rePCA

0th

Percentile

PCA of random-effects covariance matrix

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

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

Aliases
  • rePCA
Examples
# NOT RUN {
  fm1 <- lmer(Reaction~Days+(Days|Subject), sleepstudy)
  rePCA(fm1)
# }
Documentation reproduced from package lme4, version 1.1-21, License: GPL (>= 2)

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