Approximation of the squared Wasserstein distance W_g between two vectors decomposed into size, location and shape. Calculation based on the mean squared difference between the equidistant quantiles of the two input vectors a and b. As an approximation of the distribution, 1000 quantiles are computed for each vector.
Vector representing an empirical distribution under condition A
Vector representing an empirical distribution under condition B
An named Rcpp::List with the wasserstein distance between x and y, decomposed into terms for size, location, and shape
Schefzik and Goncalves 2019 Irpino and Verde (2015)
[wasserstein_metric()], [squared_wass_approx()] for different implementations of the wasserstein distance
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# input: one dimensional data in two conditions x <- rnorm(100, 42, 2) y <- c(rnorm(61, 20, 1), rnorm(41, 40,2)) # output: squared Wasserstein distance decomposed into terms for location, # shape, size d.wass.decomp <- squared_wass_decomp(x,y) d.wass.decomp$location d.wass.decomp$size d.wass.decomp$shape
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