R 2 STATISTICS FOR MIXED MODELS
By M. Kramer. 2005
102 citations recorded by the source corpus.
The R statistic, when used in a regression or ANOVA context, is appealing because it summarizes how well the model explains the data in an easy-tounderstand way. R statistics are also useful to gauge the effect of changing a model. Generalizing R to mixed models is not obvious when there are correlated errors, as might occur if data are georeferenced or result from a designed experiment with blocking. Such an R statistic might refer only to the explanation associated with the independent variables, or might capture the explanatory power of the whole model. In the latter case, one might develop an R statistic from Wald or likelihood ratio statistics, but these can yield different numeric results. Example formulas for these generalizations of R are given. Two simulated data sets, one based on a randomized complete block design and the other with spatially correlated observations, demonstrate increases in R as model complexity increases, the result of modeling the covariance structure of the residuals.
Subfields: Economics and Econometrics, Environmental Engineering, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/7d25e06e3b1ed4064f39255f5946cdcf752d8cfb
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Citations in the corpus
- Quantifying explained variance in multilevel models: An integrative framework for defining R-squared measures. (2019, methodology): “relate and lack of consensus on which measure to use for what purpose both serve as obstacles for their incorporation into standard MLM software (Jaeger et al., 2017; Kramer, 2005).”