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discrepancyCriteria <- function(design, type='all'){
#---------------------------------------
# source code by Jessica FRANCO (2006.10.05)
# modified by Bertrand Iooss (2013.26.12)
#---------------------------------------
# inputs
# - design of experiments
# - type of dicrepancies to be computed
X <- as.matrix(design)
dimension <- dim(X)[2] # dimension
n <- dim(X)[1] # number of points
if ( n < dimension ){
stop('Warning : the number of points is lower than the dimension.')
}
# To check the experimental region
if ( min(X)<0 || max(X)>1 ){
warning("The design is rescaling into the unit cube [0,1]^d.")
M <- apply(X,2,max)
m <- apply(X,2,min)
for (j in 1:dim(X)[2]){
X[,j] <- (X[,j]-m[j])/(M[j]-m[j])
}
}
R <- list()
DisC2 <- FALSE
DisL2 <- FALSE
DisL2star <- FALSE
DisM2 <- FALSE
DisS2 <- FALSE
DisW2 <- FALSE
DisMix2 <- FALSE
if (length(type)==1 && type=='all'){
type <- c('C2','L2','L2star','M2','S2','W2','Mix2')
}
for(i in 1:length(type)){
type_ <- type[i]
switch(type_,
C2 = {DisC2 <- TRUE},
L2 = {DisL2 <- TRUE},
L2star = {DisL2star <- TRUE},
M2 = {DisM2 <- TRUE},
S2 = {DisS2 <- TRUE},
W2 = {DisW2 <- TRUE},
Mix2 = {DisMix2 <- TRUE})
}
# centered L2-discrepancy
#------------------------
if(DisC2 == TRUE){
s1 <- 0; s2 <- 0
for (i in 1:n){
p <- prod((1+0.5*abs(X[i,]-0.5)-0.5*((abs(X[i,]-0.5))^2)))
s1 <- s1+p
for (k in 1:n){
q <- prod((1+0.5*abs(X[i,]-0.5)+0.5*abs(X[k,]-0.5)-0.5*abs(X[i,]-X[k,])))
s2 <- s2+q
}
}
R <- c(R,DisC2 = sqrt(((13/12)^dimension)-((2/n)*s1) + ((1/n^2)*s2)))
}
# L2-discrepancy
#------------------------
if(DisL2 == TRUE){
s1 <- 0; s2 <- 0
for (i in 1:n){
p <- prod(X[i,]*(1-X[i,]))
s1 <- s1+p
for (k in 1:n){
q <- 1
for (j in 1:dimension){
q <- q*(1-max(X[i,j],X[k,j]))*min(X[i,j],X[k,j])
}
s2 <- s2+q
}
}
R <- c(R,DisL2 = sqrt(12^(-dimension) - (((2^(1-dimension))/n)*s1) + ((1/n^2)*s2)))
}
# L2star-discrepancy
#------------------------
if(DisL2star == TRUE){
dL2<-0
for (j in 1:n){
for (i in 1:n){
if(i!=j){
t<-c()
for (l in 1:dimension) t<-c(t,1-max(X[i,l],X[j,l]))
t<-(prod(t))/(n^2)
}
else{
t1<-1-X[i,]
t1<-prod(t1)
t2<-1-X[i,]^2
t2<-prod(t2)
t<-t1/(n^2)-((2^(1-dimension))/n)*t2
}
dL2<-dL2+t}
}
R <- c(R,DisL2star = sqrt(3^(-dimension)+dL2))
}
# modified L2-discrepancy
#------------------------
if(DisM2 == TRUE){
s1 <- 0; s2 <- 0
for (i in 1:n){
p <- 1
p <- prod((3-(X[i,]*X[i,])))
s1 <- s1+p
for (k in 1:n){
q <- 1
for (j in 1:dimension){
q <- q*(2-max(X[i,j],X[k,j]))
}
s2 <- s2+q
}
}
R <- c(R,DisM2 = sqrt(((4/3)^dimension) - (((2^(1-dimension))/n)*s1) + ((1/n^2)*s2)))
}
# symmetric L2-discrepancy
#------------------------
if(DisS2 == TRUE){
s1 <- 0; s2 <- 0
for (i in 1:n){
p <- prod((1+(2*X[i,])-(2*X[i,]*X[i,])))
s1 <- s1+p
for (k in 1:n){
q <- prod((1-abs(X[i,]-X[k,])))
s2 <- s2+q
}
}
R <- c(R,DisS2 = sqrt(((4/3)^dimension) - ((2/n)*s1) + ((2^dimension/n^2)*s2)))
}
# wrap-around L2-discrepancy
#------------------------
if(DisW2 == TRUE){
s1 <- 0
for (i in 1:n){
for (k in 1:n){
p <- prod((1.5-((abs(X[i,]-X[k,]))*(1-abs(X[i,]-X[k,])))))
s1 <- s1+p
}
}
R <- c(R , DisW2 = sqrt((-((4/3)^dimension) + ((1/n^2)*s1))))
}
# mixture L2-discrepancy
#------------------------
if(DisMix2 == TRUE){
s1 <- 0; s2 <- 0
for (i in 1:n){
p <- prod((5/3-0.25*abs(X[i,]-0.5)-0.25*((abs(X[i,]-0.5))^2)))
s1 <- s1+p
for (k in 1:n){
q <- prod((15/8-0.25*abs(X[i,]-0.5)-0.25*abs(X[k,]-0.5)-0.75*abs(X[i,]-X[k,])+0.5*((abs(X[i,]-X[k,]))^2)))
s2 <- s2+q
}
}
R <- c(R,DisMix2 = sqrt(((19/12)^dimension)-((2/n)*s1) + ((1/n^2)*s2)))
}
return(R)
}
|
