cv.irglmreg_fit: Internal function of cross-validation for irglmreg

In mpath: Regularized Linear Models

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 kth 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 <[e-mail]>

References

Zhu Wang (2020) Unified Robust Estimation via the COCO, arXiv e-prints, https://arxiv.org/abs/2010.02848

See Also

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

mpath documentation built on Jan. 7, 2023, 1:17 a.m.