estimate_itr: Estimate individual treatment rules (ITR)

In evalITR: Evaluating Individualized Treatment Rules

Estimate individual treatment rules (ITR)

Description

Estimate individual treatment rules (ITR)

Usage

estimate_itr(
  treatment,
  form,
  data,
  algorithms,
  budget,
  n_folds = 5,
  split_ratio = 0,
  ngates = 5,
  preProcess = NULL,
  weights = NULL,
  trControl = caret::trainControl(method = "none"),
  tuneGrid = NULL,
  tuneLength = ifelse(trControl$method == "none", 1, 3),
  user_model = NULL,
  SL_library = NULL,
  ...
)

Arguments

treatment	Treatment variable
form	a formula object that takes the form y ~ T + x1 + x2 + ....
data	A data frame that contains the outcome y and the treatment T.
algorithms	List of machine learning algorithms to be used.
budget	The maximum percentage of population that can be treated under the budget constraint.
n_folds	Number of cross-validation folds. Default is 5.
split_ratio	Split ratio between train and test set under sample splitting. Default is 0.
ngates	The number of groups to separate the data into. The groups are determined by tau. Default is 5.
preProcess	caret parameter
weights	caret parameter
trControl	caret parameter
tuneGrid	caret parameter
tuneLength	caret parameter
user_model	A user-defined function to create an ITR. The function should take the data as input and return a model to estimate the ITR.
SL_library	A list of machine learning algorithms to be used in the super learner.
...	Additional arguments passed to caret::train

Value

An object of itr class

evalITR documentation built on Aug. 26, 2023, 1:08 a.m.