randomizer: Randomization Class for Treatment Assignment

In imt: Impact Measurement Toolkit

Randomization Class for Treatment Assignment

Description

This class provides methods to randomly assign treatments to a dataset while ensuring baseline covariate balance. It can handle both simple and stratified randomization.

Active bindings

version

The version of the im package used for randomization.

data

The data frame with the assigned treatment.

seed

The random seed used for reproducibility.

balance_summary

A summary (or list of summaries) of the balance assessment after randomization.

balance_plot

A plot (or list of plots) of the balance assessment after randomization.

Methods

Public methods

• randomizer$new()

• randomizer$clone()

---

Method new()

Initialize a new Randomizer object.

Usage

randomizer$new(
  data,
  variables,
  standard = "Not Concerned",
  seed = NULL,
  max_attempts = 100,
  group_by = NULL
)

Arguments

data

The input data frame.

variables

A vector of covariate names to check for balance.

standard

The desired level of baseline equivalence. Must be one of "Not Concerned", "Concerned", or "Very Concerned". Default is "Not Concerned". ("Not Concerned", "Concerned", or "Very Concerned").

seed

(Optional) An integer to set the random seed.

max_attempts

(Optional) Maximum number of randomization attempts.

group_by

(Optional) A character vector of column names to stratify randomization.

Returns

A new randomizer object.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

randomizer$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

imt documentation built on Sept. 11, 2024, 5:41 p.m.