Nov 26, 2023 · In simple terms, how would you explain (perhaps with simple examples) the difference between fixed effect, random effect in mixed effect models?

The following CV questions also discuss this material: Difference between generalized linear models & generalized linear mixed models in SPSS; What is the difference between generalized estimating …

Mar 14, 2019 · In principle you can use a linear mixed model to cover all sorts of random effects: random effects for intercepts (e.g., differences among subjects in baseline HR for condition = time1), …

Nov 27, 2023 · Calculating minimum sample size required for repeated measures linear mixed model using the simr package Ask Question Asked 2 years ago Modified 2 years ago

I use lme4 in R to fit the mixed model lmer (value~status+ (1|experiment))) where value is continuous, status and experiment are factors, and I get Linear mixed model fit by REML Formula: value ~ ...

Apr 24, 2017 · Linear Mixed Effects Models are Extensions of Linear Regression models for data that are collected and summarized in groups. The key advantages is the coefficients can vary with …

Jul 8, 2020 · Linear mixed effects method with "Weight" as outcome, [group, time, group*time] as fixed effects and [subject] as random effect. Again, can we use "Percent reduction in weight" here? Linear …

Mar 12, 2024 · This is obviously not helpful when you also need to determine the explicit parts of the random factors that you want to include in a mixed model, as using a similar equation on it's own will …

I am attempting to fit a mixed effects model using R and lme4, but am new to mixed models. I'd like to model the response as the Treatment + Level 1 Factor (stem, root) + Level 2 Factor (tissue A, tissue …

Nov 17, 2023 · 1002 2 3 8 6 1003 3 2 5 4 1003 3 3 6 4 Y = treatment_group + time + treatment*time + baseline_y Including a baseline covariate in a linear mixed-effects model I've seen numerous …