.randomize_internal

This function takes a data frame and adds a new column named "treated" with
randomly assigned TRUE/FALSE values. Randomization can be done either on
the entire data frame or stratified by specified columns. The probability of
being assigned to the treatment group can be specified, with a
default of 0.5.

A toolkit for causal inference in experimental and observational
studies. Implements various simple Bayesian models including linear,
negative binomial, and logistic regression for impact estimation.
Provides functionality for randomization and checking baseline equivalence
in experimental designs. The package aims to simplify the process of
impact measurement for researchers and analysts across different fields.
Examples and detailed usage instructions are available at
<https://book.martinez.fyi>.

Ignacio Martinez

Impact Measurement Toolkit

.randomize_internal function

<dl><dt>data</dt>
<dd>The input data frame.</dd>
<dt>group_by</dt>
<dd>(Optional) A character vector of column names to stratify the
randomization. If provided, the randomization will be done within
d each groupefined by the specified columns.</dd>
<dt>seed</dt>
<dd>(Optional) An integer to set the random seed for reproducibility.</dd>
<dt>pr_treated</dt>
<dd>(Optional) The probability of a row being assigned to the
treatment group (TRUE). Default is 0.5.</dd></dl>

Arguments

Add a Random Treatment Indicator Column to a Data Frame — .randomize_internal

<dl>

<dt>data</dt>
<dd>The input data frame.</dd>


<dt>group_by</dt>
<dd>(Optional) A character vector of column names to stratify the
randomization. If provided, the randomization will be done within
d each groupefined by the specified columns.</dd>


<dt>seed</dt>
<dd>(Optional) An integer to set the random seed for reproducibility.</dd>


<dt>pr_treated</dt>
<dd>(Optional) The probability of a row being assigned to the
treatment group (TRUE). Default is 0.5.</dd>

</dl>

.randomize_internal: Add a Random Treatment Indicator Column to a Data Frame

Description

Usage

Value

Arguments