Summary information from random effects — random

This function extracts the different variance components of a mixed model and returns the result as a data frame.

Usage

random_parameters(model, component = "conditional")

Arguments

model: A mixed effects model (including stanreg models).
component: Should all parameters, parameters for the conditional model, for the zero-inflation part of the model, or the dispersion model be returned? Applies to models with zero-inflation and/or dispersion component. component may be one of "conditional", "zi", "zero-inflated", "dispersion" or "all" (default). May be abbreviated.

Value

A data frame with random effects statistics for the variance components, including number of levels per random effect group, as well as complete observations in the model.

Details

The variance components are obtained from insight::get_variance() and are denoted as following:

Within-group (or residual) variance

The residual variance, σ²_ε, is the sum of the distribution-specific variance and the variance due to additive dispersion. It indicates the within-group variance.

Between-group random intercept variance

The random intercept variance, or between-group variance for the intercept (τ₀₀), is obtained from VarCorr(). It indicates how much groups or subjects differ from each other.

Between-group random slope variance

The random slope variance, or between-group variance for the slopes (τ₁₁) is obtained from VarCorr(). This measure is only available for mixed models with random slopes. It indicates how much groups or subjects differ from each other according to their slopes.

Random slope-intercept correlation

The random slope-intercept correlation (ρ₀₁) is obtained from VarCorr(). This measure is only available for mixed models with random intercepts and slopes.

Note: For the within-group and between-group variance, variance and standard deviations (which are simply the square root of the variance) are shown.

Examples

if (require("lme4")) {
  data(sleepstudy)
  model <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
  random_parameters(model)
}
#> # Random Effects
#> 
#> Within-Group Variance          654.94 (25.59)
#> Between-Group Variance
#>   Random Intercept (Subject)    612.1 (24.74)
#>   Random Slope (Subject.Days)   35.07  (5.92)
#> Correlations
#>   Subject.Days                   0.07
#> N (groups per factor)
#>   Subject                          18
#> Observations                      180