R: Internal function of cross-validation for glmreg

cv.glmreg_fit {mpath}

R Documentation

Internal function of cross-validation for glmreg

Description

Internal function to conduct k-fold cross-validation for glmreg, produces a plot, and returns cross-validated log-likelihood values for lambda

Usage

cv.glmreg_fit(x, y, weights, offset, lambda=NULL, balance=TRUE, 
              family=c("gaussian", "binomial", "poisson", "negbin"), 
              type=c("loss", "error"), nfolds=10, foldid, plot.it=TRUE,
              se=TRUE, n.cores=2, trace=FALSE, parallel=FALSE, ...)

Arguments

`x`	`x` matrix as in `glmreg`.
`y`	response `y` as in `glmreg`.
`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 `glmreg` chooses its own sequence
`balance`	for `family="binomial"` only
`family`	response variable distribution
`type`	cross-validation criteria. For `type="loss"`, loss function (log-negative-likelihood) values and `type="error"` is misclassification error if `family="binomial"`.
`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.
`plot.it`	a logical value, to plot the estimated log-likelihood values if `TRUE`.
`se`	a logical value, to plot with standard errors.
`parallel`, `n.cores`	a logical value, parallel computing or not with 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
`...`	Other arguments that can be passed to `glmreg`.

Details

The function runs glmreg 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.glmreg can be used to search for values for alpha: it is required to call cv.glmreg with a fixed vector foldid for different values of alpha.

Value

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

`fit`	a fitted glmreg object for the full data.
`residmat`	matrix of log-likelihood values with row values for `lambda` and column values for `k`th cross-validation
`cv`	The mean cross-validated log-likelihood values - a vector of `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 maximum `cv` value.
`lambda.optim`	value of `lambda` that gives maximum `cv` value.

Author(s)

Zhu Wang <zwang145@uthsc.edu>

References

Zhu Wang, Shuangge Ma, Michael Zappitelli, Chirag Parikh, Ching-Yun Wang and Prasad Devarajan (2014) Penalized Count Data Regression with Application to Hospital Stay after Pediatric Cardiac Surgery, Statistical Methods in Medical Research. 2014 Apr 17. [Epub ahead of print]