MLMR2: Stata module to compute r-squared measures for models estimated by mixed

Anthony J. Gambino ()
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Anthony J. Gambino: University of Connecticut

Statistical Software Components from Boston College Department of Economics

Abstract: mlmr2 produces r-squared measures for models estimated by mixed. Using the Rights and Sterba (2019; 2021; 2023b) framework for decomposing the total model-implied outcome variance from a linear mixed model into its sources, mlmr2 computes measures of the proportion of explained variance attributable to each (or combinations) of those sources (e.g., level-1 portion of predictors via fixed slopes). mlmr2 can be used as a postestimation command with any set of estimation results from mixed except for results from cross-classified models and/or models with more than 5 levels. mlmr2 also assesses whether the model's fixed and random effects may be subject to conflation bias. If mlmr2 detects any predictors at risk of having conflated fixed or random effects, the output will contain warning messages detailing which predictors are at risk. These warning messages are to aid the user in avoiding conflation bias in the r-squared measures (Rights, 2023; Rights & Sterba, 2023a).

Language: Stata
Requires: Stata version 17
Keywords: mixed estimation; R-squared; multilevel models (search for similar items in EconPapers)
Date: 2023-07-07, Revised 2023-12-03
Note: This module should be installed from within Stata by typing "ssc install mlmr2". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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