*! version 2.1.0 August 14, 2023 J. N. Luchman
help mixdom

Title

Wrapper program for domin to conduct linear mixed effects regression dominance analysis

Syntax

mixdom depvar indepvars if [weight] ,
id(idvar) [reopt(re_options) mopt(mixed_options)]

pweights and fweights are allowed (see help weights). Factor and
time series variables are allowed.

Description

mixdom sets the data up in a way to allow for the dominance analysis of a linear mixed effects regression by utilizing mixed.
The method outlined here follows that for the within- and between-cluster Snijders and Bosker (1994) R2 metric described by Luo and Azen (2013).

mixdom only allows 1 level of clustering in the data (i.e., 1 random effect), which must be the cluster constant/mean/intercept. Luo and
Azen (2013) recommend that even if random coefficients are present in the data, they should be restricted to a fixed effect only in the dominance
analysis.

mixdom is intended for use only as a wrapper program with domin for the dominance analysis of mixed-effects linear regression, and its syntax is designed to conform with domin's expectations.
It is not recommended for use as an estimation command outside of domin.

Note that negative R2 values indicate likely model misspecification.

Options

id() specifies the variable on which clustering occurs and that will appear after the random effects specification (i.e., ||) in the
mixed syntax.

reopt() passes options to mixed specific to the random intercept effect (i.e., pweight() the user would
like to utilize during estimation.

mopt() passes options to mixed that the user would like to utilize during estimation.
This option was named xtmopt() in mixdom versions previous to 2.0.0 and is now defunct.

Saved results

mixdom saves the following results to e():

scalars

e(r2_w) within-cluster R2

e(r2_b) between-cluster R2

macros

e(title) "Mixed-effects ML regression"

functions

e(sample) marks estimation sample

References

Luo, W., & Azen, R. (2013). Determining predictor importance in hierarchical linear models using dominance analysis. Journal of Educational and Behavioral Statistics, 38(1), 3-31.

Snijders, T. A. B., & Bosker, R. J. (1994). Modeled variance in two-level models. Sociological Methods & Research, 22(3), 342-363.

Author

Joseph N. Luchman

Research Fellow

Fors Marsh

Arlington, VA

jluchman@forsmarsh.com

Also see

[R] mixed.