random_start

random_start_binary

random_start_ordinal

<code>random_start()</code> is a general function for creating a random, plausible starting point
for the EM algorithm to begin iterating from. Varying the starting points of repeated
calculations can help the researcher detect local extremes.
Creates random initial sensitivity and specificity values for <code>n_method</code> methods.
Values are generated from a random beta distribution with shape parameters
<code>a=3</code> and <code>b=1</code>. Prevalence is a random value from a uniform distribution.

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

random_start function

<dl><dt>type</dt>
<dd>A string specifying the data type of the methods being simulated.</dd>
<dt>n_method</dt>
<dd>An integer representing the number of methods to simulate.</dd>
<dt>method_names</dt>
<dd>Optional vector of names used to identify each method.</dd></dl>

Arguments

Generate a random initial starting point for EM algorithm — random_start

<dl>

<dt>type</dt>
<dd>A string specifying the data type of the methods being simulated.</dd>


<dt>n_method</dt>
<dd>An integer representing the number of methods to simulate.</dd>


<dt>method_names</dt>
<dd>Optional vector of names used to identify each method.</dd>

</dl>

random_start: Generate a random initial starting point for EM algorithm

Description

Usage

Value

Arguments