subsets: Plot Output from regsubsets Function in leaps package

Description

The regsubsets function in the leaps package finds optimal subsets of predictors. This function plots a measure of fit (see the statistic argument below) against subset size.

Usage

subsets(object, ...)

## S3 method for class 'regsubsets':
subsets(object, 
    names=abbreviate(object$xnames, minlength = abbrev), 
    abbrev=1, min.size=1, max.size=length(names), 
    legend="interactive", 
    statistic=c("bic", "cp", "adjr2", "rsq", "rss"), 
    las=par('las'), cex.subsets=1, ...)

Arguments

object

a regsubsets object produced by the regsubsets function in the leaps package.

names

a vector of (short) names for the predictors, excluding the regression intercept, if one is present; if missing, these are derived from the predictor names in object.

abbrev

minimum number of characters to use in abbreviating predictor names.

min.size

minimum size subset to plot; default is 1.

max.size

maximum size subset to plot; default is number of predictors.

legend

If not FALSE, in which case the legend is suppressed, the coordinates at which to place a legend of the abbreviated predictor names on the plot, in a form recognized by the legend

statistic

statistic to plot for each predictor subset; one of: "bic", Bayes Information Criterion; "cp", Mallows's $C_{p}$; "adjr2", $R^{2}$ adjusted for degrees of freedom; "rsq", unadjusted

las

if 0, ticks labels are drawn parallel to the axis; set to 1 for horizontal labels (see par).

cex.subsets

can be used to change the relative size of the characters used to plot the regression subsets; default is 1.

...

arguments to be passed down to subsets.regsubsets and plot.

Value

NULL if the legend is TRUE; otherwise a data frame with the legend.

References

Fox, J. (2008) Applied Regression Analysis and Generalized Linear Models, Second Edition. Sage. Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, Second Edition, Sage.

Examples

Run this code

if (require(leaps)){
    subsets(regsubsets(undercount ~ ., data=Ericksen),
            legend=c(3.5, -37))
}