plot,MultiMethodMLEstimate-method

Create a list of plots visualizing the expectation maximization process and
resulting accuracy statistics stored in a MultiMethodMLEstimate object.

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

plot,MultiMethodMLEstimate-method function

<dl><dt>x</dt>
<dd>a MultiMethodMLEstimate S4 object</dd>
<dt>y</dt>
<dd>not used</dd>
<dt>...</dt>
<dd>Arguments passed on to <code>plot_ML</code><dl>
 <dt><code>params</code></dt>
<dd>A list of population parameters. This is primarily used to evaluate
results from a simulation where the target parameters are known, but can be used to visualize
results with respect to some True value.</dd></dl></dd> 
</dl>

Arguments

Create plots from a MultiMethodMLEstimate object — plot,MultiMethodMLEstimate-method

<dl>

<dt>x</dt>
<dd>a MultiMethodMLEstimate S4 object</dd>


<dt>y</dt>
<dd>not used</dd>


<dt>...</dt>
<dd>Arguments passed on to <code>plot_ML</code><dl>
 <dt><code>params</code></dt>
<dd>A list of population parameters. This is primarily used to evaluate
results from a simulation where the target parameters are known, but can be used to visualize
results with respect to some True value.</dd>

 
</dl></dd>

</dl>

plot,MultiMethodMLEstimate-method: Create plots from a MultiMethodMLEstimate object

Description

Usage

Value

Arguments