Pillai_iterativ

Pillai's Trace with respect to sample size.

Balancing quasi-experimental field research for effects of covariates is fundamental for drawing causal inference. Propensity Score Matching deals with this issue but current
techniques are restricted to binary treatment variables. Moreover, they provide several solutions without providing a comprehensive framework on choosing the best model. The
MAGMA R-package addresses these restrictions by offering nearest neighbor matching for two to four groups. It also includes the option to match data of a 2x2 design. In addition,
MAGMA includes a framework for evaluating the post-matching balance. The package includes functions for the matching process and matching reporting. We provide a tutorial on
MAGMA as vignette. More information on MAGMA can be found in Feuchter, M. D., Urban, J., Scherrer V., Breit, M. L., and Preckel F. (2022) <https://osf.io/p47nc/>.

Julian Urban

MAGMA.R

MAny-Group MAtching

Markus D. Feuchter

Vsevolod Scherrer

Moritz L. Breit

Franzis Preckel

Pillai_iterativ function

<dl><dt>da</dt>
<dd>Specifying the data frame or tibble with the data.</dd>
<dt>gr</dt>
<dd>A character vector specifying the IVs.</dd>
<dt>co</dt>
<dd>A character vector naming the DVs.</dd>
<dt>st</dt>
<dd>A character naming the variable for iteratively inclusion</dd></dl>

Arguments

Author

Pillai_iterativ — Pillai_iterativ

<dl>

<dt>da</dt>
<dd>Specifying the data frame or tibble with the data.</dd>


<dt>gr</dt>
<dd>A character vector specifying the IVs.</dd>


<dt>co</dt>
<dd>A character vector naming the DVs.</dd>


<dt>st</dt>
<dd>A character naming the variable for iteratively inclusion</dd>

</dl>

Pillai_iterativ: Pillai_iterativ

Description

Usage

Value

Arguments

Author

Details