graph

In discrimination experiments candidates are sent on the same test (e.g. job, house rental) and one examines whether they receive the same outcome. The number of non negative answers are first examined in details looking for outcome differences. Then various answering rates and their exacts confidence intervals are computed. Last, exact and asymptotic discrimination tests are performed. Graphical methods are also available.

Emmanuel Duguet

callback

Computes Statistics from Discrimination Experimental Data

David Gray

Loïc du Parquet

Yannick L'Horty

Rémi Le Gall

Noam Léandri

Pascale Petit

Florent Sari

graph function

<dl><dt>data</dt>
<dd>A <code>stat_glob</code>, <code>stat_mcr</code> or <code>stat_ecs</code> object.</dd>
<dt>method</dt>
<dd>the type of confidence interval: "cp" for Clopper-Pearson,
"wilson" for Wilson (the default), "student" for Student.</dd>
<dt>col</dt>
<dd>A list of colors.</dd>
<dt>...</dt>
<dd>further arguments passed to or from other methods.</dd></dl>

Arguments

Generic plot function — graph

<dl>

<dt>data</dt>
<dd>A <code>stat_glob</code>, <code>stat_mcr</code> or <code>stat_ecs</code> object.</dd>


<dt>method</dt>
<dd>the type of confidence interval: "cp" for Clopper-Pearson,
"wilson" for Wilson (the default), "student" for Student.</dd>


<dt>col</dt>
<dd>A list of colors.</dd>


<dt>...</dt>
<dd>further arguments passed to or from other methods.</dd>

</dl>

Generic plot function

graph: Generic plot function

Description

Usage

Arguments