PIR_MOM: Moment estimator for prevalence and incidence, with bootstrap...

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

Moment estimator for prevalence and incidence, with bootstrap confidence intervals

Description

Estimates prevalence and incidence for two samples, along with the ratios of each parameter, assuming that the behavior follows an '. Also provides bootstrap confidence intervals.

Usage

PIR_MOM(
  PIR,
  phase,
  base_level,
  intervals,
  interval_length,
  rest_length = 0,
  Bootstraps = 2000,
  conf_level = 0.95,
  exponentiate = FALSE,
  seed = NULL
)

Arguments

PIR	vector of PIR measurements
phase	factor or vector indicating levels of the PIR measurements.
base_level	a character string or value indicating the name of the baseline level.
intervals	the number of intervals in the sample of observations
interval_length	the total length of each interval
rest_length	length of the portion of the interval devoted to recording. Default is 0
Bootstraps	desired number of bootstrap replicates. Default is 2000
conf_level	Desired coverage rate of the calculated confidence interval. Default is .95.
exponentiate	a logical indicating whether the row corresponding to the ratio of treatment to baseline should be exponentiated, with the default as FALSE.
seed	seed value set in order to make bootstrap results reproducible. Default is null

Details

The moment estimators are based on the assumption that the underlying behavior stream follows an Alternating Poisson Process, in which both the event durations and interim times are exponentially distributed.

The PIR vector can be in any order corresponding to the factor or vector phase. The levels of phase can be any two levels, such as "A" and "B", "base" and "treat", or "0" and "1". If there are more than two levels in phase this function will not work. A value for base_level must be specified - if it is a character string it is case sensitive.

intervals, interval_length, and rest_length are all single values that are assumed to be held constant across both samples and all observation sessions. If vectors of values are provided for these variables, it is assumed that the first value in each vector is constant across all observations.

interval_length This is the total length of each individual interval. Sometimes a portion of the interval is set aside for recording purposes, in which case rest_length should be set to the length of time devoted to recording. The default assumption is that there is no recording time. The length of time devoted to active observation is calculated as interval_length - rest_length.

At the default setting of bootstraps = 2000, PIR_MOM takes just under six seconds to run on an Intel Core i5-2410M processor.

Value

A dataframe with six columns and three rows corresponding to baseline, treatment, and the log ratio or ratio (depending upon the value of exponentiate) of treatment to baseline

Author(s)

Daniel Swan <[e-mail]>

Examples

# Estimate prevalence and incidence ratios for Carl from the Moes dataset
data(Moes)
with(subset(Moes, Case == "Carl"),
  PIR_MOM(PIR = outcome,
          phase = Phase,
          intervals = intervals,
          interval_length = (active_length + rest_length),
          rest_length = rest_length,
          base_level = "No Choice",
          Bootstraps = 200,
          seed = 149568373))

ARPobservation documentation built on Aug. 25, 2023, 5:19 p.m.