dtree: Main function for creating different types of decision trees

In Rjacobucci/dtree: Decision Trees

Description

Main function for creating different types of decision trees

Usage

dtree(formula, data, methods = c("lm", "rpart", "tree", "ctree"),
  samp.method = "repeatedcv", tuneLength = 3, bump.rep = 50,
  subset = FALSE, perc.sub = 0.75, weights = NULL, verbose = TRUE)

Arguments

formula	a formula, with a response to left of ~.
data	Data frame to run models on
methods	Which tree methods to use. Defaults: lm, rpart, ctree, evtree. Also can use "rf" for random forests (cforest from party). Also a FDR pruning method for ctree termed "ctreePrune". Finally bumping is implemented as methods="bump".
samp.method	Sampling method. Refer to caret package trainControl() documentation. Default is repeated cross-validation. Other options include "cv" and "boot".
tuneLength	Number of tuning parameters to try. Applies to train(). Can also be specified as a vector, with order corresponding to the order specified in the methods argument.
bump.rep	Number of repetitions for bumping
subset	Whether to split dataset into training and test sets
perc.sub	What fraction of data to put into train dataset. 1-frac.sub is allocated to test dataset. Defaults to 0.75
weights	Optional weights for each case.
verbose	Whether to print what method on

Examples

# continuous outcome
#library(MASS) # for boston data
#data(Boston)
#out <- dtree(medv ~., data=Boston,methods=c("lm","rpart","ctree"))
#summary(out)
# plot(out$rpart.out)

# categorical outcome
#library(ISLR)
#data(Default)

#out <- dtree(default ~ ., data=Default,methods=c("lm","rpart"))
#summary(out)

Rjacobucci/dtree documentation built on July 12, 2019, 8:56 p.m.