da.lmerMod.fit: Provides fit indices for hierarchical linear models, based on...

In clbustos/dominanceAnalysis: Dominance Analysis

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()

clbustos/dominanceAnalysis documentation built on March 8, 2024, 5:22 a.m.