Permutation test for linear mixed-effects models — grouped_perm

grouped_perm_glmm is a nonparametric test for linear mixed-effects models. It assesses the significance of fixed-effect coefficients by permutation, computing the null distribution of the test statistic by randomly rearranging the outcome variable while preserving the random-effects structure.

Usage

grouped_perm_glmm(tbl, formla, var_to_perm, permNum = 1000, seed = 42)

Arguments

tbl: A data frame or tibble containing all model variables.
formla: A formula with both fixed- and random-effects parts; passed directly to lme4::lmer().
var_to_perm: Character. Name of the column to permute (typically the outcome variable).
permNum: Integer. Number of permutations to generate. Default 1000.
seed: Integer. Random seed for reproducibility; passed to base::set.seed(). Default 42.

Value

A tibble with one row per fixed-effect term and columns:

term: Name of the regression term.
effect: Always "fixed" (random-parameter rows are dropped).
estimate: Estimated coefficient.
statistic: Observed t-statistic.
p.perm: Permutation p-value: proportion of permuted |statistics| >= |observed statistic|. Replaces the conventional p.value because lme4::lmer does not compute degrees of freedom or p-values by default. A value of 0 means no permuted statistic was as extreme; report as p < 1/permNum.

Examples

if (FALSE) { # \dontrun{
library(sdamr)
data("anchoring")
grouped_perm_glmm(
  anchoring,
  everest_feet ~ anchor + sex + (1 | referrer),
  "everest_feet"
)
} # }