gTests: Graph-Based Tests
In DataSimilarity: Quantifying Similarity of Datasets and Multivariate Two- And k-Sample Testing

gTests

R Documentation

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)

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)

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.

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

Applicability

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

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. \Sexpr[results=rd]{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, \Sexpr[results=rd]{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, \Sexpr[results=rd]{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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/24-SS149")}

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)
}

DataSimilarity documentation built on April 3, 2025, 9:39 p.m.