meantest.pe.fisher {PEtests} | R Documentation |
Two-sample PE mean test for high-dimensional data via Fisher's combination
Description
This function implements the two-sample PE covariance test via
Fisher's combination.
Suppose are i.i.d.
copies of
, and
are i.i.d. copies of
.
Let
and
denote the
-values associated with
the
-norm-based covariance test (see
meantest.cq
for details)
and the -norm-based covariance test
(see
meantest.clx
for details), respectively.
The PE covariance test via Fisher's combination is defined as
It has been proved that with some regularity conditions, under the null hypothesis
the two tests are asymptotically independent as
,
and therefore
asymptotically converges in distribution to a
distribution.
The asymptotic
-value is obtained by
where is the cdf of the
distribution.
Usage
meantest.pe.fisher(dataX,dataY)
Arguments
dataX |
an |
dataY |
an |
Value
stat
the value of test statistic
pval
the p-value for the test.
References
Chen, S. X. and Qin, Y. L. (2010). A two-sample test for high-dimensional data with applications to gene-set testing. Annals of Statistics, 38(2):808–835.
Cai, T. T., Liu, W., and Xia, Y. (2014). Two-sample test of high dimensional means under dependence. Journal of the Royal Statistical Society: Series B: Statistical Methodology, 76(2):349–372.
Examples
n1 = 100; n2 = 100; pp = 500
set.seed(1)
X = matrix(rnorm(n1*pp), nrow=n1, ncol=pp)
Y = matrix(rnorm(n2*pp), nrow=n2, ncol=pp)
meantest.pe.fisher(X,Y)