gg_variable: Marginal variable depedance data object.

Description

plot.variable generates a data.frame containing the marginal variable dependance or the partial variable dependence. The gg_variable function creates a data.frame of containing the full set of covariate data (predictor variables) and the predicted response for each observation. Marginal dependence figures are created using the plot.gg_variable function. Optional arguments time point (or vector of points) of interest (for survival forests only) time.labels If more than one time is specified, a vector of time labels for differentiating the time points (for survival forests only) oob indicate if predicted results should include oob or full data set.

Usage

gg_variable(object, ...)

Arguments

object

a rfsrc object

...

optional arguments

Value

gg_variable object

Details

The marginal variable dependence is determined by comparing relation between the predicted response from the randomforest and a covariate of interest.

The gg_variable function operates on a rfsrc object, or the output from the plot.variable function.

Examples

Run this code

## ------------------------------------------------------------
## 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")

plot(gg_dta, xvar=rfsrc_iris$xvar.names, 
     panel=TRUE) # , se=FALSE)

## ------------------------------------------------------------
## regression
## ------------------------------------------------------------
## Not run: 
# ## -------- 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)
# ## End(Not run)
## Not run: 
# ## -------- 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")
# 
# 
# # panels
# plot(gg_dta,xvar=c("disp","hp", "drat", "wt", "qsec"),  panel=TRUE)
# plot(gg_dta, xvar=c("cyl", "vs", "am", "gear", "carb"), panel=TRUE, notch=TRUE)
# ## End(Not run)
## -------- Boston data

## ------------------------------------------------------------
## survival examples
## ------------------------------------------------------------
## Not run: 
# ## -------- veteran data
# ## survival
# # data(veteran, package = "randomForestSRC")
# # rfsrc_veteran <- rfsrc(Surv(time,status)~., veteran, nsplit = 10, ntree = 100)
# data(rfsrc_veteran, package="ggRandomForests")
# 
# # 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, se=FALSE)
# 
# # 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")
# ## End(Not run)
## -------- pbc data