sjstats (version 0.17.1)

re_var: Random effect variances

Description

These functions extracts random effect variances as well as random-intercept-slope-correlation of mixed effects models. Currently, merMod, glmmTMB, stanreg and brmsfit objects are supported.

Usage

re_var(x)
get_re_var(x, comp = c("tau.00", "tau.01", "tau.11", "rho.01",
  "sigma_2"))

Arguments

x

Fitted mixed effects model (of class merMod, glmmTMB, stanreg or brmsfit). get_re_var() also accepts an object of class icc.lme4, as returned by the icc function.

comp

Name of the variance component to be returned. See 'Details'.

Value

get_re_var() returns the value of the requested variance component, re_var() returns all random effects variances.

Details

The random effect variances indicate the between- and within-group variances as well as random-slope variance and random-slope-intercept correlation. Use following values for comp to get the particular variance component:

"sigma_2"

Within-group (residual) variance

"tau.00"

Between-group-variance (variation between individual intercepts and average intercept)

"tau.11"

Random-slope-variance (variation between individual slopes and average slope)

"tau.01"

Random-Intercept-Slope-covariance

"rho.01"

Random-Intercept-Slope-correlation

The within-group-variance is affected by factors at level one, i.e. by the lower-level direct effects. Level two factors (i.e. cross-level direct effects) affect the between-group-variance. Cross-level interaction effects are group-level factors that explain the variance in random slopes (Aguinis et al. 2013).

References

Aguinis H, Gottfredson RK, Culpepper SA. 2013. Best-Practice Recommendations for Estimating Cross-Level Interaction Effects Using Multilevel Modeling. Journal of Management 39(6): 1490<U+2013>1528 (10.1177/0149206313478188)

See Also

icc

Examples

# NOT RUN {
library(lme4)
fit1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)

# all random effect variance components
re_var(fit1)

# just the rand. slope-intercept covariance
get_re_var(fit1, "tau.01")

sleepstudy$mygrp <- sample(1:45, size = 180, replace = TRUE)
fit2 <- lmer(Reaction ~ Days + (1 | mygrp) + (Days | Subject), sleepstudy)
re_var(fit2)

# }