plot.partialpro: Partial Plots for VarPro

In kogalur/varPro: Model-Independent Variable Selection via the Rule-Based Variable Priority (VarPro)

Partial Plots for VarPro

Description

Plot partial effects of x-variable(s) from a VarPro analysis.

Usage

## S3 method for class 'partialpro'
plot(x, xvar.names, nvar,
  parametric = FALSE, se = TRUE,
  causal = FALSE, subset = NULL, plot.it = TRUE, ...)

Arguments

x	partialpro object returned from a previous call to partialpro.
xvar.names	Names (or integer indices) of the x-variables to plot. Defaults to all variables.
nvar	Number of variables to plot. Defaults to all.
parametric	Logical. Set to TRUE only if the partial effect is believed to follow a polynomial form.
se	Display standard errors?
causal	Display causal estimator?
subset	Optional conditioning factor. Not applicable if parametric = TRUE. May also be a logical or integer vector to subset the analysis.
plot.it	If FALSE, no plot is produced; instead, partial effect values are returned.
...	Additional arguments passed to plot.

Details

Generates smoothed partial effect plots for continuous variables. The solid black line represents the estimated partial effect; dashed red lines show an approximate plus-minus standard error band. These standard errors are intended as heuristic guides and should be interpreted cautiously.

Partial effects are estimated nonparametrically using locally fitted polynomial models. This is the default behavior and is recommended when effects are expected to be nonlinear. Use parametric = TRUE if the underlying effect is believed to follow a global polynomial form.

For binary variables, partial effects are shown as boxplots, with whiskers reflecting variability analogous to standard error.

The causal estimator, when requested, displays the baseline-subtracted parametric local effect.

Conditioning is supported via the subset option. When supplied as a factor (with length equal to the original data), the plot is stratified by its levels. Alternatively, subset can be a logical or integer vector indicating the cases to include in the analysis.

Author(s)

Min Lu and Hemant Ishwaran

References

Ishwaran H. (2025). Multivariate Statistics: Classical Foundations and Modern Machine Learning, CRC (Chapman and Hall), in press.

See Also

partialpro

Examples

##------------------------------------------------------------------
##
## Boston housing
##
##------------------------------------------------------------------

library(mlbench)
data(BostonHousing)
o.boston <- varpro(medv~.,BostonHousing)
oo.boston <- partialpro(o.boston, nvar=4, learner=rf.learner(o.boston))

par(mfrow=c(2,4))

## parametric local estimation (default)
plot(oo.boston, ylab="parametric est.")

## non-parametric local estimation
plot(oo.boston, parametric=FALSE, ylab="non-parametric est.")


##------------------------------------------------------------------
##
## Boston housing with subsetting
##
##------------------------------------------------------------------

library(mlbench)
data(BostonHousing)
o.boston <- varpro(medv~.,BostonHousing)
oo.boston <- partialpro(o.boston, nvar=3, learner=rf.learner(o.boston))

## subset analysis
price <- BostonHousing$medv
pricef <- factor(price>median(price), labels=c("low priced","high priced"))
par(mfrow=c(1,1))
plot(oo.boston, subset=pricef, nvar=1)


##------------------------------------------------------------------
##
## veteran data with subsetting using celltype as a factor
##
##------------------------------------------------------------------

data(veteran, package = "randomForestSRC")
dta <- veteran
dta$celltype <- factor(dta$celltype)
o.vet <- varpro(Surv(time, status)~., dta)
oo.vet <- partialpro(o.vet, nvar=6, nsmp=Inf, learner=rf.learner(o.vet))

## partial effects, with subsetting
par(mfrow=c(2,3))
plot(oo.vet, subset=dta$celltype)

## causal effects, with subsetting
par(mfrow=c(2,3))
plot(oo.vet, subset=dta$celltype, causal=TRUE)

kogalur/varPro documentation built on June 2, 2025, 6:24 a.m.