DataSimilarity (version 0.1.1)

gTests: Graph-Based Tests

Description

Performs the edge-count two-sample tests for multivariate data implementated in g.tests from the gTests package. This function is inteded to be used e.g. in comparison studies where all four graph-based tests need to be calculated at the same time. Since large parts of the calculation coincide, using this function should be faster than computing all four statistics individually.

Usage

gTests(X1, X2, dist.fun = stats::dist, graph.fun = MST, 
        n.perm = 0, dist.args = NULL, graph.args = NULL,
        maxtype.kappa = 1.14,  seed = 42)

Value

A list with the following components:

statistic

Observed values of the test statistics

p.value

Asymptotic or permutation p values

alternative

The alternative hypothesis

method

Description of the test

data.name

The dataset names

Arguments

X1

First dataset as matrix or data.frame

X2

Second dataset as matrix or data.frame

dist.fun

Function for calculating a distance matrix on the pooled dataset (default: stats::dist, Euclidean distance).

graph.fun

Function for calculating a similarity graph using the distance matrix on the pooled sample (default: MST, Minimum Spanning Tree).

n.perm

Number of permutations for permutation test (default: 0, asymptotic test is performed).

dist.args

Named list of further arguments passed to dist.fun.

graph.args

Named list of further arguments passed to graph.fun.

maxtype.kappa

Parameter \(\kappa\) of the maxtype test (default: 1.14). See ZC.

seed

Random seed (default: 42)

Applicability

Target variable?	Numeric?	Categorical?	K-sample?
No	Yes	No	No

Details

The original, weighted, generalized and maxtype edge-count test are performed.

For n.perm = 0, an asymptotic test using the asymptotic normal approximation of the null distribution is performed. For n.perm > 0, a permutation test is performed.

This implementation is a wrapper function around the function g.tests that modifies the in- and output of that function to match the other functions provided in this package. For more details see the g.tests.

References

Friedman, J. H., and Rafsky, L. C. (1979). Multivariate Generalizations of the Wald-Wolfowitz and Smirnov Two-Sample Tests. The Annals of Statistics, 7(4), 697-717.

Chen, H. and Friedman, J.H. (2017). A New Graph-Based Two-Sample Test for Multivariate and Object Data. Journal of the American Statistical Association, 112(517), 397-409. tools:::Rd_expr_doi("10.1080/01621459.2016.1147356")

Chen, H., Chen, X. and Su, Y. (2018). A Weighted Edge-Count Two-Sample Test for Multivariate and Object Data. Journal of the American Statistical Association, 113(523), 1146-1155, tools:::Rd_expr_doi("10.1080/01621459.2017.1307757")

Zhang, J. and Chen, H. (2022). Graph-Based Two-Sample Tests for Data with Repeated Observations. Statistica Sinica 32, 391-415, tools:::Rd_expr_doi("10.5705/ss.202019.0116").

Chen, H., and Zhang, J. (2017). gTests: Graph-Based Two-Sample Tests. R package version 0.2, https://CRAN.R-project.org/package=gTests.

Stolte, M., Kappenberg, F., Rahnenführer, J., Bommert, A. (2024). Methods for quantifying dataset similarity: a review, taxonomy and comparison. Statist. Surv. 18, 163 - 298. tools:::Rd_expr_doi("10.1214/24-SS149")

See Also

FR for the original edge-count test, CF for the generalized edge-count test, CCS for the weighted edge-count test, and ZC for the maxtype edge-count test, gTests_cat, CCS_cat, FR_cat, CF_cat, and ZC_cat for versions of the tests for categorical data

Examples

# Draw some data
X1 <- matrix(rnorm(1000), ncol = 10)
X2 <- matrix(rnorm(1000, mean = 0.5), ncol = 10)
# Perform edge-count tests
if(requireNamespace("gTests", quietly = TRUE)) {
  gTests(X1, X2)
}