rePCA: PCA of random-effects covariance matrix
In lme4: Linear Mixed-Effects Models using 'Eigen' and S4
rePCA R Documentation
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
- \insertRef
bates2015parsimoniouslme4
See Also
isSingular
Examples
fm1 <- lmer(Reaction~Days+(Days|Subject), sleepstudy)
rePCA(fm1)
lme4 documentation built on March 6, 2026, 1:07 a.m.
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| rePCA | R Documentation |
PCA of random-effects covariance matrix
Description
PCA of random-effects variance-covariance estimates
Usage
rePCA(x)
Arguments
x |
a |
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
- \insertRef
bates2015parsimoniouslme4
See Also
isSingular
Examples
fm1 <- lmer(Reaction~Days+(Days|Subject), sleepstudy)
rePCA(fm1)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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