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cv.irglmreg: Cross-validation for irglmreg

Description

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

Usage

# S3 method for formula
cv.irglmreg(formula, data, weights, offset=NULL, ...)
# S3 method for matrix
cv.irglmreg(x, y, weights, offset=NULL, ...)
# S3 method for default
cv.irglmreg(x,  ...)
# S3 method for cv.irglmreg
plot(x,se=TRUE,ylab=NULL, main=NULL, width=0.02, col="darkgrey", ...)
# S3 method for cv.irglmreg
coef(object,which=object$lambda.which, ...)

Value

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

fit

a fitted irglmreg 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 minimum cv value.

lambda.optim

value of lambda that gives minimum cv value.

Arguments

formula

symbolic description of the model, see details.

data

argument controlling formula processing via model.frame.

x

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

y

response y as in irglmreg.

weights

Observation weights; defaults to 1 per observation

offset

Not implemented yet

object

object of cv.irglmreg

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 irglmreg.

Author

Zhu Wang <zwang145@uthsc.edu>

Details

The function runs irglmreg 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 loss value is accumulated, and the average value and standard deviation over the folds is computed. Note that cv.irglmreg can be used to search for values for alpha: it is required to call cv.irglmreg with a fixed vector foldid for different values of alpha.

References

Zhu Wang (2024) Unified Robust Estimation, Australian & New Zealand Journal of Statistics. 66(1):77-102.

See Also

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