Maximum likelihood estimation of the ridge parameter by crossvalidation
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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 loglikelihood 
.
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, 340349.
manylm
1 2 3 4 5  data(spider)
spiddat < mvabund(spider$abund)
X < spider$x
ridgeParamEst(dat = spiddat, X = model.matrix(spiddat~X))

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