approxtoexact_func

Convert the approximate allocation (proportion) to exact allocation (integer) without constraint

In the context of paid research studies and clinical trials, budget considerations and patient sampling from available populations are subject to inherent constraints. We introduce the 'CDsampling' package, which integrates optimal design theories within the framework of constrained sampling. This package offers the possibility to find both D-optimal approximate and exact allocations for samplings with or without constraints. Additionally, it provides functions to find constrained uniform sampling as a robust sampling strategy with limited model information. Our package offers functions for the computation of the Fisher information matrix under generalized linear models (including regular linear regression model) and multinomial logistic models.To demonstrate the applications, we also provide a simulated dataset and a real dataset embedded in the package. Yifei Huang, Liping Tong, and Jie Yang (2025)<doi:10.5705/ss.202022.0414>.

Yifei Huang

CDsampling

Constrained Sampling in Paid Research Studies

Liping Tong

Jie Yang

approxtoexact_func function

<dl><dt>n</dt>
<dd>Sample size, must be a positive integer</dd>
<dt>w</dt>
<dd>Approximate allocation/proportion, must be a real-valued vector, can get from running liftone_constrained_GLM or liftone_constrained_MLM</dd></dl>

Arguments

Convert the approximate allocation (proportion) to exact allocation (integer) without constraint — approxtoexact_func

<dl>

<dt>n</dt>
<dd>Sample size, must be a positive integer</dd>


<dt>w</dt>
<dd>Approximate allocation/proportion, must be a real-valued vector, can get from running liftone_constrained_GLM or liftone_constrained_MLM</dd>

</dl>

approxtoexact_func: Convert the approximate allocation (proportion) to exact allocation (integer) without constraint

Description

Usage

Value

Arguments

Examples