model_matrix

Grow a model matrix while at the same time compactly
encoding missingness patterns in RHS variables of a model frame.

internal

Tools for randomization-based inference. Current focus is on the d^2 omnibus test of differences of means following Hansen and Bowers (2008) <doi:10.1214/08-STS254> . This test is useful for assessing balance in matched observational studies or for analysis of outcomes in block-randomized experiments.

Jake Bowers

RItools

Randomization Inference Tools

Mark Fredrickson

Ben Hansen

Josh Errickson

model_matrix function

<dl><dt>object</dt>
<dd>Model formula or terms object (as in `model.matrix`)</dd>
<dt>data</dt>
<dd>data.frame, as in `model.matrix()` but has to have &#8216;<code>(weights)</code>&#8217; column</dd>
<dt>remove.intercept</dt>
<dd>logical</dd>
<dt>...</dt>
<dd>passed to `model.matrix.default` (and further)</dd></dl>

Arguments

Author

Model matrices along with compact encodings of data availability/missingness — model_matrix

<dl>

<dt>object</dt>
<dd>Model formula or terms object (as in `model.matrix`)</dd>


<dt>data</dt>
<dd>data.frame, as in `model.matrix()` but has to have &#8216;<code>(weights)</code>&#8217; column</dd>


<dt>remove.intercept</dt>
<dd>logical</dd>


<dt>...</dt>
<dd>passed to `model.matrix.default` (and further)</dd>

</dl>

model_matrix: Model matrices along with compact encodings of data availability/missingness

Description

Usage

Value

Arguments

Author