ggRandomForests (version 2.0.1)

plot.gg_partial_list: Partial variable dependence plot, operates on a gg_partial_list 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_list object).

Usage

"plot"(x, points = TRUE, panel = FALSE, ...)

Arguments

x

gg_partial_list object created from a gg_partial forest object

points

plot points (boolean) or a smooth line.

panel

should the entire list be plotted together?

...

extra arguments

Value

list of ggplot objects, or a single faceted ggplot object

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.

See Also

plot.variable gg_partial plot.gg_partial gg_variable plot.gg_variable

Examples

## Not run: 
# ## ------------------------------------------------------------
# ## 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
# ## End(Not run)