treeCV: Cross Validation for Optimal rcDT Model Selection for Given...

In kdoub5ha/mvITR: Discovery of risk controlled individualized treatment rules (ITRs)

Description

Performs k-fold cross validation for rcDT model to select the best subtree from the set of optimally pruned subtree generated from 'prune' function.

Usage

treeCV(dat, split.var, N0 = 20, n0 = 5, efficacy = "y", risk = "r",
  col.trt = "trt", col.prtx = "prtx", lambda = 0, risk.control = FALSE,
  risk.threshold = NA, nfolds = 10, AIPWE = FALSE, sort = TRUE,
  ctgs = NA, stabilize.type = c("linear", "rf"), stabilize = TRUE,
  use.other.nodes = TRUE, use.bootstrap = FALSE,
  extremeRandomized = FALSE)

Arguments

dat	data.frame. Data used to construct rcDT 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.
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.
efficacy	char. Efficacy outcome column. Defaults to 'y'.
risk	char. Risk outcome column. Defaults to 'r'.
col.trt	char. Treatment indicator column name. Should be of form 0/1 or -1/+1.
col.prtx	char. Propensity score column name.
lambda	numeric. Penalty parameter for risk scores. Defaults to 0, i.e. no constraint.
risk.control	logical. Should risk be controlled? Defaults to TRUE.
risk.threshold	numeric. Desired level of risk control.
AIPWE	logical. Should AIPWE (TRUE) or IPWE (FALSE) be used. Not available yet.
sort	internal use.
stabilize.type	character specifying method used for estimating residuals. Current options are 'linear' for linear model (default) and 'rf' for random forest.
stabilize	logical indicating if efficacy should be modeled using residuals. Defaults to TRUE.
use.other.nodes	logical. Should global estimator of objective function be used. Defaults to TRUE.
use.bootstrap	logical. Should a bootstrap resampling be done? Defaults to FALSE.
extremeRandomized	logical. Experimental for randomly selecting cutpoints in a random forest model. Defaults to FALSE and users should change this at their own peril. #' @return A summary of the cross validation including optimal penalty parameter and the optimal model.
test	data.frame of testing observations. Should be formatted the same as 'data'.
max.depth	numeric specifying maximum depth of the tree. Defaults to 15 levels.
mtry	numeric specifying the number of randomly selected splitting variables to be included. Defaults to number of splitting variables.
ctg	numeric vector corresponding to the categorical input columns. Defaults to NULL. Not available yet.

Value

best.tree.size	optimal rcDT model based on size
best.tree.alpha	optimal rcDT model based on alpha parameter selection
best.alpha	optimal lambda parameter selected from the cross validation procedure
full.tree	unpruned tree
pruned.tree	output from pruning of 'full.tree'
data	input data
details	summary of model performance
subtrees	sequence of optimally pruned subtrees of 'full.tree'
in.train	training samples from splits
in.test	testing samples from splits

Examples

# Grow large tree
set.seed(1)
dat <- generateData()
fit <- treeCV(dat, split.var = 1:10, nfolds = 5, lambda = 1,
                 risk.control = TRUE, risk.threshold = 2.75)

kdoub5ha/mvITR documentation built on April 7, 2020, 3:59 a.m.