R: Cross-validation for nclreg

cv.nclreg {mpath}

R Documentation

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 `k`th cross-validation
`bic`	matrix of BIC values with row values for `lambda` and column values for `k`th 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 <zwang145@uthsc.edu>

References

Zhu Wang (2021), MM for Penalized Estimation, TEST, doi: 10.1007/s11749-021-00770-2