qp_solver_DEoptim

This function solves a quadratic programming problem using the Differential Evolution optimization method from the <code>DEoptim</code> package.

Provides functions to calculate the minimum and maximum possible
values of Cronbach's alpha when item-level missing data are present.
Cronbach's alpha (Cronbach, 1951 <doi:10.1007/BF02310555>) is one of the most widely used
measures of internal consistency in the social, behavioral, and medical sciences
(Bland & Altman, 1997 <doi:10.1136/bmj.314.7080.572>; Tavakol & Dennick, 2011
<doi:10.5116/ijme.4dfb.8dfd>). However, conventional implementations assume
complete data, and listwise deletion is often applied when missingness occurs,
which can lead to biased or overly optimistic reliability estimates (Enders, 2003
<doi:10.1037/1082-989X.8.3.322>). This package implements computational strategies
including enumeration, Monte Carlo sampling, and optimization algorithms
(e.g., Genetic Algorithm, Differential Evolution, Sequential Least Squares
Programming) to obtain sharp lower and upper bounds of Cronbach's alpha under
arbitrary missing data patterns. The approach is motivated by Manski's partial
identification framework and pessimistic bounding ideas from optimization literature.

Biying Zhou

missalpha

Find Range of Cronbach Alpha with a Dataset Including Missing
Data

Feng Ji

qp_solver_DEoptim function

<dl><dt>n</dt>
<dd>An integer representing the number of decision variables.</dd>
<dt>A</dt>
<dd>A matrix representing the quadratic coefficients.</dd>
<dt>b</dt>
<dd>A numeric vector representing the linear coefficients.</dd>
<dt>c</dt>
<dd>A numeric scalar representing the constant term in the objective function.</dd>
<dt>x_max</dt>
<dd>An integer representing the upper bound for the decision variables. Default is 1.</dd>
<dt>print_message</dt>
<dd>A logical value indicating whether to print optimization details. Default is FALSE.</dd>
<dt>NP</dt>
<dd>An integer specifying the population size for the DEoptim algorithm. Default is 50.</dd>
<dt>itermax</dt>
<dd>An integer specifying the maximum number of iterations. Default is 100.</dd>
<dt>...</dt>
<dd>Additional control parameters for <code>DEoptim</code>.</dd></dl>

Arguments

Solve Quadratic Programming Problem using DEoptim — qp_solver_DEoptim

<dl>

<dt>n</dt>
<dd>An integer representing the number of decision variables.</dd>


<dt>A</dt>
<dd>A matrix representing the quadratic coefficients.</dd>


<dt>b</dt>
<dd>A numeric vector representing the linear coefficients.</dd>


<dt>c</dt>
<dd>A numeric scalar representing the constant term in the objective function.</dd>


<dt>x_max</dt>
<dd>An integer representing the upper bound for the decision variables. Default is 1.</dd>


<dt>print_message</dt>
<dd>A logical value indicating whether to print optimization details. Default is FALSE.</dd>


<dt>NP</dt>
<dd>An integer specifying the population size for the DEoptim algorithm. Default is 50.</dd>


<dt>itermax</dt>
<dd>An integer specifying the maximum number of iterations. Default is 100.</dd>


<dt>...</dt>
<dd>Additional control parameters for <code>DEoptim</code>.</dd>

</dl>

qp_solver_DEoptim: Solve Quadratic Programming Problem using DEoptim

Description

Usage

Value

Arguments

See Also