stochdom_Ab

Provides the necessary linear equality constraints to test stochastic dominance
of continuous distributions, that is, whether the cumulative density functions
\(F\) satisfy the constraint \(F_1(t) &lt; F_2(t)\) for all \(t\).

Implements Gibbs sampling and Bayes factors for multinomial models with
linear inequality constraints on the vector of probability parameters. As
special cases, the model class includes models that predict a linear order
of binomial probabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture models
assuming that the parameter vector p must be inside the convex hull of a
finite number of predicted patterns (i.e., vertices). A formal definition of
inequality-constrained multinomial models and the implemented computational
methods is provided in: Heck, D.W., & Davis-Stober, C.P. (2019).
Multinomial models with linear inequality constraints: Overview and improvements
of computational methods for Bayesian inference. Journal of Mathematical
Psychology, 91, 70-87. <doi:10.1016/j.jmp.2019.03.004>.
Inequality-constrained multinomial models have applications in the area of
judgment and decision making to fit and test random utility models
(Regenwetter, M., Dana, J., & Davis-Stober, C.P. (2011). Transitivity of
preferences. Psychological Review, 118, 42–56, <doi:10.1037/a0021150>) or to
perform outcome-based strategy classification to select the decision strategy
that provides the best account for a vector of observed choice frequencies
(Heck, D.W., Hilbig, B.E., & Moshagen, M. (2017). From information
processing to decisions: Formalizing and comparing probabilistic choice models.
Cognitive Psychology, 96, 26–40. <doi:10.1016/j.cogpsych.2017.05.003>).

Daniel W. Heck

multinomineq

Bayesian Inference for Multinomial Models with Inequality
Constraints

stochdom_Ab function

<dl><dt>bins</dt>
<dd>number of bins of histogram</dd>
<dt>conditions</dt>
<dd>number of conditions</dd>
<dt>order</dt>
<dd>order constraint on the random variables across conditions.
The default <code>order="&lt;"</code> implies that the random variables increase across
conditions (implying that the cdfs decrease: \(F_1(t) &gt; F_2(t)\)).</dd></dl>

Arguments

Ab-Representation for Stochastic Dominance of Histogram Bins — stochdom_Ab

<dl>

<dt>bins</dt>
<dd>number of bins of histogram</dd>


<dt>conditions</dt>
<dd>number of conditions</dd>


<dt>order</dt>
<dd>order constraint on the random variables across conditions.
The default <code>order="&lt;"</code> implies that the random variables increase across
conditions (implying that the cdfs decrease: \(F_1(t) &gt; F_2(t)\)).</dd>

</dl>

stochdom_Ab: Ab-Representation for Stochastic Dominance of Histogram Bins

Description

Usage

Arguments

References

See Also

Examples