subsets

From car v3.0-10 by John Fox

Plot Output from regsubsets Function in leaps package

The regsubsets function in the leaps package finds optimal subsets of predictors based on some criterion statistic. This function plots a measure of fit against subset size.

Keywords: hplot, regression

Usage

subsets(object, ...)
# S3 method for 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 function. If "interactive", the legend is placed on the plot interactively with the mouse. By expanding the left or right plot margin, you can place the legend in the margin, if you wish (see par).
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 \(R^{2}\); "rss", residual sum of squares.
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. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.

Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.

Examples

# NOT RUN {
if (require(leaps)){
    subsets(regsubsets(undercount ~ ., data=Ericksen),
            legend=c(3.5, -37))
}
# }

Documentation reproduced from package car, version 3.0-10, License: GPL (>= 2)

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