make.randomized.dat

Randomize a science table to get observed outcomes and treatment assignment

Implements methods developed by Ding, Feller, and Miratrix (2016) <doi:10.1111/rssb.12124> <arXiv:1412.5000>,
and Ding, Feller, and Miratrix (2018) <doi:10.1080/01621459.2017.1407322> <arXiv:1605.06566>
for testing whether there is unexplained variation in treatment effects across observations, and for characterizing
the extent of the explained and unexplained variation in treatment effects. The package includes wrapper functions
implementing the proposed methods, as well as helper functions for analyzing and visualizing the results of the test.

Ben Fifield

hettx

Fisherian and Neymanian Methods for Detecting and Measuring
Treatment Effect Variation

Peng Ding

Avi Feller

Luke Miratrix

make.randomized.dat function

<dl><dt>n</dt>
<dd>Sample size</dd>
<dt>p</dt>
<dd>Proportion treated</dd>
<dt>science.table.generator</dt>
<dd>Data generator</dd>
<dt>include.POs</dt>
<dd>TRUE/FALSE. Keep POs</dd>
<dt>as.data.frame</dt>
<dd>TRUE/FALSE. Return as dataframe or as list of elements.</dd>
<dt>...</dt>
<dd>Additional to be passed to science.table.generator</dd></dl>

Arguments

Make fake data for simulations — make.randomized.dat

<dl>

<dt>n</dt>
<dd>Sample size</dd>


<dt>p</dt>
<dd>Proportion treated</dd>


<dt>science.table.generator</dt>
<dd>Data generator</dd>


<dt>include.POs</dt>
<dd>TRUE/FALSE. Keep POs</dd>


<dt>as.data.frame</dt>
<dd>TRUE/FALSE. Return as dataframe or as list of elements.</dd>


<dt>...</dt>
<dd>Additional to be passed to science.table.generator</dd>

</dl>

make.randomized.dat: Make fake data for simulations

Description

Usage

Value

Arguments