## What is the difference between LMER and Glmer?

lmer() and glmer() The lmer() (pronounced el-mer) and glmer() functions are used in the examples of this article. The lmer() function is for linear mixed models and the glmer() function is for generalized mixed models.

### What is a LME model?

2.0. The Mathematical Model Defined. As mentioned in Section 1, the LME model is an extention of a basic linear model. Recall the model for a simple linear regression: Yi=β0+β1xi+ϵi.

**What are random effects in a model?**

In econometrics, random effects models are used in panel analysis of hierarchical or panel data when one assumes no fixed effects (it allows for individual effects). A random effects model is a special case of a mixed model.

**What are random effects in mixed models?**

Random effects are simply the extension of the partial pooling technique as a general-purpose statistical model. This enables principled application of the idea to a wide variety of situations, including multiple predictors, mixed continuous and categorical variables, and complex correlation structures.

## Does Glmer use REML?

Glmer() always uses Maximum Likelihood (ML) rather than REstricted Maximum Likelihood (REML) (http://glmm.wikidot.com/faq#reml-glmm).

### What does Glmer stand for?

glmer: Fitting Generalized Linear Mixed-Effects Models.

**What is the function for LME?**

3.2 The lme function This generic function fits a linear mixed-effects model in the formulation described in Laird and Ware (1982) but allowing for nested random effects. The within-group errors are allowed to be correlated and/or have unequal variances.

**Should I use random or fixed effects?**

If the study effect sizes are seen as having been sampled from a distribution of effect sizes, then the random-effects model, which reflects this idea, is the logical one to use. If the between-studies variance is substantial (and statistically significant) then the fixed-effect model is inappropriate.

## Should I use REML or ML?

Recap that, ML estimates for variance has a term 1/n, but the unbiased estimate should be 1/(n−p), where n is the sample size, p is the number of mean parameters. So REML should be used when you are interested in variance estimates and n is not big enough as compared to p.

### What are random terms in LME?

These random terms additively determine the conditional mean of each observation based on its covariate values. The statsmodels implementation of LME is primarily group-based, meaning that random effects must be independently-realized for responses in different groups.

**What are fixed and random effects in LMM?**

As a rule of thumb, i) factors with fewer than 5 levels should be considered fixed and conversely ii) factors with numerous levels should be considered random effects in order to increase the accuracy in the estimation of variance. Both points relate to the LMM assumption of having normally distributed random effects.

**What are the types of random effects in mixed models?**

There are two types of random effects in our implementation of mixed models: (i) random coefficients (possibly vectors) that have an unknown covariance matrix, and (ii) random coefficients that are independent draws from a common univariate distribution.

## What is the difference between random effects and linear models?

Random effects models include only an intercept as the fixed effect and a defined set of random effects. Random effects comprise random intercepts and / or random slopes. Also, random effects might be crossed and nested. In terms of estimation, the classic linear model can be easily solved using the least-squares method.