R: Internal function of cross-validation for irglmreg

cv.irglmreg_fit {mpath}

R Documentation

Internal function of cross-validation for irglmreg

Description

Internal function to conduct k-fold cross-validation for irglmreg, produces a plot, and returns cross-validated loss values for lambda

Usage

cv.irglmreg_fit(x, y, weights, offset, lambda=NULL, balance=TRUE, cfun=4, dfun=1, 
                s=1.5, nfolds=10, foldid, type = c("loss", "error"), plot.it=TRUE,
                se=TRUE, n.cores=2, trace=FALSE, parallel=FALSE, ...)

Arguments

`x`	`x` matrix as in `irglmreg`.
`y`	response `y` as in `irglmreg`.
`weights`	Observation weights; defaults to 1 per observation
`offset`	this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector of length equal to the number of cases. Currently only one offset term can be included in the formula.
`lambda`	Optional user-supplied lambda sequence; default is `NULL`, and `irglmreg` chooses its own sequence
`balance`	for `dfun=4, 5, 6` only
`cfun`	a number from 1 to 7, type of convex cap (concave) function
`dfun`	a number from 1, 4-7, type of convex downward function
`s`	nonconvex loss tuning parameter for robust regression and classification.
`nfolds`	number of folds >=3, default is 10
`foldid`	an optional vector of values between 1 and `nfold` identifying what fold each observation is in. If supplied, `nfold` can be missing and will be ignored.
`type`	cross-validation criteria. For `type="loss"`, loss function values and `type="error"` is misclassification error.
`plot.it`	a logical value, to plot the estimated log-likelihood values if `TRUE`.
`se`	a logical value, to plot with standard errors.
`n.cores`	The number of CPU cores to use. The cross-validation loop will attempt to send different CV folds off to different cores.
`trace`	a logical value, print progress of cross validation or not
`parallel`	a logical value, parallel computing or not
`...`	Other arguments that can be passed to `irglmreg`.

Details

The function runs irglmreg 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 log-likelihood value is accumulated, and the average value and standard deviation over the folds is computed. Note that cv.irglmreg can be used to search for values for alpha: it is required to call cv.irglmreg with a fixed vector foldid for different values of alpha.

Value

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

`fit`	a fitted irglmreg object for the full data.
`residmat`	matrix of loss values or errors with row values for `lambda` and column values for `k`th cross-validation
`cv`	The mean cross-validated loss values or errors - 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 (2024) Unified Robust Estimation, Australian & New Zealand Journal of Statistics. 66(1):77-102.