nu2_log_self

function to calculate the second derivative of nu function given eta for log link

Designing experimental plans that involve both discrete and continuous factors with general parametric statistical models using the 'ForLion' algorithm and 'EW ForLion' algorithm. The algorithms will search for locally optimal designs and EW optimal designs under the D-criterion. Reference: Huang, Y., Li, K., Mandal, A., & Yang, J., (2024)<doi:10.1007/s11222-024-10465-x>.

Siting Lin

ForLion

'ForLion' Algorithm to Find D-Optimal Designs for Experiments

Yifei Huang

Jie Yang

nu2_log_self function

<dl><dt>x</dt>
<dd>vector of eta, eta=X*beta</dd></dl>

Arguments

function to calculate the second derivative of nu function given eta for log link — nu2_log_self

<dl>

<dt>x</dt>
<dd>vector of eta, eta=X*beta</dd>

</dl>

nu2_log_self: function to calculate the second derivative of nu function given eta for log link

Description

Usage

Value

Arguments

Examples