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

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

## 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.

- sorted
Should variables be sorted by importance values?

- ...
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.

## 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)
# }
```