View source: R/RidgeParamEst.R
ridgeParamEst | R Documentation |
Maximum likelihood estimation of the ridge parameter by cross-validation
ridgeParamEst(dat, X, weights = rep(1,times=nRows), refs, tol=1.0e-010, only.ridge=FALSE, doPlot=FALSE, col="blue",type="l", ...)
dat |
the data matrix. |
X |
the design matrix. |
weights |
weights on the cases of the design matrix. |
refs |
a vector specifying validation group membership. Default is to
construct |
tol |
the sensitivity in calculations near zero. |
only.ridge |
logical, whether only the ridge Parameters should be passed back or additionally the Cross Validation penalised likelihood. |
doPlot |
logical, whether a plot of -2logL vs a candidate for the ridge parameter should be drawn. |
col |
color of Plot symbols. |
type |
type of Plot symbols. |
... |
further plot arguments. |
This function estimates the ridge parameter when applying ridge regularization to a sample correlation matrix of residuals. The ridge parameter is estimated to maximize the normal likelihood as estimated via cross validation (Warton 2008).
A list with the following component:
ridgeParameter |
the estimated ridge parameter |
If only.ridge=FALSE
the returned list additionally contains the element:
minLL |
the minimum of the negative log-likelihood |
.
David Warton <David.Warton@unsw.edu.au> and Ulrike Naumann.
Warton D.I. (2008). Penalized normal likelihood and ridge regularization of correlation and covariance matrices. Journal of the American Statistical Association 103, 340-349.
manylm
data(spider) spiddat <- mvabund(spider$abund) X <- as.matrix(spider$x) ridgeParamEst(dat = spiddat, X = model.matrix(spiddat~X))
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