CVlm: Cross-Validation for Linear Regression

In DAAG: Data Analysis and Graphics Data and Functions

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

Arguments

data	a data frame
form.lm	a formula or lm call or lm object
m	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 plot)
col.folds	Per fold color settings
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
...	Other arguments, to be passed through to the function legend()

Details

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

Value

The input data frame is returned, with additional columns Predicted (Predicted values using all observations) and cvpred (cross-validation predictions). The cross-validation residual sum of squares (ss) and degrees of freedom (df) are returned as attributes of the data frame.

Author(s)

J.H. Maindonald

See Also

lm, CVbinary

Examples

CVlm()
## Not run: 
CVlm(data=nihills, form.lm=formula(log(time)~log(climb)+log(dist)),
          plotit="Observed")
CVlm(data=nihills, form.lm=formula(log(time)~log(climb)+log(dist)),
     plotit="Residual")
out <- CVlm(data=nihills, form.lm=formula(log(time)~log(climb)+log(dist)),
               plotit="Observed")
out[c("ms","df")]

## End(Not run)

DAAG documentation built on May 29, 2024, 9:13 a.m.