.wassersteinTestAsy {waddR} | R Documentation |
Two-sample test to check for differences between two distributions (conditions) using the 2-Wasserstein distance: Implementation using a test based on asymptotic theory
.wassersteinTestAsy(x, y)
x |
univariate sample (vector) representing the distribution of condition A |
y |
univariate sample (vector) representing the distribution of condition B |
This is the asymptotic version of wasserstein.test, for the semi-parametric procedure see .wassersteinTestSp
Details concerning the testing procedure based on asymptotic theory can be found in Schefzik and Goncalves (2019).
A vector concerning the testing results, precisely (see Schefzik and Goncalves (2019) for details)
d.wass: 2-Wasserstein distance between the two samples computed by quantile approximation
d.wass^2 squared 2-Wasserstein distance between the two samples computed by quantile approximation
d.comp^2: squared 2-Wasserstein distance between the two samples computed by decomposition approximation
d.comp: 2-Wasserstein distance between the two samples computed by decomposition approximation
location: location term in the decomposition of the squared 2-Wasserstein distance between the two samples
size: size term in the decomposition of the squared 2-Wasserstein distance between the two samples
shape: shape term in the decomposition of the squared 2-Wasserstein distance between the two samples
rho: correlation coefficient in the quantile-quantile plot
pval: p-value of the 2-Wasserstein distance-based test using asymptotic theory
perc.loc: fraction (in overall squared 2-Wasserstein distance obtained by the decomposition approximation
perc.size: fraction (in overall squared 2-Wasserstein distance obtained by the decomposition approximation
perc.shape: fraction (in overall squared 2-Wasserstein distance obtained by the decomposition approximation
decomp.error: relative error between the squared 2-Wasserstein distance computed by the quantile approximation and the squared 2-Wasserstein distance computed by the decomposition approximation
Schefzik, R. and Goncalves, A. (2019).