MultiMethodMLEstimate-class

S4 object containing the results of multi-method ML accuracy estimates

Produce maximum likelihood estimates of common accuracy statistics
for multiple measurement methods when a gold standard is not available. An R
implementation of the expectation maximization algorithms described in Zhou et
al. (2011) <doi:10.1002/9780470906514> with additional functions for creating
simulated data and visualizing results. Supports binary, ordinal, and
continuous measurement methods.

Corie Drake

emery

Accuracy Statistic Estimation for Imperfect Gold Standards

MultiMethodMLEstimate-class function

<dl>
<dt><code>results</code></dt>
<dd>a list of estimated accuracy statistics</dd></dl>
<dt><code>names</code></dt>
<dd>a list containing vectors of names of various dimensions</dd>
<dt><code>data</code></dt>
<dd>a copy of the data used to generate the estimated values</dd>
<dt><code>iter</code></dt>
<dd>an integer number of iterations needed for the EM algorithm to converge</dd>
<dt><code>prog</code></dt>
<dd>a list containing the values calculated during each iteration of
the EM algorithm</dd>
<dt><code>type</code></dt>
<dd>a string describing the data type</dd>

Slots

S4 object containing the results of multi-method ML accuracy estimates — MultiMethodMLEstimate-class

<dl>
<dt><code>results</code></dt>
<dd>a list of estimated accuracy statistics</dd>


<dt><code>names</code></dt>
<dd>a list containing vectors of names of various dimensions</dd>


<dt><code>data</code></dt>
<dd>a copy of the data used to generate the estimated values</dd>


<dt><code>iter</code></dt>
<dd>an integer number of iterations needed for the EM algorithm to converge</dd>


<dt><code>prog</code></dt>
<dd>a list containing the values calculated during each iteration of
the EM algorithm</dd>


<dt><code>type</code></dt>
<dd>a string describing the data type</dd>


</dl>

MultiMethodMLEstimate-class: S4 object containing the results of multi-method ML accuracy estimates

Description

Arguments

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