.hurdle_prepare_data

Internal function to prepare data structures for TMB hurdle model fitting.
Converts subject IDs to 0-indexed integers and creates derived variables.

internal

Facilitates many of the analyses performed in studies of
behavioral economic demand. The package supports commonly-used options for
modeling operant demand including (1) data screening proposed by Stein,
Koffarnus, Snider, Quisenberry, & Bickel (2015; <doi:10.1037/pha0000020>),
(2) fitting models of demand such as linear (Hursh, Raslear, Bauman,
& Black, 1989, <doi:10.1007/978-94-009-2470-3_22>), exponential (Hursh & Silberberg, 2008,
<doi:10.1037/0033-295X.115.1.186>) and modified exponential (Koffarnus,
Franck, Stein, & Bickel, 2015, <doi:10.1037/pha0000045>), and (3) calculating
numerous measures relevant to applied behavioral economists (Intensity,
Pmax, Omax). Also supports plotting and comparing data.

Brent Kaplan

beezdemand

Behavioral Economic Easy Demand

Shawn Gilroy

.hurdle_prepare_data function

<dl><dt>data</dt>
<dd>A validated data frame.</dd>
<dt>y_var</dt>
<dd>Character string, name of consumption variable.</dd>
<dt>x_var</dt>
<dd>Character string, name of price variable.</dd>
<dt>id_var</dt>
<dd>Character string, name of subject ID variable.</dd></dl>

Arguments

Prepare Hurdle Model Data — .hurdle_prepare_data

<dl>

<dt>data</dt>
<dd>A validated data frame.</dd>


<dt>y_var</dt>
<dd>Character string, name of consumption variable.</dd>


<dt>x_var</dt>
<dd>Character string, name of price variable.</dd>


<dt>id_var</dt>
<dd>Character string, name of subject ID variable.</dd>

</dl>

.hurdle_prepare_data: Prepare Hurdle Model Data

Description

Usage

Value

Arguments