Does k-fold cross-validation for glmreg, produces a plot,
and returns cross-validated log-likelihood values for lambda
# S3 method for formula
cv.glmreg(formula, data, weights, offset=NULL, contrasts=NULL, ...)
# S3 method for matrix
cv.glmreg(x, y, weights, offset=NULL, ...)
# S3 method for default
cv.glmreg(x, ...)
# S3 method for cv.glmreg
plot(x,se=TRUE,ylab=NULL, main=NULL, width=0.02, col="darkgrey", ...)
# S3 method for cv.glmreg
predict(object, newx, ...)
# S3 method for cv.glmreg
coef(object,which=object$lambda.which, ...)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
matrix of BIC values with row values for lambda and column values for kth cross-validation
The mean cross-validated log-likelihood values - a vector of length
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.
symbolic description of the model, see details.
argument controlling formula processing
via model.frame.
x matrix as in glmreg. It could be object of cv.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.
the contrasts corresponding to levels from the
respective models
object of cv.glmreg
Matrix of values at which predictions are to be made. Not
used for type="coefficients"
Indices of the penalty parameter lambda at which
estimates are extracted. By default, the one which generates the optimal cross-validation value.
logical value, if TRUE, standard error curve is also plotted
ylab on y-axis
title of plot
width of lines
color of standard error curve
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]
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)
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