Internal function to conduct k-fold cross-validation for glmreg, produces a plot,
and returns cross-validated log-likelihood values for lambda
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, ...)an object of class "cv.glmreg" is returned, which is a
list with the ingredients of the cross-validation fit.
a fitted glmreg object for the full data.
matrix of log-likelihood values with row values for lambda and column values for kth cross-validation
The mean cross-validated log-likelihood values - a vector of
length(lambda).
estimate of standard error of cv.
an optional vector of values between 1 and nfold
identifying what fold each observation is in.
a vector of lambda values
index of lambda that gives maximum cv value.
value of lambda that gives maximum cv value.
x matrix as in glmreg.
response y as in glmreg.
Observation weights; defaults to 1 per observation
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.
Optional user-supplied lambda sequence; default is
NULL, and glmreg chooses its own sequence
for family="binomial" only
response variable distribution
cross-validation criteria. For type="loss", loss function (log-negative-likelihood) values and type="error" is misclassification error if family="binomial".
number of folds >=3, default is 10
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.
a logical value, to plot the estimated log-likelihood values if TRUE.
a logical value, to plot with standard errors.
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.
a logical value, print progress of cross validation or not
Other arguments that can be passed to glmreg.
Zhu Wang <zwang145@uthsc.edu>
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.
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]