stabilise_re_glmer: stabilise_re_glmer

In stabiliser: Stabilising Variable Selection

stabilise_re_glmer

Description

Function to calculate stability of variables' association with an outcome for a given model over a number of bootstrap repeats using clustered data.

Arguments

data	A dataframe containing an outcome variable to be permuted.
outcome	The outcome as a string (i.e. "y").
intercept_level_ids	A vector names defining which variables are random effect, i.e., c("level_2_column_name", "level_3_column_name").
n_top_filter	The number of variables to filter for final model (Default = 50).
boot_reps	The number of bootstrap samples. Default is "auto" which selects number based on dataframe size. For glmer models, these are subsamples of the dataset, set to 80%.
permutations	The number of times to be permuted per repeat. Default is "auto" which selects number based on dataframe size.
perm_boot_reps	The number of times to repeat each set of permutations. Default is 20.
normalise	Normalise numeric variables (TRUE/FALSE)
dummy	Create dummy variables for factors/characters (TRUE/FALSE)
impute	Impute missing data (TRUE/FALSE)
base_id	level of the random effect to bootstrap by, e.g individual. This is likely the lower level of random effect specified
parallel	TRUE or FALSE, whether to set up parallel processing
num_cores	Number of cores to use if parallel processing required

Value

A list containing a table of variable stabilities and a numeric permutation threshold.

stabiliser documentation built on March 9, 2026, 5:08 p.m.