rforest: Random Forest using Ranger

In radiant.model: Model Menu for Radiant: Business Analytics using R and Shiny

Random Forest using Ranger

Description

Random Forest using Ranger

Usage

rforest(
  dataset,
  rvar,
  evar,
  type = "classification",
  lev = "",
  mtry = NULL,
  num.trees = 100,
  min.node.size = 1,
  sample.fraction = 1,
  replace = NULL,
  num.threads = 12,
  wts = "None",
  seed = NA,
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame(),
  ...
)

Arguments

dataset	Dataset
rvar	The response variable in the model
evar	Explanatory variables in the model
type	Model type (i.e., "classification" or "regression")
lev	Level to use as the first column in prediction output
mtry	Number of variables to possibly split at in each node. Default is the (rounded down) square root of the number variables
num.trees	Number of trees to create
min.node.size	Minimal node size
sample.fraction	Fraction of observations to sample. Default is 1 for sampling with replacement and 0.632 for sampling without replacement
replace	Sample with (TRUE) or without (FALSE) replacement. If replace is NULL it will be reset to TRUE if the sample.fraction is equal to 1 and will be set to FALSE otherwise
num.threads	Number of parallel threads to use. Defaults to 12 if available
wts	Case weights to use in estimation
seed	Random seed to use as the starting point
data_filter	Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000")
arr	Expression to arrange (sort) the data on (e.g., "color, desc(price)")
rows	Rows to select from the specified dataset
envir	Environment to extract data from
...	Further arguments to pass to ranger

Details

See https://radiant-rstats.github.io/docs/model/rforest.html for an example in Radiant

Value

A list with all variables defined in rforest as an object of class rforest

See Also

summary.rforest to summarize results

plot.rforest to plot results

predict.rforest for prediction

Examples

rforest(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>% summary()
rforest(titanic, "survived", c("pclass", "sex")) %>% str()
rforest(titanic, "survived", c("pclass", "sex"), max.depth = 1)
rforest(diamonds, "price", c("carat", "clarity"), type = "regression") %>% summary()

radiant.model documentation built on Oct. 16, 2023, 9:06 a.m.