sim2.2018HN | R Documentation |
Given a multivariate sample X, hypothesized mean μ_0 and covariance Σ_0, it tests
H_0 : μ_x = μ_y \textrm{ and } Σ_x = Σ_y \quad vs\quad H_1 : \textrm{ not } H_0
using the procedure by Hyodo and Nishiyama (2018) in a similar fashion to that of Liu et al. (2017) for one-sample test.
sim2.2018HN(X, Y)
X |
an (n_x \times p) data matrix of 1st sample. |
Y |
an (n_y \times p) data matrix of 2nd sample. |
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.
hyodo_simultaneous_2018SHT
## CRAN-purpose small example smallX = matrix(rnorm(10*3),ncol=3) smallY = matrix(rnorm(10*3),ncol=3) sim2.2018HN(smallX, smallY) # run the test ## empirical Type 1 error niter = 1000 counter = rep(0,niter) # record p-values for (i in 1:niter){ X = matrix(rnorm(121*10), ncol=10) Y = matrix(rnorm(169*10), ncol=10) counter[i] = ifelse(sim2.2018HN(X,Y)$p.value < 0.05, 1, 0) } ## print the result cat(paste("\n* Example for 'sim2.2018HN'\n","*\n", "* number of rejections : ", sum(counter),"\n", "* total number of trials : ", niter,"\n", "* empirical Type 1 error : ",round(sum(counter/niter),5),"\n",sep=""))
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