ForLion (version 0.1.0)

EW_Xw_maineffects_self: 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
)

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

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)

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)