cv.glmreg_fit: 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, lambda=NULL, balance=TRUE, 
family=c("gaussian", "binomial", "poisson", "negbin"), 
nfolds=10, foldid, plot.it=TRUE, se=TRUE, trace=FALSE, ...)

Arguments

x matrix as in glmreg.

response y as in glmreg.

weights

Observation weights; defaults to 1 per observation

lambda

Optional user-supplied lambda sequence; default is NULL, and glmreg chooses its own sequence

balance

for family="binomial" only

family

response variable distribution

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.

a logical value, to plot with standard errors.

trace

if TRUE, shows cross-validation progress

...

Other arguments that can be passed to glmreg.

Value

an object of class "cv.glmreg" is returned, which is a list with the ingredients of the cross-validation fit.
fita fitted glmreg object for the full data.
residmatmatrix of log-likelihood values with row values for lambda and column values for kth cross-validation
bicmatrix of BIC values with row values for lambda and column values for kth cross-validation
cvThe mean cross-validated log-likelihood values - a vector of length length(lambda).
cv.errorestimate of standard error of cv.
foldidan optional vector of values between 1 and nfold identifying what fold each observation is in.
fractiona vector of lambda values with length of lambda
lambda.whichindex of lambda that gives maximum cv value.
lambda.optimvalue of lambda that gives maximum cv value.

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.

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]