CVlm: Cross-Validation for Linear Regression

Description

This function gives internal and cross-validation measures of predictive accuracy for multiple linear regression. (For binary logistic regression, use the CVbinary function.) The data are randomly assigned to a number of `folds'. Each fold is removed, in turn, while the remaining data is used to re-fit the regression model and to predict at the deleted observations.

Usage

CVlm(df = houseprices, form.lm = formula(sale.price ~ area), m=3, dots =
FALSE, seed=29,  plotit = c("Observed","Residual"),
main="Small symbols show cross-validation predicted values",
legend.pos="topleft", printit=TRUE)
cv.lm(df = houseprices, form.lm = formula(sale.price ~ area), m=3, dots =
FALSE, seed=29,  plotit = c("Observed","Residual"),
main="Small symbols show cross-validation predicted values",
legend.pos="topleft", printit=TRUE)

Arguments

a data frame

form.lm

a formula or lm call or lm object

the number of folds

dots

uses pch=16 for the plotting character

seed

random number generator seed

plotit

This can be one of the text strings "Observed", "Residual", or a logical value. The logical TRUE is equivalent to "Observed", while FALSE is equivalent to "" (no pl

main

main title for graph

legend.pos

position of legend: one of "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right", "center"

printit

if TRUE, output is printed to the screen

Value

ssthe cross-validation residual sum of squares
dfdegrees of freedom

Details

When plotit="Residual" and there is more than one explanatory variable, the fitted lines that are shown for the individual folds are approximations.

Examples

Run this code

CVlm()
CVlm(df=nihills, form.lm=formula(log(time)~log(climb)+log(dist)),
          plotit="Observed")
CVlm(df=nihills, form.lm=formula(log(time)~log(climb)+log(dist)),
          plotit="Residual")