ggRandomForests (version 2.0.1)

gg_minimal_vimp: Minimal depth vs VIMP camparison by variable rankings.

Description

Minimal depth vs VIMP camparison by variable rankings.

Usage

gg_minimal_vimp(object, ...)

Arguments

object

A rfsrc object, predict.rfsrc object or the list from the var.select.rfsrc function.

...

optional arguments passed to the var.select function if operating on an rfsrc object.

@return gg_minimal_vimp comparison object. @seealso plot.gg_minimal_vimp var.select @aliases gg_minimal_vimp

Examples

## Examples from RFSRC package... 
## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
## Not run: 
# ## -------- iris data
# ## You can build a randomForest
# # rfsrc_iris <- rfsrc(Species ~ ., data = iris)
# # varsel_iris <- randomForestSRC::var.select(rfsrc_iris)
# # ... or load a cached randomForestSRC object
# data(varsel_iris, package="ggRandomForests")
# 
# # Get a data.frame containing minimaldepth measures
# gg_dta<- gg_minimal_vimp(varsel_iris)
# 
# # Plot the gg_minimal_depth object
# plot(gg_dta)
# ## End(Not run)
## ------------------------------------------------------------
## Regression example
## ------------------------------------------------------------
## Not run: 
# ## -------- air quality data
# # rfsrc_airq <- rfsrc(Ozone ~ ., data = airquality, na.action = "na.impute")
# # varsel_airq <- randomForestSRC::var.select(rfsrc_airq)
# # ... or load a cached randomForestSRC object
# data(varsel_airq, package="ggRandomForests")
# 
# # Get a data.frame containing error rates
# gg_dta<- gg_minimal_vimp(varsel_airq)
# 
# # Plot the gg_minimal_vimp object
# plot(gg_dta)
# ## End(Not run)
## Not run: 
# ## -------- Boston data
# data(varsel_Boston, package="ggRandomForests")
# 
# # Get a data.frame containing error rates
# gg_dta<- gg_minimal_vimp(varsel_Boston)
# 
# # Plot the gg_minimal_vimp object
# plot(gg_dta)
# ## End(Not run)
## Not run: 
# ## -------- mtcars data
# data(varsel_mtcars, package="ggRandomForests")
# 
# # Get a data.frame containing error rates
# gg_dta<- gg_minimal_vimp(varsel_mtcars)
# 
# # Plot the gg_minimal_vimp object
# plot(gg_dta)
# ## End(Not run)
## ------------------------------------------------------------
## Survival example
## ------------------------------------------------------------
## Not run: 
# ## -------- veteran data
# ## randomized trial of two treatment regimens for lung cancer
# # data(veteran, package = "randomForestSRC")
# # rfsrc_veteran <- rfsrc(Surv(time, status) ~ ., data = veteran, ntree = 100)
# # varsel_veteran <- randomForestSRC::var.select(rfsrc_veteran)
# # Load a cached randomForestSRC object
# data(varsel_veteran, package="ggRandomForests")
# 
# gg_dta <- gg_minimal_vimp(varsel_veteran)
# plot(gg_dta)
# ## End(Not run)
## Not run: 
# ## -------- pbc data
# data(varsel_pbc, package="ggRandomForests")
# 
# gg_dta <- gg_minimal_vimp(varsel_pbc)
# plot(gg_dta)
# ## End(Not run)