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))) given a design point x=(1,x1,...,xd)^T

Description

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

Usage

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

Arguments

x	x=(x1,...,xd) – design point/experimental setting
Integral_based	TRUE or FALSE, if TRUE then we will find the integral-based EW D-optimality otherwise we will find the sample-based EW D-optimality
joint_Func_b	prior distribution function of model parameters
Lowerbounds	vector of lower ends of ranges of prior distribution for model parameters.
Upperbounds	vector of upper ends of ranges of prior distribution for model parameters.
b_matrix	matrix of bootstrapped or simulated parameter values.
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,Integral_based=TRUE,joint_Func_b=gjoint_b,
Lowerbounds=paras_lowerbound,Upperbounds=paras_upperbound, link=link.temp,
h.func=hfunc.temp)

ForLion documentation built on June 28, 2025, 1:09 a.m.