pkgname <- "moreparty"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
library('moreparty')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("BivariateAssoc")
### * BivariateAssoc
flush(stderr()); flush(stdout())
### Name: BivariateAssoc
### Title: Bivariate association measures for supervised learning tasks.
### Aliases: BivariateAssoc
### ** Examples
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
BivariateAssoc(iris2$Species,iris2[,1:4])
cleanEx()
nameEx("EasyTreeVarImp")
### * EasyTreeVarImp
flush(stderr()); flush(stdout())
### Name: EasyTreeVarImp
### Title: Variable importance for conditional inference trees.
### Aliases: EasyTreeVarImp
### Keywords: tree
### ** Examples
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
iris.ct = partykit::ctree(Species ~ ., data = iris2)
EasyTreeVarImp(iris.ct, nsim = 1)
cleanEx()
nameEx("FeatureSelection")
### * FeatureSelection
flush(stderr()); flush(stdout())
### Name: FeatureSelection
### Title: Feature selection for conditional random forests.
### Aliases: FeatureSelection
### ** Examples
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
featsel <- FeatureSelection(iris2$Species, iris2[,1:4], measure='ACC', ntree=200)
featsel$selection.0se
featsel$selection.1se
cleanEx()
nameEx("GetAleData")
### * GetAleData
flush(stderr()); flush(stdout())
### Name: GetAleData
### Title: Accumulated Local Effects for a conditional random forest.
### Aliases: GetAleData
### Keywords: tree
### ** Examples
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
iris.cf = party::cforest(Species ~ ., data = iris2,
controls = party::cforest_unbiased(mtry=2, ntree=50))
GetAleData(iris.cf)
cleanEx()
nameEx("GetCtree")
### * GetCtree
flush(stderr()); flush(stdout())
### Name: GetCtree
### Title: Gets a tree from a conditional random forest
### Aliases: GetCtree
### Keywords: tree
### ** Examples
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
iris.cf = party::cforest(Species ~ ., data = iris2,
control = party::cforest_unbiased(mtry = 2, ntree = 50))
plot(GetCtree(iris.cf))
cleanEx()
nameEx("GetInteractionStrength")
### * GetInteractionStrength
flush(stderr()); flush(stdout())
### Name: GetInteractionStrength
### Title: Strength of interactions
### Aliases: GetInteractionStrength
### Keywords: tree
### ** Examples
## Not run:
##D data(iris)
##D iris2 = iris
##D iris2$Species = factor(iris$Species == "versicolor")
##D iris.cf = party::cforest(Species ~ ., data = iris2,
##D controls = party::cforest_unbiased(mtry=2, ntree=50))
##D GetInteractionStrength(iris.cf)
##D
## End(Not run)
cleanEx()
nameEx("GetPartialData")
### * GetPartialData
flush(stderr()); flush(stdout())
### Name: GetPartialData
### Title: Partial dependence for a conditional random forest.
### Aliases: GetPartialData
### Keywords: tree
### ** Examples
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
iris.cf = party::cforest(Species ~ ., data = iris2,
controls = party::cforest_unbiased(mtry=2, ntree=50))
GetPartialData(iris.cf)
cleanEx()
nameEx("GetSplitStats")
### * GetSplitStats
flush(stderr()); flush(stdout())
### Name: GetSplitStats
### Title: Permutation tests results for each split in a conditional tree.
### Aliases: GetSplitStats
### Keywords: tree
### ** Examples
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
iris.ct = partykit::ctree(Species ~ ., data = iris2)
GetSplitStats(iris.ct)
cleanEx()
nameEx("NiceTreePlot")
### * NiceTreePlot
flush(stderr()); flush(stdout())
### Name: NiceTreePlot
### Title: Plots conditional inference trees.
### Aliases: NiceTreePlot
### Keywords: tree
### ** Examples
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
iris.ct = partykit::ctree(Species ~ ., data = iris2)
NiceTreePlot(iris.ct, inner_plots = TRUE)
cleanEx()
nameEx("Outliers")
### * Outliers
flush(stderr()); flush(stdout())
### Name: Outliers
### Title: Computes outliers
### Aliases: Outliers
### Keywords: classif
### ** Examples
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
iris.cf = party::cforest(Species ~ ., data = iris2,
control = party::cforest_unbiased(mtry = 2, ntree = 50))
prox=proximity(iris.cf)
Outliers(prox, iris2$Species, iris2[,1:4])
cleanEx()
nameEx("Prototypes")
### * Prototypes
flush(stderr()); flush(stdout())
### Name: Prototypes
### Title: Prototypes of groups
### Aliases: Prototypes
### Keywords: classif
### ** Examples
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
iris.cf = party::cforest(Species ~ ., data = iris2,
control = party::cforest_unbiased(mtry = 2, ntree = 50))
prox=proximity(iris.cf)
Prototypes(iris2$Species,iris2[,1:4],prox)
cleanEx()
nameEx("SurrogateTree")
### * SurrogateTree
flush(stderr()); flush(stdout())
### Name: SurrogateTree
### Title: Surrogate tree for conditional inference random forests
### Aliases: SurrogateTree
### ** Examples
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
iris.cf = party::cforest(Species ~ ., data = iris2,
control = party::cforest_unbiased(mtry = 2, ntree = 50))
surro <- SurrogateTree(iris.cf)
surro$r.squared
plot(surro$tree)
cleanEx()
nameEx("ctree-module")
### * ctree-module
flush(stderr()); flush(stdout())
### Name: ctree-module
### Title: Shiny module to build and analyse conditional inference trees
### Aliases: ctree-module ctreeUI ctreeServer
### Keywords: tree
### ** Examples
library(shiny)
library(moreparty)
data(titanic)
ui <- fluidPage(
titlePanel("Conditional inference trees"),
ctreeUI(id = "ctree_app")
)
server <- function(input, output, session) {
rv <- reactiveValues(
data = titanic,
name = deparse(substitute(titanic))
)
ctreeServer(id = "ctree_app", reactive(rv$data), reactive(rv$name))
}
if (interactive())
shinyApp(ui, server)
cleanEx()
nameEx("fastcforest")
### * fastcforest
flush(stderr()); flush(stdout())
### Name: fastcforest
### Title: Parallelized conditional inference random forest
### Aliases: fastcforest
### Keywords: tree
### ** Examples
## classification
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species=="versicolor")
iris.cf = fastcforest(Species~., data=iris2, parallel=FALSE)
cleanEx()
nameEx("fastvarImp")
### * fastvarImp
flush(stderr()); flush(stdout())
### Name: fastvarImp
### Title: Variable importance for conditional inference random forests
### Aliases: fastvarImp
### ** Examples
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
iris.cf = party::cforest(Species ~ ., data = iris2,
control = party::cforest_unbiased(mtry = 2, ntree = 50))
fastvarImp(object = iris.cf, measure='ACC', parallel=FALSE)
cleanEx()
nameEx("fastvarImpAUC")
### * fastvarImpAUC
flush(stderr()); flush(stdout())
### Name: fastvarImpAUC
### Title: Variable importance (with AUC performance measure) for
### conditional inference random forests
### Aliases: fastvarImpAUC
### ** Examples
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
iris.cf = party::cforest(Species ~ ., data = iris2,
control = party::cforest_unbiased(mtry = 2, ntree = 50))
fastvarImpAUC(object = iris.cf, parallel = FALSE)
cleanEx()
nameEx("ggForestEffects")
### * ggForestEffects
flush(stderr()); flush(stdout())
### Name: ggForestEffects
### Title: Dot plot of covariates effects
### Aliases: ggForestEffects
### Keywords: aplot tree
### ** Examples
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
iris.cf = party::cforest(Species ~ ., data = iris2, controls = cforest_unbiased(mtry=2))
ale <- GetAleData(iris.cf)
ale$cat <- paste(ale$var,ale$cat,sep='_') # to avoid duplicated categories
ggForestEffects(ale)
cleanEx()
nameEx("ggVarImp")
### * ggVarImp
flush(stderr()); flush(stdout())
### Name: ggVarImp
### Title: Dot plot of variable importance
### Aliases: ggVarImp
### Keywords: aplot tree
### ** Examples
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
iris.cf = party::cforest(Species ~ ., data = iris2,
control = party::cforest_unbiased(mtry = 2, ntree = 50))
imp <- fastvarImpAUC(object = iris.cf, parallel = FALSE)
ggVarImp(imp)
cleanEx()
nameEx("ictree")
### * ictree
flush(stderr()); flush(stdout())
### Name: ictree
### Title: An interactive app for conditional inference trees
### Aliases: ictree
### Keywords: tree
### ** Examples
if (interactive()) {
ictree(iris)
}
cleanEx()
nameEx("titanic")
### * titanic
flush(stderr()); flush(stdout())
### Name: titanic
### Title: Titanic dataset
### Aliases: titanic
### Keywords: datasets
### ** Examples
data(titanic)
str(titanic)
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
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