Description Usage Arguments Details Value References Examples

View source: R/WassersteinTest.R

Two-sample test to check for differences between two distributions (conditions) using the 2-Wasserstein distance, either using the semi-parametric permutation testing procedure with GPD approximation to estimate small p-values accurately or the test based on asymptotic theory

1 | ```
wasserstein.test(x, y, method = c("SP", "ASY"), permnum = 10000)
``` |

`x` |
univariate sample (vector) representing the distribution of condition A |

`y` |
univariate sample (vector) representing the distribution of condition B |

`method` |
testing procedure to be employed: "SP" for the semi-parametric permutation testing procedure with GPD approximation to estimate small p-values accurately; "ASY" for the test based on asymptotic theory. If no method is given, "SP" will be used by default. |

`permnum` |
number of permutations used in the permutation testing procedure (if method=<e2><80><9d>SP<e2><80><9d> is performed); default is 10000 |

Details concerning the two testing procedures (i.e. the permutation testing procedure with GPD approximation to estimate small p-values accurately and the test based on asymptotic theory) can be found in Schefzik and Goncalves (2019).

A vector concerning the testing results (see Schefzik and Goncalves (2019) for details).

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: The p-value of the semi-parametric 2-Wasserstein distance-based test or p-value determined using asymptotic theory, depending on the method

p.ad.gpd: in case the GPD fitting is performed: p-value of the Anderson-Darling test to check whether the GPD actually fits the data well (otherwise NA). This output is only returned when performing a semi-parametric test (method="SP")!

N.exc: in case the GPD fitting is performed: number of exceedances (starting with 250 and iteratively decreased by 10 if necessary) that are required to obtain a good GPD fit (i.e. p-value of Anderson-Darling test greater or eqaul to 0.05) (otherwise NA). This output is only returned when performing a semi-parametric test (method="SP")!

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).

1 2 3 4 5 6 7 8 9 | ```
# generate two input distributions
x<-rnorm(500)
y<-rnorm(500,4,1.5)
wasserstein.test(x,y,method="ASY")
# Run with default options: method="SP", permnum=10000
wasserstein.test(x,y)
# Run with a seed for the semi-parametric test ("SP")
set.seed(42)
wasserstein.test(x,y, method="SP")
``` |

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