drop_fixed

add_fixed

Switches between two representation of polytopes for multinomial probabilities
(whether the fixed parameters are included).

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

drop_fixed function

<dl><dt>x</dt>
<dd>a vector (typically <code>k</code>, <code>n</code>, or <code>prior</code>) or
a matrix (typically <code>A</code> or <code>V</code>), in which case the fixed dimensions
are dropped/added column-wise.</dd>
<dt>options</dt>
<dd>number of observable categories/probabilities for each item
type/multinomial distribution, e.g., <code>c(3,2)</code> for a ternary and binary item.</dd>
<dt>sum</dt>
<dd>a vector that determines the fixed sum in each multinomial condition.
By default, probabilities are assumed that sum to one.
If frequencies <code>n</code> are provided, use <code>sum=n</code>.</dd></dl>

Arguments

Drop or Add Fixed Dimensions for Multinomial Probabilities/Frequencies — drop_fixed

<dl>

<dt>x</dt>
<dd>a vector (typically <code>k</code>, <code>n</code>, or <code>prior</code>) or
a matrix (typically <code>A</code> or <code>V</code>), in which case the fixed dimensions
are dropped/added column-wise.</dd>


<dt>options</dt>
<dd>number of observable categories/probabilities for each item
type/multinomial distribution, e.g., <code>c(3,2)</code> for a ternary and binary item.</dd>


<dt>sum</dt>
<dd>a vector that determines the fixed sum in each multinomial condition.
By default, probabilities are assumed that sum to one.
If frequencies <code>n</code> are provided, use <code>sum=n</code>.</dd>

</dl>

drop_fixed: Drop or Add Fixed Dimensions for Multinomial Probabilities/Frequencies

Description

Usage

Arguments

Examples