Plots VIMP (variable importance) confidence regions obtained from subsampling a forest.

plot.subsample(x, alpha = .01, xvar.names,
 standardize = TRUE, normal = TRUE, jknife = TRUE,
 target, m.target = NULL, pmax = 75, main = "", ...)

Arguments

x

An object obtained from calling subample.

alpha

Desired level of significance.

xvar.names

Names of the x-variables to be used. If not specified all variables used.

standardize

Standardize VIMP? For regression families, VIMP is standardized by dividing by the variance and then multipled by 100. For all other families, VIMP is scaled by 100.

normal

Use parametric normal confidence regions or nonparametric regions? Generally, parametric regions perform better.

jknife

Use the delete-d jackknife variance estimator?

target

For classification families, an integer or character value specifying the class VIMP will be conditioned on (default is to use unconditional VIMP). For competing risk families, an integer value between 1 and J indicating the event VIMP is requested, where J is the number of event types. The default is to use the first event.

m.target

Character value for multivariate families specifying the target outcome to be used. If left unspecified, the algorithm will choose a default target.

pmax

Trims the data to this number of variables (sorted by VIMP).

main

Title used for plot.

...

Further arguments that can be passed to bxp.

Details

Most of the options used by the R function bxp will work here and can be used for customization of plots. Currently the following parameters will work:

"xaxt", "yaxt", "las", "cex.axis", "col.axis", "cex.main", "col.main", "sub", "cex.sub", "col.sub", "ylab", "cex.lab", "col.lab"

Value

Invisibly, returns the boxplot data that is plotted.

Author

Hemant Ishwaran and Udaya B. Kogalur

References

Ishwaran H. and Lu M. (2017). Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival.

Politis, D.N. and Romano, J.P. (1994). Large sample confidence regions based on subsamples under minimal assumptions. The Annals of Statistics, 22(4):2031-2050.

Shao, J. and Wu, C.J. (1989). A general theory for jackknife variance estimation. The Annals of Statistics, 17(3):1176-1197.

See also

Examples

# \donttest{
o <- rfsrc(Ozone ~ ., airquality)
oo <- subsample(o)
plot.subsample(oo)
plot.subsample(oo, xvar.names = o$xvar.names[1:3])
plot.subsample(oo, jknife = FALSE)
plot.subsample(oo, alpha = .01)
plot(oo,cex.axis=.5)
# }