| Type: | Package |
| Title: | Inference for Optimal Transport |
| Version: | 0.1.0 |
| Imports: | MASS (≥ 7.3-45), Rglpk (≥ 0.6-2), sm (≥ 2.2-5.4), transport (≥ 0.8-1) |
| Suggests: | Rcplex (≥ 0.3.3) |
| Description: | Sample from the limiting distributions of empirical Wasserstein distances under the null hypothesis and under the alternative. Perform a two-sample test on multivariate data using these limiting distributions and binning. |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| RoxygenNote: | 5.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2017-03-07 13:12:07 UTC; msommerfeld |
| Author: | Max Sommerfeld [aut, cre] |
| Maintainer: | Max Sommerfeld <max.sommerfeld@mathematik.uni-goettingen.de> |
| Repository: | CRAN |
| Date/Publication: | 2017-03-07 14:46:11 |
Two-sample test for multivariate data based on binning.
Description
Two-sample test for multivariate data based on binning.
Usage
binWDTest(x, y, L = 5, B = 100)
Arguments
x, y |
The two samples, rows are realizations. |
L |
Number of bins in each dimension. |
B |
Number of realizations of limiting distribution to simulate. |
Value
p-value.
Examples
## Not run:
x <- MASS::mvrnorm(n = 100, mean = c(0, 0), Sigma = diag(1, 2))
y <- MASS::mvrnorm(n = 100, mean = c(0, 0), Sigma = diag(2, 2))
pVal <- binWDTest(x, y)
## End(Not run)
Sample from the limit distribution under the alternative.
Description
Sample from the limit distribution under the alternative.
Usage
limDisAlt(B = 1000, r, s, distMat, p = 1)
Arguments
B |
Number of samples to generate. |
r, s |
Number of counts giving the two samples. |
distMat |
Distance matrix. |
p |
Cost exponent. Defaults to 1. |
Value
A vector of samples.
m-out-of-n Bootstrap for the limiting distribution.
Description
m-out-of-n Bootstrap for the limiting distribution.
Usage
limDisAltBoot(r, s, distMat, B = 1000, p = 1, gamma = 0.9)
Arguments
r, s |
Vectors of counts giving the two samples. |
distMat |
Distance matrix. |
B |
The number of samples to generate. Defaults to 1000. |
p |
Cost exponent. Defaults to 1. |
gamma |
m = n^gamma. Defaults to 0.9. |
Value
A sample from the limiting distribution.
Sample from the limiting distribution under the null.
Description
Sample from the limiting distribution under the null.
Usage
limDisNull(B = 500, r, distMat, p = 1)
Arguments
B |
number of samples to generate. Defaults to 500. |
r |
vector of probabilities in the original problem. |
distMat |
distance matrix in the original problem. |
p |
cost exponent. Defaults to 1. |
Value
A vector of samples.
Sample from the limiting distribution under the null when the underlying space is a grid.
Description
Sample from the limiting distribution under the null when the underlying space is a grid.
Usage
limDisNullGrid(B = 500, r, p = 1)
Arguments
B |
Number of bootstrap samples to generate. Defaults to 500. |
r |
vector of probabilities in the original problem. Is interpreted as a square matrix. |
p |
cost exponent. |
Value
A vector of samples.
Compute the Wasserstein distance between to finite distributions.
Description
Compute the Wasserstein distance between to finite distributions.
Usage
wassDist(a, b, distMat, p = 1)
Arguments
a, b |
Vectors representing probability distributions. |
distMat |
Cost matrix. |
p |
cost exponent. |
Value
The Wasserstein distance.