Does k-fold cross-validation for nclreg, produces a plot,
and returns cross-validated loss values for lambda
# S3 method for formula
cv.nclreg(formula, data, weights, offset=NULL, ...)
# S3 method for matrix
cv.nclreg(x, y, weights, offset=NULL, ...)
# S3 method for default
cv.nclreg(x, ...)
# S3 method for cv.nclreg
plot(x,se=TRUE,ylab=NULL, main=NULL, width=0.02, col="darkgrey", ...)
# S3 method for cv.nclreg
coef(object,which=object$lambda.which, ...)an object of class "cv.nclreg" is returned, which is a
list with the ingredients of the cross-validation fit.
a fitted nclreg object for the full data.
matrix of loss values with row values for lambda and column values for kth cross-validation
matrix of BIC values with row values for lambda and column values for kth cross-validation
The mean cross-validated loss values - a vector of length
length(lambda).
estimate of standard error of cv.
an optional vector of values between 1 and nfold
identifying what fold each observation is in.
a vector of lambda values
index of lambda that gives minimum cv value.
value of lambda that gives minimum cv value.
symbolic description of the model, see details.
argument controlling formula processing
via model.frame.
x matrix as in nclreg. It could be object of cv.nclreg.
response y as in nclreg.
Observation weights; defaults to 1 per observation
Not implemented yet
object of cv.nclreg
Indices of the penalty parameter lambda at which
estimates are extracted. By default, the one which generates the optimal cross-validation value.
logical value, if TRUE, standard error curve is also plotted
ylab on y-axis
title of plot
width of lines
color of standard error curve
Other arguments that can be passed to nclreg.
Zhu Wang <zwang145@uthsc.edu>
The function runs nclreg nfolds+1 times; the
first to compute the lambda sequence, and then to
compute the fit with each of the folds omitted. The error or the loss value is
accumulated, and the average value and standard deviation over the
folds is computed. Note that cv.nclreg can be used to search for
values for alpha: it is required to call cv.nclreg with a fixed vector foldid for different values of alpha.
Zhu Wang (2021), MM for Penalized Estimation, TEST, tools:::Rd_expr_doi("10.1007/s11749-021-00770-2")