rcRF: Constructs rcRF model

In kdoub5ha/rcITR: Risk Controlled ITR Discovery

Description

Constructs a risk controlled random forest (rcRF) composed of rcDT predictors.

Usage

rcRF(data, split.var, efficacy = "y", risk = "r", col.trt = "trt",
  col.prtx = "prtx", risk.control = TRUE, risk.threshold = NA,
  lambda = 0, stabilize = TRUE, stabilize.type = c("linear", "rf"),
  test = NULL, ctg = NULL, N0 = 20, n0 = 5, max.depth = 10,
  ntree = 500, mtry = max(floor(length(split.var)/3), 1),
  avoid.nul.tree = FALSE, AIPWE = FALSE, verbose = FALSE,
  use.other.nodes = TRUE, extremeRandomized = FALSE, importance = FALSE,
  order.importances = TRUE)

Arguments

data	data.frame. Data used to construct rcRF model. Must contain efficacy variable (y), risk variable (r), binary treatment indicator coded as 0 / 1 (trt), propensity score (prtx), candidate splitting covariates.
split.var	numeric vector. Columns of spliting variables.
efficacy	char. Efficacy outcome column. Defaults to 'y'.
risk	char. Risk outcome column. Defaults to 'r'.
col.trt	char. Treatment column name
risk.control	logical. Should risk be controlled? Defaults to TRUE.
risk.threshold	numeric. Desired level of risk control.
lambda	numeric. Penalty parameter for risk scores. Defaults to 0, i.e. no constraint. Optional arguments
stabilize	logical indicating if efficacy should be modeled using residuals. Defaults to TRUE.
stabilize.type	character specifying method used for estimating residuals. Current options are 'linear' for linear model (default) and 'rf' for random forest.
test	data.frame of testing observations. Should be formatted the same as 'data'.
ctg	numeric vector corresponding to the categorical input columns. Defaults to NULL. Not available yet.
N0	numeric specifying minimum number of observations required to call a node terminal. Defaults to 20.
n0	numeric specifying minimum number of treatment/control observations needed in a split to declare a node terminal. Defaults to 5.
max.depth	numeric specifying maximum depth of the tree. Defaults to 15 levels.
ntree	numeric. Number of trees generated. Defaults to 500.
mtry	numeric specifying the number of randomly selected splitting variables to be included. Defaults to larger of 1 and length(split.var)/3.
avoid.nul.tree	logical. Should null trees be discarded?
AIPWE	logical. Should AIPWE (TRUE) or IPWE (FALSE) be used. Not available yet.
verbose	logical. Give updates about forest progression?
use.other.nodes	logical. Should global estimator of objective function be used. Defaults to TRUE.
extremeRandomized	logical. Experimental for randomly selecting cutpoints in a random forest model. Defaults to FALSE and users should change this at their own peril.
importance	logical. Indicated if variable importance measures should be estimated and returned. Defaults to FALSE.
order.importances	logical. Should importances be ordered (if requested)?
col.ptrx	char. Propensity score column name.

Value

List of rcRF outputs

ID.Boots.Samples	list of bootstrap sample IDs
TREES	list of trees
Model.Specification	information about the input parameters of the forest
...	Summaries for in and out of bag samples

Examples

set.seed(123)
dat <- generateData()
# Generates rcRF model using simualated data with splitting variables located in columns 1-10.
fit <- rcRF(data = dat, 
            split.var = 1:10, 
            ntree = 200,
            risk.threshold = 2.75, 
            lambda = 1)
            

kdoub5ha/rcITR documentation built on Aug. 5, 2020, 9:05 p.m.