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.

`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, ...)

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`

.

`NULL`

if the `legend`

is `TRUE`

; otherwise a data frame with the legend.

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.

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