| Type: | Package |
| Title: | Two-Sample Test in High Dimensions using Random Projection |
| Version: | 0.1.4 |
| Description: | Performs the random projection test (Lopes et al., (2011) <doi:10.48550/arXiv.1108.2401>) for the one-sample and two-sample hypothesis testing problem for equality of means in the high dimensional setting. We are interested in detecting the mean vector in the one-sample problem or the difference between mean vectors in the two-sample problem. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | MASS, stats, glue |
| RoxygenNote: | 7.3.1 |
| NeedsCompilation: | no |
| Packaged: | 2024-06-03 09:32:33 UTC; jortialo |
| Author: | Juan Ortiz Author [aut, cre, cph, rev] |
| Maintainer: | Juan Ortiz Author <juan.ortiz1alonso@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-06-04 09:44:51 UTC |
Two-Sample Test in High Dimensions using Random Projection
Description
This function performs the random projection test (Lopes et al., (2011) <arXiv:1108.2401>) for the one-sample and two-sample hypothesis testing problem for equality of means in the high dimensional setting. We are interested in detecting the mean vector in the one-sample problem or the difference between mean vectors in the two-sample problem.
Usage
random_projection_test(X, Y = NULL, mu0 = NULL, proj_dimension = NULL)
Arguments
X |
The n1-by-p observation matrix with numeric column variables. |
Y |
An optional n2-by-p observation matrix with numeric column variables. If NULL, one-sample test is conducted on X; otherwise, a two-sample test is conducted on X and Y. |
mu0 |
The null hypothesis vector to be tested. If NULL, the default value is the 0 vector of lenght p. |
proj_dimension |
Dimension where to project the given samples. If NULL, the default value is floor(n/2), where n=n1 if Y=NULL or n=n1+n2-2 if not, as in Lopes et al. |
Details
Since the matrix used to project the data into a lower-dimension subset is a random matrix, obtaining the exactly same p-values in two repetitions is not likely. However, power function has been proved to perform adequately in the vast majority of settings.
Value
statistic |
Value of the test's statistic T_k^2. |
p_value |
The p-value of the test. |
degrees_freedom |
The degrees of freedom used for the F distribution, returns list(k, n-k+1). |
null_value |
Returns mu0. |
method |
Brief description of the test that has been carried out. |
Author(s)
Juan Ortiz, <juan.ortiz1alonso@gmail.com>
References
Lopes, M. E., Jacob, L. J. & Wainwright, M. J. (2011). A More Powerful Two-Sample Test in High Dimensions using Random Projection. <arXiv:1108.2401>.
Examples
set.seed(10086)
# One-sample test
n1=30; p=120
X = matrix(rnorm(n1*p), nrow = n1, ncol = p)
res1 = random_projection_test(X)
# Two-sample test
n2=65
Y = matrix(rnorm(n2*p), nrow = n2, ncol = p)
res2 = random_projection_test(X, Y)
# Specify a null hypothesis vector
res3 = random_projection_test(X, Y, mu0 = rep(0.1, p))
# Choose a projection dimension manually, will work worse than previous example
res4 = random_projection_test(X, Y, mu0 = rep(0.1, p), proj_dimension = 4)