This uses the lmerTest package to get the p-values, and fits a model with assumed homoskedasticity, etc. I.e., this is the vanilla MLM that one would typically fit.
Usage
MLM_estimators(
formula,
data = NULL,
control_formula = NULL,
suppress_warnings = TRUE,
include_disfavored = FALSE
)Arguments
- formula
Formula for outcome and treatment and nesting. If NULL, data is assumed to be in canonical form (see vignette for further discussion).
- data
Data frame holding the outcome, treatment, and clustering/blocking columns referenced by `formula` (or already in canonical form if `formula` is NULL).
- control_formula
What variables to control for, in the form of "~ X1 + X2".
- suppress_warnings
If TRUE, suppress the (typically benign) convergence/singularity warnings `lmer()` throws.
- include_disfavored
Include the full random-intercept- and random-slope-by-block (RIRC/FIRC) MLM variants, which are considered disfavored due to instability with few blocks.