Nothing
set.seed(20101111)
myMCS <- function(x,p){
## First and second args are
## x (dxn matrix) and p (vector of probabilities).
n <- dim(x)[2]
d <- dim(x)[1]
u <- t(apply(x,1,rank))/(n+1)# schmid and schmidt use n not n+1
rho <- numeric(length(p))
for(k in 1:length(p)) {
Diff <- p[k] - u
Diff[Diff<0] <- 0
Prod <- apply(Diff,2,prod)
num <- mean(Prod) - ((p[k]^2)/2)^d
den <- (p[k]^(d+1)) / (d+1) - (0.5*p[k]^2)^d
rho[k] <- num/den
}
rho
}
## simulated data - dimension 2
n <- 1000
by <- 0.01
p <- seq(by,1-by,by=by)
data <- rbind(rnorm(n),rnorm(n))
tmRl <- MCS(t(data), p)
myRl <- myMCS(data, tmRl$p)
expect_equal(myRl, tmRl$mcs, info="MCS:independentnormaldata")
expect_equal(p, tmRl$p, info="MCS:mathchingpargument")
## winter air pollution data - dimension 5
tmWinterMCS <- MCS(winter, p)
myWinterMCS <- myMCS(t(winter), p)
expect_equal(myWinterMCS, tmWinterMCS$mcs,
info="MCS:winterairpollutiondata")
## summer airpollution data - dimension 5
tmSummerMCS <- MCS(summer, p)
mySummerMCS <- myMCS(t(summer), p)
expect_equal(mySummerMCS, tmSummerMCS$mcs,
info="MCS:summerairpollutiondata")
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