ForLion (version 0.1.0)

liftoneDoptimal_MLM_func: function of liftone for multinomial logit model

Description

function of liftone for multinomial logit model

Usage

liftoneDoptimal_MLM_func(
  m,
  p,
  Xi,
  J,
  thetavec,
  link = "continuation",
  reltol = 1e-05,
  maxit = 500,
  p00 = NULL,
  random = FALSE,
  nram = 3
)

Value

p reported D-optimal approximate allocation

p0 the initial approximate allocation that derived the reported D-optimal design

Maximum the maximum of the determinant of the Fisher information matrix

Convergence TRUE or FALSE, whether the algorithm converges

itmax, maximum iterations

Arguments

m

number of design points

p

number of parameters in the multinomial logit model

Xi

model matrix

J

number of response levels in the multinomial logit model

thetavec

model parameter

link

multinomial logit model link function name "baseline", "cumulative", "adjacent", or"continuation", default to be "continuation"

reltol

relative tolerance for convergence, default to 1e-5

maxit

the number of maximum iteration, default to 500

p00

specified initial approximate allocation, default to NULL, if NULL, will generate a random initial approximate allocation

random

TRUE or FALSE, if TRUE then the function will run with additional "nram" number of initial allocation p00, default to be TRUE

nram

when random == TRUE, the function will generate nram number of initial points, default is 3

Examples

m=5
p=10
J=5
factor_x = matrix(c(-1,-25,199.96,-150,-100,16,1,23.14,196.35,0,-100,
16,1,-24.99,199.99,-150,0,16,-1,25,-200,0,0,16,-1,-25,-200,-150,0,16),ncol=6,byrow=TRUE)
Xi=rep(0,J*p*m); dim(Xi)=c(J,p,m)
hfunc.temp = function(y){
if(length(y) != 6){stop("Input should have length 6");}
 model.mat = matrix(NA, nrow=5, ncol=10, byrow=TRUE)
 model.mat[5,]=0
 model.mat[1:4,1:4] = diag(4)
 model.mat[1:4, 5] =((-1)*y[6])
 model.mat[1:4, 6:10] = matrix(((-1)*y[1:5]), nrow=4, ncol=5, byrow=TRUE)
 return(model.mat)
 }
for(i in 1:m) {
Xi[,,i]=hfunc.temp(factor_x[i,])
}
thetavec=c(-1.77994301, -0.05287782,  1.86852211, 2.76330779, -0.94437464, 0.18504420,
-0.01638597, -0.03543202, -0.07060306, 0.10347917)
liftoneDoptimal_MLM_func(m=m,p=p,Xi=Xi,J=J,thetavec=thetavec,
link="cumulative",p00=rep(1/5,5), random=FALSE)