minExactMatch: Find the minimal exact match factors that will be feasible...

In jgellar/GroupMatch: GroupMatch: Optimal Matching under Rolling Enrollment

Find the minimal exact match factors that will be feasible for a given maximum problem size.

Description

The exactMatch function creates a smaller matching problem by stratifying observations into smaller groups. For a problem that is larger than maximum allowed size, minExactMatch provides a way to find the smallest exact matching problem that will allow for matching.

Usage

minExactMatch(x, scores = NULL, width = NULL, maxarcs = 1e+07, ...)

Arguments

x	The object for dispatching.
scores	Optional vector of scores that will be checked against a caliper width.
width	Optional width of a caliper to place on the scores.
maxarcs	The maximum problem size to attempt to fit.
...	Additional arguments for methods.

Details

x is a formula of the form Z ~ X1 + X2, where Z is indicates treatment or control status, and X1 and X2 are variables can be converted to factors. Any additional arguments are passed to model.frame (e.g., a data argument containing Z, X1, and X2).

The the arguments scores and width must be passed together. The function will apply the caliper implied by the scores and the width while also adding in blocking factors.

Value

A factor grouping units, suitable for exactMatch.

jgellar/GroupMatch documentation built on Nov. 8, 2022, 10:48 p.m.