Extract and plot variance components from a mixed model

Extracts variance components from a mixed linear model fitted with mixlm::lm() and computes each component's percentage contribution to total variance. Results are returned as both a summary data frame and a horizontal bar chart, making it easy to identify which sources of variance (e.g. person, item, time, interactions) dominate the measurement design.

Usage

extract_components(model, title = "")

Arguments

model: A mixed model object returned by mixlm::lm(). The model must include random effects specified with r() so that variance components are available via mixlm::Anova().
title: Character string used as the plot title. Default is "".

Value

A named list with two elements:

components: A data frame with one row per variance component containing columns component (effect name), VC (estimated variance component), and vc_percent (percentage of total absolute variance explained by that component).
plot: A ggplot horizontal bar chart displaying vc_percent for each component, with a line and points overlaid to show the profile across components.

Examples

design <- expand.grid(time = 1:3, item = 1:3, person = 1:10)
design <- change_data_type(design, type = "factor")
design$response <- rowSums(change_data_type(design[, 1:2], type = "numeric")) +
  rnorm(90, 0, 0.1)
model <- mixlm::lm(response ~ r(time) * r(person) + r(item) * r(person), data = design)
extract_components(model)
#> $components
#>     component         VC vc_percent
#> 1        time  1.0087172   50.53238
#> 2      person -0.0008239    0.04128
#> 3        item  0.9746568   48.82611
#> 4 time:person  0.0013259    0.06642
#> 5 person:item -0.0002685    0.01345
#> 6   Residuals  0.0103874    0.52036
#> 
#> $plot

#>