logLik.lm.rrpp returns the log-likelihood of
an lm.rrpp object or an lmm.rrpp object.
Ridge regularization will be performed for
ill-conditioned or singular residual covariance matrices, but dimension
reduction could be augmented via projection, using the arguments, tol
and pc.no. See ordinate for details.
# S3 method for lm.rrpp
logLik(
object,
tol = NULL,
pc.no = NULL,
Z = TRUE,
verbose = FALSE,
gls.null = FALSE,
...
)Object from lm.rrpp
A value indicating the magnitude below which
components should be omitted, following projection. See ordinate
for details.
Optionally, a number specifying the maximal number of
principal components, passed onto ordinate, as argument, rank.
A logical value for whether to calculate Z scores based on RRPP.
A logical value for whether to return random log-likelihood values, if Z-scores are calculated.
A logical value for if a fit has a GLS estimation, should the null model (intercept) also have a GLS estimation, for estimating Z. Should be FALSE if the log-likelihood is measured to compare different GLS estimations for a covariance matrices
further arguments passed to or from other methods
Michael Collyer
Note that options are limited for lmm.rrpp objects. It is currently
not possible to use RRPP to find Z scores.