rePCA: PCA of random-effects covariance matrix

In lme4/lme4: Linear Mixed-Effects Models using 'Eigen' and S4

PCA of random-effects covariance matrix

Description

PCA of random-effects variance-covariance estimates

Usage

rePCA(x)

Arguments

x	a merMod object

Details

Perform a Principal Components Analysis (PCA) of the random-effects variance-covariance estimates from a fitted mixed-effects model. This allows the user to detect and diagnose overfitting problems in the random effects model (see Bates et al. 2015 for details).

Value

a prcomplist object

Author(s)

Douglas Bates

References

• Douglas Bates, Reinhold Kliegl, Shravan Vasishth, and Harald Baayen. Parsimonious Mixed Models. arXiv:1506.04967 [stat], June 2015. arXiv: 1506.04967.

See Also

isSingular

Examples

  fm1 <- lmer(Reaction~Days+(Days|Subject), sleepstudy)
  rePCA(fm1)

lme4/lme4 documentation built on May 4, 2024, 6:46 p.m.