calculateEffectSizes: Calculate Effect Sizes for Treatment vs. Control

In imt: Impact Measurement Toolkit

Calculate Effect Sizes for Treatment vs. Control

Description

This function calculates effect sizes for each variable in a data frame,  comparing treatment and control groups. It handles continuous, binary,  and categorical variables using appropriate effect size measures.

Usage

calculateEffectSizes(data, treatment_column, to_check = NULL)

Arguments

data	A data frame containing the variables and treatment/control indicator.
treatment_column	The name of the column (as a string) in the data frame that    indicates whether an observation is in the treatment or control group. This column    must be a factor with exactly two levels.
to_check	(optional) A character vector specifying the names of the variables for which effect sizes should be calculated. If NULL (default), all variables (except the treatment column) are processed.

Details

- For continuous variables, Hedges' g effect size is calculated.   - For binary variables, Cox's Proportional Hazards Index (Cox's C) is calculated.   - For categorical variables, the variable is converted into multiple indicator     (dummy) variables, and the average Cox's C across these indicators is reported.

Value

A data frame with two columns:   * Variable: The name of each variable in the original data frame.   * EffectSize: The calculated effect size for each variable.

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