cv.glmregNB {mpath}

Cross-validation for glmregNB

Description

Does k-fold cross-validation for glmregNB, produces a plot, and returns cross-validated log-likelihood values for lambda

Usage

cv.glmregNB(formula, data, weights, offset=NULL, lambda=NULL, nfolds=10, 
            foldid, plot.it=TRUE, se=TRUE, n.cores=2, trace=FALSE, 
            parallel=FALSE, ...)

Arguments

formula

symbolic description of the model

data

arguments controlling formula processing via model.frame.

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 glmregNB chooses its own sequence

nfolds

number of folds - default is 10. Although nfolds can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is nfolds=3

foldid

an optional vector of values between 1 and nfold identifying what fold each observation is in. If supplied, nfold can be missing.

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 glmregNB.

Details

The function runs glmregNB nfolds+1 times; the first to get the lambda sequence, and then the remainder to compute the fit with each of the folds omitted. The error is accumulated, and the average error and standard deviation over the folds is computed. Note that cv.glmregNB does NOT search for values for alpha. A specific value should be supplied, else alpha=1 is assumed by default. If users would like to cross-validate alpha as well, they should call cv.glmregNB with a pre-computed vector foldid, and then use this same fold vector in separate calls to cv.glmregNB with different values of alpha.

Value

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

fit

a fitted glmregNB object for the full data.

residmat

matrix of log-likelihood values with row values for lambda and column values for kth cross-validation

cv

The mean cross-validated log-likelihood values - a vector of length length(lambda).

cv.error

The standard error of cross-validated log-likelihood values - a vector of length length(lambda).

lambda

a vector of lambda values

foldid

indicators of data used in each cross-validation, for reproductive purposes

lambda.which

index of lambda that gives maximum cv value.

lambda.optim

value of lambda that gives maximum cv value.

Author(s)

Zhu Wang <[e-mail]>

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]

See Also

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

Examples

## Not run: 
data("bioChemists", package = "pscl")
fm_nb <- cv.glmregNB(art ~ ., data = bioChemists)
plot(fm_nb)

## End(Not run)