EW_Xw_maineffects_self: function for calculating X=h(x) and E_w=E(nu(beta^T h(x)))...

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

function for calculating X=h(x) and E_w=E(nu(beta^T h(x))) give a design point x=(1,x1,...,xd)^T

Description

function for calculating X=h(x) and E_w=E(nu(beta^T h(x))) give a design point x=(1,x1,...,xd)^T

Usage

EW_Xw_maineffects_self(
  x,
  joint_Func_b,
  Lowerbounds,
  Upperbounds,
  link = "logit",
  h.func = NULL
)

Arguments

x	x=(x1,...,xd) – design point/experimental setting
joint_Func_b	The prior joint probability distribution of the parameters
Lowerbounds	The lower limit of the prior distribution for each parameter
Upperbounds	The upper limit of the prior distribution for each parameter
link	link = "logit" – link function, default: "logit", other links: "probit", "cloglog", "loglog", "cauchit", "log"
h.func	function h(x)=(h1(x),...,hp(x)), default (1,x1,...,xd)

Value

X=h(x)=(h1(x),...,hp(x)) – a row for design matrix

E_w – E(nu(b^t h(x)))

link – link function applied

Examples

hfunc.temp = function(y) {c(y,1);};   # y -> h(y)=(y1,y2,y3,1)
link.temp="logit"
paras_lowerbound<-rep(-Inf, 4)
paras_upperbound<-rep(Inf, 4)
gjoint_b<- function(x) {
mu1 <- -0.5; sigma1 <- 1
mu2 <- 0.5; sigma2 <- 1
mu3 <- 1; sigma3 <- 1
mu0 <- 1; sigma0 <- 1
d1 <- stats::dnorm(x[1], mean = mu1, sd = sigma1)
d2 <- stats::dnorm(x[2], mean = mu2, sd = sigma2)
d3 <- stats::dnorm(x[3], mean = mu3, sd = sigma3)
d4 <- stats::dnorm(x[4], mean = mu0, sd = sigma0)
return(d1 * d2 * d3 * d4)
}
x.temp = c(2,1,3)
EW_Xw_maineffects_self(x=x.temp,joint_Func_b=gjoint_b, Lowerbounds=paras_lowerbound,
 Upperbounds=paras_upperbound, link=link.temp, h.func=hfunc.temp)

ForLion documentation built on April 11, 2025, 5:38 p.m.