PCA of random-effects covariance matrix
rePCA.Rd
PCA of random-effects variance-covariance estimates
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).
References
Douglas Bates, Reinhold Kliegl, Shravan Vasishth, and Harald Baayen. Parsimonious Mixed Models. arXiv:1506.04967 [stat], June 2015. arXiv: 1506.04967.
Examples
fm1 <- lmer(Reaction~Days+(Days|Subject), sleepstudy)
rePCA(fm1)
#> $Subject
#> Standard deviations (1, .., p=2):
#> [1] 0.9668680 0.2308798
#>
#> Rotation (n x k) = (2 x 2):
#> [,1] [,2]
#> [1,] -0.99986158 -0.01663769
#> [2,] -0.01663769 0.99986158
#>
#> attr(,"class")
#> [1] "prcomplist"