transport_plan {approxOT}

Optimal transport plans

Description

Optimal transport plans

Usage

transport_plan(
  X,
  Y,
  a = NULL,
  b = NULL,
  p = 2,
  ground_p = 2,
  observation.orientation = c("rowwise", "colwise"),
  method = transport_options(),
  ...
)

Arguments

X

The covariate data of the first sample.

Y

The covariate data of the second sample.

a

Optional. Empirical measure of the first sample

b

Optional. Empirical measure of the second sample

p

The power of the Wasserstein distance

ground_p

The power of the Lp norm

observation.orientation

Are observations by row ("rowwise") or column ("colwise").

method

Which transportation method to use. See [transport_options][transport_options]

...

Additional arguments for various methods

• "niter": The number of iterations to use for the entropically penalized optimal transport distances

• "epsilon": The multiple of the median cost to use as a penalty in the entropically penalized optimal transport distances

• "unbiased": If using Sinkhorn distances, should the distance be de-biased? (TRUE/FALSE)

• "nboot": If using sliced Wasserstein distances, specify the number of Monte Carlo samples

Value

a list with slots "tplan" and "cost". "tplan" is the optimal transport plan and "cost" is the optimal transport distance.

Examples

set.seed(203987)
n <- 100
d <- 10
x <- matrix(stats::rnorm(d*n), nrow=d, ncol=n)
y <- matrix(stats::rnorm(d*n), nrow=d, ncol=n)
#get hilbert sort orders for x in backwards way
transx <- transport_plan(X=x, Y=x, ground_p = 2, p = 2, 
                         observation.orientation =  "colwise", 
                         method = "hilbert")