cv.nclreg: Cross-validation for nclreg

In mpath: Regularized Linear Models

Cross-validation for nclreg

Description

Does k-fold cross-validation for nclreg, produces a plot, and returns cross-validated loss values for lambda

Usage

## S3 method for class 'formula'
cv.nclreg(formula, data, weights, offset=NULL, ...)
## S3 method for class 'matrix'
cv.nclreg(x, y, weights, offset=NULL, ...)
## Default S3 method:
cv.nclreg(x,  ...)
## S3 method for class 'cv.nclreg'
plot(x,se=TRUE,ylab=NULL, main=NULL, width=0.02, col="darkgrey", ...)
## S3 method for class 'cv.nclreg'
coef(object,which=object$lambda.which, ...)

Arguments

formula	symbolic description of the model, see details.
data	argument controlling formula processing via model.frame.
x	x matrix as in nclreg. It could be object of cv.nclreg.
y	response y as in nclreg.
weights	Observation weights; defaults to 1 per observation
offset	Not implemented yet
object	object of cv.nclreg
which	Indices of the penalty parameter lambda at which estimates are extracted. By default, the one which generates the optimal cross-validation value.
se	logical value, if TRUE, standard error curve is also plotted
ylab	ylab on y-axis
main	title of plot
width	width of lines
col	color of standard error curve
...	Other arguments that can be passed to nclreg.

Details

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.

Value

an object of class "cv.nclreg" is returned, which is a list with the ingredients of the cross-validation fit.

fit	a fitted nclreg object for the full data.
residmat	matrix of loss values with row values for lambda and column values for kth cross-validation
bic	matrix of BIC values with row values for lambda and column values for kth cross-validation
cv	The mean cross-validated loss values - a vector of length length(lambda).
cv.error	estimate of standard error of cv.
foldid	an optional vector of values between 1 and nfold identifying what fold each observation is in.
lambda	a vector of lambda values
lambda.which	index of lambda that gives minimum cv value.
lambda.optim	value of lambda that gives minimum cv value.

Author(s)

Zhu Wang <[e-mail]>

References

Zhu Wang (2021), MM for Penalized Estimation, TEST, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11749-021-00770-2")}

See Also

nclreg and plot, predict, and coef methods for "cv.nclreg" object.

mpath documentation built on June 28, 2024, 1:06 a.m.