EW_Fi_MLM_func: function to generate the expected fisher information at one...

In ForLion: 'ForLion' Algorithms to Find Optimal Experimental Designs with Mixed Factors

function to generate the expected fisher information at one design point xi for multinomial logit models

Description

function to generate the expected fisher information at one design point xi for multinomial logit models

Usage

EW_Fi_MLM_func(X_x, bvec_matrix, link = "continuation")

Arguments

X_x	model matrix for a specific design point x_i, X_x=h.func(xi)
bvec_matrix	the matrix of the sampled parameter values of beta
link	multinomial logit model link function name "baseline", "cumulative", "adjacent", or"continuation", default to be "continuation"

Value

F_x The expected Fisher information matrix at x_i

EU_x E(U) matrix for calculation the expected of Fisher information matrix at x_i

Examples

link.temp = "continuation"
xi.temp=c(80)
hfunc.temp = function(y){
matrix(data=c(1,y,y*y,0,0,0,0,0,1,y,0,0,0,0,0), nrow=3, ncol=5, byrow=TRUE)
}
X_xtemp=hfunc.temp(xi.temp)
bvec_bootstrap<-matrix(c(-0.2401, -1.9292, -2.7851, -1.614,-1.162,
                         -0.0535, -0.0274, -0.0096,-0.0291, -0.04,
                          0.0004,  0.0003,  0.0002,  0.0003,  0.1,
                         -9.2154, -9.7576, -9.6818, -8.5139, -8.56),nrow=4,byrow=TRUE)
EW_Fi_MLM_func(X_x=X_xtemp, bvec_matrix=bvec_bootstrap, link=link.temp)

ForLion documentation built on June 10, 2025, 5:13 p.m.