Fdet_func_GLM

Determinant of Fisher information matrix for GLM

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

Fdet_func_GLM function

<dl><dt>w</dt>
<dd>allocation (can be exact or approximate)</dd>
<dt>beta</dt>
<dd>GLM model covariate coefficient</dd>
<dt>X</dt>
<dd>model matrix</dd>
<dt>link</dt>
<dd>link function, default"logit", choose from "logit", "cloglog", "loglog", "probit", and "identity"(for regular linear regression)</dd></dl>

Arguments

Determinant of Fisher information matrix for GLM — Fdet_func_GLM

<dl>

<dt>w</dt>
<dd>allocation (can be exact or approximate)</dd>


<dt>beta</dt>
<dd>GLM model covariate coefficient</dd>


<dt>X</dt>
<dd>model matrix</dd>


<dt>link</dt>
<dd>link function, default"logit", choose from "logit", "cloglog", "loglog", "probit", and "identity"(for regular linear regression)</dd>

</dl>

Fdet_func_GLM: Determinant of Fisher information matrix for GLM

Description

Usage

Value

Arguments

Examples