test2.2018ext: extended : seems like it's working

In kisungyou/DAS: Distance-bAsed Statistical methods

Description

extended : seems like it's working

Usage

test2.2018ext(x, label)

Examples

## small test for CRAN submission
dat1 <- matrix(rnorm(60, mean= 1), ncol=2) # group 1 : 30 obs of mean  1
dat2 <- matrix(rnorm(50, mean=-1), ncol=2) # group 2 : 25 obs of mean -1

dmat <- as.matrix(dist(rbind(dat1, dat2)))  # Euclidean distance matrix
lab  <- c(rep(1,30), rep(2,25))             # corresponding label

test2.2018ext(dmat, lab)                    # run the code !

## Not run: 
## WARNING: computationally heavy. 
#  Let's compute empirical Type 1 error at alpha=0.05 : should not be on 45 line
niter = 496  
pvals1 = rep(0,niter) # 2018 method
pvals2 = rep(0,niter) # extended
for (i in 1:niter){
  dat1 = matrix(rnorm(60*3,mean= 1), ncol=3) 
  dat2 = matrix(rnorm(40*3,mean=-1), ncol=3)
  dat  = rbind(dat1, dat2) 
  lab = c(rep(1,60), rep(2,40))
  pvals1[i] = test2.2018bbw(as.matrix(dist(dat)), lab)$p.value
  pvals2[i] = test2.2018ext(as.matrix(dist(dat)), lab)$p.value
  print(paste("iteration ",i," complete..",sep=""))
}

#  Visualize the above at multiple significance levels
alphas = seq(from=0.001, to=0.999, length.out=100)
errors1 = rep(0,100)
errors2 = rep(0,100)
for (i in 1:100){
   errors1[i] = sum(pvals1<=alphas[i])/niter
   errors2[i] = sum(pvals2<=alphas[i])/niter
}
par(mfrow=c(1,2))
plot(alphas, errors1, "b", main="Type 1 : 2018 method", 
     xlab="alpha", ylab="error", lwd=2)
abline(0,1, lwd=2, col="red")
plot(alphas, errors2, "b", main="Type 1 : extension", 
     xlab="alpha", ylab="error", lwd=2)
abline(0,1, lwd=2, col="red")

## End(Not run)

kisungyou/DAS documentation built on Jan. 6, 2020, 7:09 a.m.