plot.gg_partial: Partial variable dependence plot, operates on a `gg_partial` object.

Description

Generate a risk adjusted (partial) variable dependence plot. The function plots the rfsrc response variable (y-axis) against the covariate of interest (specified when creating the gg_partial object).

Usage

# S3 method for gg_partial
plot(x, points = TRUE, error = c("none", "shade", "bars", "lines"), ...)

Value

ggplot object

Arguments

x: gg_partial object created from a rfsrc forest object
points: plot points (boolean) or a smooth line.
error: "shade", "bars", "lines" or "none"
...: extra arguments passed to ggplot2 functions.

References

Breiman L. (2001). Random forests, Machine Learning, 45:5-32.

Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R, Rnews, 7(2):25-31.

Ishwaran H. and Kogalur U.B. (2013). Random Forests for Survival, Regression and Classification (RF-SRC), R package version 1.4.

Examples

Run this code

if (FALSE) {
## ------------------------------------------------------------
## classification
## ------------------------------------------------------------
## -------- iris data

## iris "Petal.Width" partial dependence plot
##
# rfsrc_iris <- rfsrc(Species ~., data = iris)
# partial_iris <- plot.variable(rfsrc_iris, xvar.names = "Petal.Width",
#                            partial=TRUE)
data(partial_iris, package="ggRandomForests")

gg_dta <- gg_partial(partial_iris)
plot(gg_dta)

## ------------------------------------------------------------
## regression
## ------------------------------------------------------------
## -------- air quality data
## airquality "Wind" partial dependence plot
##
# rfsrc_airq <- rfsrc(Ozone ~ ., data = airquality)
# partial_airq <- plot.variable(rfsrc_airq, xvar.names = "Wind",
#                            partial=TRUE, show.plot=FALSE)
data(partial_airq, package="ggRandomForests")

gg_dta <- gg_partial(partial_airq)
plot(gg_dta)

gg_dta.m <- gg_dta[["Month"]]
plot(gg_dta.m, notch=TRUE)

gg_dta[["Month"]] <- NULL
plot(gg_dta, panel=TRUE)

## -------- Boston data
data(partial_boston, package="ggRandomForests")

gg_dta <- gg_partial(partial_boston)
plot(gg_dta)
plot(gg_dta, panel=TRUE)

## -------- mtcars data
data(partial_mtcars, package="ggRandomForests")

gg_dta <- gg_partial(partial_mtcars)

plot(gg_dta)

gg_dta.cat <- gg_dta
gg_dta.cat[["disp"]] <- gg_dta.cat[["wt"]] <- gg_dta.cat[["hp"]] <- NULL
gg_dta.cat[["drat"]] <- gg_dta.cat[["carb"]] <-
   gg_dta.cat[["qsec"]] <- NULL

plot(gg_dta.cat, panel=TRUE)

gg_dta[["cyl"]] <- gg_dta[["vs"]] <- gg_dta[["am"]] <- NULL
gg_dta[["gear"]] <- NULL
plot(gg_dta, panel=TRUE)

## ------------------------------------------------------------
## survival examples
## ------------------------------------------------------------
## -------- veteran data
## survival "age" partial variable dependence plot
##
# data(veteran, package = "randomForestSRC")
# rfsrc_veteran <- rfsrc(Surv(time,status)~., veteran, nsplit = 10,
#                        ntree = 100)
#
## 30 day partial plot for age
# partial_veteran <- plot.variable(rfsrc_veteran, surv.type = "surv",
#                               partial = TRUE, time=30,
#                               xvar.names = "age",
#                               show.plots=FALSE)
data(partial_veteran, package="ggRandomForests")

gg_dta <- gg_partial(partial_veteran[[1]])
plot(gg_dta)

gg_dta.cat <- gg_dta
gg_dta[["celltype"]] <- gg_dta[["trt"]] <- gg_dta[["prior"]] <- NULL
plot(gg_dta, panel=TRUE)

gg_dta.cat[["karno"]] <- gg_dta.cat[["diagtime"]] <-
     gg_dta.cat[["age"]] <- NULL
plot(gg_dta.cat, panel=TRUE, notch=TRUE)

gg_dta <- lapply(partial_veteran, gg_partial)
length(gg_dta)
gg_dta <- combine.gg_partial(gg_dta[[1]], gg_dta[[2]] )

plot(gg_dta[["karno"]])
plot(gg_dta[["celltype"]])

gg_dta.cat <- gg_dta
gg_dta[["celltype"]] <- gg_dta[["trt"]] <- gg_dta[["prior"]] <- NULL
plot(gg_dta, panel=TRUE)

gg_dta.cat[["karno"]] <- gg_dta.cat[["diagtime"]] <-
     gg_dta.cat[["age"]] <- NULL
plot(gg_dta.cat, panel=TRUE, notch=TRUE)

## -------- pbc data
}