| mle.cv {wle} | R Documentation |
The Cross Validation selection method is evaluated for each submodel.
mle.cv(formula, data=list(), model=TRUE, x=FALSE,
y=FALSE, monte.carlo=500, split,
contrasts=NULL, verbose=FALSE)
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. |
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.
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. |
Claudio Agostinelli
Shao, J., (1993) Linear model selection by Cross-Validation. Journal American Statistical Association, 88, 486-494.
library(wle) data(hald) cor(hald) result <- mle.cv(y.hald~x.hald) summary(result)