PIR_loglik

Calculates the log-likelihood of within-session PIR data

Tools for simulating data generated by direct observation
recording. Behavior streams are simulated based on an alternating renewal
process, given specified distributions of event durations and interim
times. Different procedures for recording data can then be applied to the
simulated behavior streams. Functions are provided for the following
recording methods: continuous duration recording, event counting, momentary
time sampling, partial interval recording, whole interval recording, and
augmented interval recording.

James Pustejovsky

ARPobservation

Tools for Simulating Direct Behavioral Observation Recording
Procedures Based on Alternating Renewal Processes

PIR_loglik function

<dl><dt>param</dt>
<dd>vector of parameter values logit(prevalence), log(incidence)</dd>
<dt>U</dt>
<dd>a vector containing interval-level PIR data</dd>
<dt>c</dt>
<dd>the length of the active interval</dd>
<dt>d</dt>
<dd>the length of the recording interval
The vector <code>U</code> should only contain values of 1 or 0.
<code>c</code> must be a positive value. <code>d</code> must be some non-negative value. Setting 
<code>d</code> equal to zero represents a PIR observation where no time was set aside for recording.</dd></dl>

Arguments

Daniel Swan &lt;dswan@utexas.edu&gt;

Author

Calculate log-likelihood — PIR_loglik

<dl>

<dt>param</dt>
<dd>vector of parameter values logit(prevalence), log(incidence)</dd>


<dt>U</dt>
<dd>a vector containing interval-level PIR data</dd>


<dt>c</dt>
<dd>the length of the active interval</dd>


<dt>d</dt>
<dd>the length of the recording interval
The vector <code>U</code> should only contain values of 1 or 0.
<code>c</code> must be a positive value. <code>d</code> must be some non-negative value. Setting 
<code>d</code> equal to zero represents a PIR observation where no time was set aside for recording.</dd>

</dl>

PIR_loglik: Calculate log-likelihood

Description

Usage

Value

Arguments

Author