What package is LMER in R?

lme4 package
Maximum likelihood or restricted maximum likelihood (REML) estimates of the pa- rameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R.

What package is lme4?

the Eigen package
lme4 uses modern, efficient linear algebra methods as implemented in the Eigen package, and uses reference classes to avoid undue copying of large objects; it is therefore likely to be faster and more memory-efficient than nlme.

Does LMER give P values?

A linear mixed model analyses using lmer will automatically include p values computed via the Satterthwaite approximation.

What is a LMER model?

a two-sided linear formula object describing both the fixed-effects and random-effects part of the model, with the response on the left of a ~ operator and the terms, separated by + operators, on the right.

What is a Glmer model?

Details. Fit a generalized linear mixed model, which incorporates both fixed-effects parameters and random effects in a linear predictor, via maximum likelihood. The linear predictor is related to the conditional mean of the response through the inverse link function defined in the GLM family .

Is lower Reml better?

It says, “The REML likelihood depends on which fixed effects are in the model, and so are not comparable if the fixed effects change. REML is generally considered to give better estimates for the random effects, though, so the usual advice is to fit your best model using REML for your final inference and reporting.”

What is numDF and denDF?

I am having a difficult time understanding the *meaning* of the numDF (degrees of freedom in the numerator), denDF (DF in the denomenator), as well as the Intercept term in the output.

How do you find the p value in LMER?

Three ways to get parameter-specific p-values from lmer

1. load(“Examples.RData”) require(lme4) # fit the model. m. sem <- lmer(Semantic.
2. require(lmerTest) # re-fit model. m. semTest <- lmer(Semantic.
3. require(pbkrtest) # get the KR-approximated degrees of freedom. df. KR <- get_ddf_Lb(m.

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.

What is the difference between fixed effects and random effects?

A fixed-effects model supports prediction about only the levels/categories of features used for training. A random-effects model, by contrast, allows predicting something about the population from which the sample is drawn.

What is REML in LMER?

Tldr version: REML is a method for estimating variance components in models with random effects. If all effects are fixed, then using REML makes no sense because the first thing REML does, computationally speaking, is removing all fixed effects and evaluating remaining variance that belongs to random effects.

What is denominator degrees of freedom?

The denominator degrees of freedom is the bottom portion of the F distribution ratio and is often called the degrees of freedom error. You can calculate the denominator degrees of freedom by subtracting the number of sample groups from the total number of samples tested.

What does compound symmetry mean?

Compound Symmetry just means that all the variances are equal and all the covariances are equal. So the same variance and covariance are used for all subjects. If you think this applies to the factors in your ANOVA model, compound symmetry is a good covariance structure to use because of its simple structure.

What is Kenward Roger?

Introduction. The Kenward–Roger (KR) test is widely used for testing linear hypotheses about fixed effects in normal mixed linear models. Following its introduction in 1997 (Kenward and Roger 1997), it has been cited in the literature more than 2500 times according to Google Scholar.

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.

How are variables and data taken in lmer?

By default the variables are taken from the environment from which lmer is called. While data is optional, the package authors strongly recommend its use, especially when later applying methods such as update and drop1 to the fitted model ( such methods are not guaranteed to work properly if data is omitted ).

What is a *lmercontrol?

a list (of correct class, resulting from lmerControl () or glmerControl () respectively) containing control parameters, including the nonlinear optimizer to be used and parameters to be passed through to the nonlinear optimizer, see the *lmerControl documentation for details. a named list of starting values for the parameters in the model.

What is Theta in lmer?

For lmer this can be a numeric vector or a list with one component named “theta”. integer scalar. If > 0 verbose output is generated during the optimization of the parameter estimates.