ggRandomForests (version 2.0.1)

plot.gg_variable: Plot a gg_variable object,

Description

Plot a gg_variable object,

Usage

"plot"(x, xvar, time, time_labels, panel = FALSE, oob = TRUE, points = TRUE, smooth = TRUE, ...)

Arguments

x

gg_variable object created from a rfsrc object

xvar

variable (or list of variables) of interest.

time

For survival, one or more times of interest

time_labels

string labels for times

panel

Should plots be facetted along multiple xvar?

oob

oob estimates (boolean)

points

plot the raw data points (boolean)

smooth

include a smooth curve (boolean)

...

arguments passed to the ggplot2 functions.

Value

A single ggplot object, or list of ggplot objects

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

## Not run: 
# ## ------------------------------------------------------------
# ## classification
# ## ------------------------------------------------------------
# ## -------- iris data
# ## iris
# #rfsrc_iris <- rfsrc(Species ~., data = iris)
# data(rfsrc_iris, package="ggRandomForests")
# 
# gg_dta <- gg_variable(rfsrc_iris)
# plot(gg_dta, xvar="Sepal.Width")
# plot(gg_dta, xvar="Sepal.Length")
# 
# ## !! TODO !! this needs to be corrected
# plot(gg_dta, xvar=rfsrc_iris$xvar.names, 
#      panel=TRUE, se=FALSE)
# 
# ## ------------------------------------------------------------
# ## regression
# ## ------------------------------------------------------------
# ## -------- air quality data
# #rfsrc_airq <- rfsrc(Ozone ~ ., data = airquality)
# data(rfsrc_airq, package="ggRandomForests")
# gg_dta <- gg_variable(rfsrc_airq)
# 
# # an ordinal variable 
# gg_dta[,"Month"] <- factor(gg_dta[,"Month"])
# 
# plot(gg_dta, xvar="Wind")
# plot(gg_dta, xvar="Temp")
# plot(gg_dta, xvar="Solar.R")
# 
# plot(gg_dta, xvar=c("Solar.R", "Wind", "Temp", "Day"), panel=TRUE)
# 
# plot(gg_dta, xvar="Month", notch=TRUE)
# 
# ## -------- motor trend cars data
# #rfsrc_mtcars <- rfsrc(mpg ~ ., data = mtcars)
# data(rfsrc_mtcars, package="ggRandomForests")
# gg_dta <- gg_variable(rfsrc_mtcars)
# 
# # mtcars$cyl is an ordinal variable 
# gg_dta$cyl <- factor(gg_dta$cyl)
# gg_dta$am <- factor(gg_dta$am)
# gg_dta$vs <- factor(gg_dta$vs)
# gg_dta$gear <- factor(gg_dta$gear)
# gg_dta$carb <- factor(gg_dta$carb)
# 
# plot(gg_dta, xvar="cyl")
# 
# # Others are continuous
# plot(gg_dta, xvar="disp")
# plot(gg_dta, xvar="hp")
# plot(gg_dta, xvar="wt")
# 
# # panel
# plot(gg_dta,xvar=c("disp","hp", "drat", "wt", "qsec"),  panel=TRUE)
# plot(gg_dta, xvar=c("cyl", "vs", "am", "gear", "carb") ,panel=TRUE)
# 
# ## -------- Boston data
# 
# ## ------------------------------------------------------------
# ## survival examples
# ## ------------------------------------------------------------
# ## -------- veteran data
# ## survival
# data(veteran, package = "randomForestSRC")
# rfsrc_veteran <- rfsrc(Surv(time,status)~., veteran, nsplit = 10, ntree = 100)
# 
# # get the 1 year survival time.
# gg_dta <- gg_variable(rfsrc_veteran, time=90)
# 
# # Generate variable dependance plots for age and diagtime
# plot(gg_dta, xvar = "age")
# plot(gg_dta, xvar = "diagtime")
# 
# # Generate coplots
# plot(gg_dta, xvar = c("age", "diagtime"), panel=TRUE)
# 
# # If we want to compare survival at different time points, say 30, 90 day 
# # and 1 year
# gg_dta <- gg_variable(rfsrc_veteran, time=c(30, 90, 365))
# 
# # Generate variable dependance plots for age and diagtime
# plot(gg_dta, xvar = "age")
# plot(gg_dta, xvar = "diagtime") 
# 
# # Generate coplots
# plot(gg_dta, xvar =  c("age", "diagtime"), panel=TRUE)
# 
# ## -------- pbc data
# ## End(Not run)