| da.lmerMod.fit {dominanceanalysis} | R Documentation |
Provides fit indices for hierarchical linear models, based on Nakagawa et al.(2017) and Luo and Azen (2013).
Description
Provides fit indices for hierarchical linear models, based on Nakagawa et al.(2017) and Luo and Azen (2013).
Usage
da.lmerMod.fit(original.model, null.model, newdata = NULL, ...)
Arguments
original.model |
Original fitted model |
null.model |
needed for HLM models |
newdata |
Data used in update statement |
... |
ignored |
Value
A function described by using-fit-indices description for interface. By default, four indices are provided:
rb.r2.1 |
Amount of Level-1 variance explained by the addition of the predictor. |
rb.r2.2 |
Amount of Level-2 variance explained by the addition of the predictor. |
sb.r2.1 |
Proportional reduction in error of predicting scores at Level 1 |
sb.r2.2 |
Proportional reduction in error of predicting cluster means at Level 2 |
If performance library is available, the two following indices are also available:
n.marg |
Marginal R2 coefficient based on Nakagawa et al. (2017). Considers only the variance of the fixed effects. |
n.cond |
Conditional R2 coefficient based on Nakagawa et al. (2017). Takes both the fixed and random effects into account. |
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. doi:10.3102/1076998612458319
Nakagawa, S., Johnson, P. C. D., and Schielzeth, H. (2017). The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of The Royal Society Interface, 14(134), 20170213.
See Also
Other fit indices:
da.betareg.fit(),
da.clm.fit(),
da.dynlm.fit(),
da.glm.fit(),
da.lm.fit(),
da.lmWithCov.fit(),
da.mlmWithCov.fit()