| unif.2017YMi | R Documentation | 
Given a multivariate sample X, it tests
H_0 : \Sigma_x = \textrm{ uniform on } \otimes_{i=1}^p [a_i,b_i]  \quad vs\quad H_1 : \textrm{ not } H_0
using the procedure by Yang and Modarres (2017). Originally, it tests the goodness of fit 
on the unit hypercube [0,1]^p and modified for arbitrary rectangular domain.
unif.2017YMi(
  X,
  type = c("Q1", "Q2", "Q3"),
  lower = rep(0, ncol(X)),
  upper = rep(1, ncol(X))
)
X | 
 an   | 
type | 
 type of statistic to be used, one of   | 
lower | 
 length-  | 
upper | 
 length-  | 
a (list) object of S3 class htest containing: 
a test statistic.
p-value under H_0.
alternative hypothesis.
name of the test.
name(s) of provided sample data.
yang_multivariate_2017SHT
## CRAN-purpose small example
smallX = matrix(rnorm(10*3),ncol=3)
unif.2017YMi(smallX) # run the test
## empirical Type 1 error 
##   compare performances of three methods 
niter = 1234
rec1  = rep(0,niter) # for Q1
rec2  = rep(0,niter) #     Q2
rec3  = rep(0,niter) #     Q3
for (i in 1:niter){
  X = matrix(runif(50*10), ncol=50) # (n,p) = (10,50)
  rec1[i] = ifelse(unif.2017YMi(X, type="Q1")$p.value < 0.05, 1, 0)
  rec2[i] = ifelse(unif.2017YMi(X, type="Q2")$p.value < 0.05, 1, 0)
  rec3[i] = ifelse(unif.2017YMi(X, type="Q3")$p.value < 0.05, 1, 0)
}
## print the result
cat(paste("\n* Example for 'unif.2017YMi'\n","*\n",
"* Type 1 error with Q1 : ", round(sum(rec1/niter),5),"\n",
"*                   Q2 : ", round(sum(rec2/niter),5),"\n",
"*                   Q3 : ", round(sum(rec3/niter),5),"\n",sep=""))
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