged after adding a covariate in my mixed-effects model?
My data is structured as follows:
Issues with ‘boundary (singular) fit ‘ when fitting a Mixed Effect Negative Binomial model
I am running a linear mixed models for a study about scRNAseq with the variable leiden( meaning different clusters) as a random variable. The thing is that I got what is called ‘boundary (singular) fit ‘: my random variable has a variance and Std.Dev. close to 0
Mixed Effect Model using lme4 in R
I have GPS collar data on a species of gazelle throughout different seasons and want to model the effect of seasonal climatic changes on their movement patterns (e.g. daily distance moved, and monthly home range sizes) using the lme4 package in R.
How to Implement a Mixed Effect Model for Nested Data in R?
Data sample below. I’m working on an analysis involving a complex nested dataset and I need to implement a mixed effect model in R. Here’s a brief overview of my situation: Objective: Determine the effect of personality traits (OCEAN), emotional states, situational perception, and emotional perception on the evaluation of valence of images generated by […]
issues with lme4 package for Mixed-effects modeling
I am trying to use the lmer() function R to perform a mixed effects analysis on phyloseq data. This is my d
ataframe. I keep getting the error message
Why does lmer show coefficents for both levels of factor variable for first fixed effect?
I am trying to fit a linear mixed model to my data in R using lme4, but I’m new to lmer / mixed models in general and have trouble with the output.
There are two issues: