intubate (version 1.0.0)

party: Interfaces for party package for data science pipelines.

Description

Interfaces to party functions that can be used in a pipeline implemented by magrittr.

Usage

ntbt_cforest(data, ...)
ntbt_ctree(data, ...)
ntbt_mob(data, ...)

Arguments

data

data frame, tibble, list, ...

...

Other arguments passed to the corresponding interfaced function.

Value

Object returned by interfaced function.

Details

Interfaces call their corresponding interfaced function.

Examples

## Not run: 
# library(intubate)
# library(magrittr)
# library(party)
# 
# ## ntbt_cforest: Random Forest
# 
# ### honest (i.e., out-of-bag) cross-classification of
# ### true vs. predicted classes
# data("mammoexp", package = "TH.data")
# #table(mammoexp$ME, predict(cforest(ME ~ ., data = mammoexp, 
# #                                   control = cforest_unbiased(ntree = 50)),
# #                           OOB = TRUE))
# 
# ## Original function to interface
# set.seed(290875)
# cforest(ME ~ ., data = mammoexp, control = cforest_unbiased(ntree = 50))
# 
# ## The interface puts data as first parameter
# set.seed(290875)
# ntbt_cforest(mammoexp, ME ~ ., control = cforest_unbiased(ntree = 50))
# 
# ## so it can be used easily in a pipeline.
# set.seed(290875)
# mammoexp %>%
#   ntbt_cforest(ME ~ ., control = cforest_unbiased(ntree = 50))
# 
# ## ntbt_ctree: Conditional Inference Trees
# airq <- subset(airquality, !is.na(Ozone))
# 
# ## Original function to interface
# set.seed(290875)
# ctree(Ozone ~ ., data = airq, controls = ctree_control(maxsurrogate = 3))
# 
# ## The interface puts data as first parameter
# set.seed(290875)
# ntbt_ctree(airq, Ozone ~ ., controls = ctree_control(maxsurrogate = 3))
# 
# ## so it can be used easily in a pipeline.
# set.seed(290875)
# airq %>%
#   ntbt_ctree(Ozone ~ ., controls = ctree_control(maxsurrogate = 3))
# 
# 
# ## ntbt_mob: Model-based Recursive Partitioning
# data("BostonHousing", package = "mlbench")
# ## and transform variables appropriately (for a linear regression)
# BostonHousing$lstat <- log(BostonHousing$lstat)
# BostonHousing$rm <- BostonHousing$rm^2
# ## as well as partitioning variables (for fluctuation testing)
# BostonHousing$chas <- factor(BostonHousing$chas, levels = 0:1, 
#                              labels = c("no", "yes"))
# BostonHousing$rad <- factor(BostonHousing$rad, ordered = TRUE)
# 
# ## Original function to interface
# set.seed(290875)
# mob(medv ~ lstat + rm | zn + indus + chas + nox + age + dis + rad + tax + crim + b + ptratio,
#     control = mob_control(minsplit = 40), data = BostonHousing, 
#     model = linearModel)
# 
# ## The interface puts data as first parameter
# set.seed(290875)
# ntbt_mob(BostonHousing, 
#          medv ~ lstat + rm | zn + indus + chas + nox + age + dis + rad + tax + crim + b + ptratio,
#          control = mob_control(minsplit = 40), model = linearModel)
# 
# ## so it can be used easily in a pipeline.
# set.seed(290875)
# BostonHousing %>%
#   ntbt_mob(medv ~ lstat + rm | zn + indus + chas + nox + age + dis + rad + tax + crim + b + ptratio,
#            control = mob_control(minsplit = 40), model = linearModel)
# ## End(Not run)