mle.cv: Cross Validation Selection Method

In wle: Weighted Likelihood Estimation

Description

The Cross Validation selection method is evaluated for each submodel.

Usage

mle.cv(formula, data=list(), model=TRUE, x=FALSE, 
       y=FALSE, monte.carlo=500, split, 
       contrasts=NULL, verbose=FALSE)

Arguments

formula	a symbolic description of the model to be fit. The details of model specification are given below.
data	an optional data frame containing the variables in the model. By default the variables are taken from the environment which mle.cv is called from.
model, x, y	logicals. If TRUE the corresponding components of the fit (the model frame, the model matrix, the response.)
monte.carlo	the number of Monte Carlo replication we use to estimate the average prediction error.
split	the size of the costruction sample. When the suggested value is outside the possible range, the split size is let equal to max(round(size^{(3/4)}),nvar+2).
contrasts	an optional list. See the contrasts.arg of model.matrix.default.
verbose	if TRUE warnings are printed.

Details

Models for mle.cv are specified symbolically. A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first+second indicates all the terms in first together with all the terms in second with duplicates removed. A specification of the form first:second indicates the the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first+second+first:second.

Value

mle.cv returns an object of class "mle.cv".

The function summary is used to obtain and print a summary of the results.

The object returned by mle.cv are:

cv	the estimated prediction error for each submodels
call	the match.call().
contrasts
xlevels
terms	the model frame.
model	if model=TRUE a matrix with first column the dependent variable and the remain column the explanatory variables for the full model.
x	if x=TRUE a matrix with the explanatory variables for the full model.
y	if y=TRUE a vector with the dependent variable.
info	not well working yet, if 0 no error occurred.

Author(s)

Claudio Agostinelli

References

Shao, J., (1993) Linear model selection by Cross-Validation. Journal American Statistical Association, 88, 486-494.

Examples

library(wle)

data(hald)

cor(hald)

result <- mle.cv(y.hald~x.hald)

summary(result)

wle documentation built on May 29, 2017, 11:48 a.m.