cv.glmreg {mpath}

Cross-validation for glmreg

Description

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

Usage

## S3 method for class 'formula'
cv.glmreg(formula, data, weights, offset=NULL, contrasts=NULL, ...)
## S3 method for class 'matrix'
cv.glmreg(x, y, weights, offset=NULL, ...)
## Default S3 method:
cv.glmreg(x,  ...)
## S3 method for class 'cv.glmreg'
plot(x,se=TRUE,ylab=NULL, main=NULL, width=0.02, col="darkgrey", ...)
## S3 method for class 'cv.glmreg'
predict(object, newx, ...)
## S3 method for class 'cv.glmreg'
coef(object,which=object$lambda.which, ...)

Arguments

formula

symbolic description of the model, see details.

data

argument controlling formula processing via model.frame.

x

x matrix as in glmreg. It could be object of cv.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.

contrasts

the contrasts corresponding to levels from the respective models

object

object of cv.glmreg

newx

Matrix of values at which predictions are to be made. Not used for type="coefficients"

which

Indices of the penalty parameter lambda at which estimates are extracted. By default, the one which generates the optimal cross-validation value.

se

logical value, if TRUE, standard error curve is also plotted

ylab

ylab on y-axis

main

title of plot

width

width of lines

col

color of standard error curve

...

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 kth cross-validation

bic

matrix of BIC 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

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 <[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

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

Examples

data("bioChemists", package = "pscl")
fm_pois <- cv.glmreg(art ~ ., data = bioChemists, family = "poisson")
title("Poisson Family",line=2.5)
predict(fm_pois, newx=bioChemists[,-1])[1:4]
coef(fm_pois)