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.
subsets(object, ...)
"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, ...)
regsubsets
object produced by the regsubsets
function
in the leaps package.object
.1
.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
)."bic"
, Bayes Information Criterion;
"cp"
, Mallows's $Cp$;
"adjr2"
, $R^2$ adjusted for degrees of freedom;
"rsq"
, unadjusted $R^2$;
"rss"
, residual sum of squares.0
, ticks labels are drawn parallel to the
axis; set to 1
for horizontal labels (see par
).1
.subsets.regsubsets
and plot
.NULL
if the legend
is TRUE
; otherwise a data frame with the legend.
regsubsets
if (require(leaps)){
subsets(regsubsets(undercount ~ ., data=Ericksen),
legend=c(3.5, -37))
}
Run the code above in your browser using DataCamp Workspace