ForLion (version 0.3.0)

EW_Xw_maineffects_self: 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
)

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

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)

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)