HistDAWass-package | R Documentation |
We consider histogram-valued data, i.e., data described by univariate histograms. The methods and the basic statistics for histogram-valued data are mainly based on the L2 Wasserstein metric between distributions, i.e., a Euclidean metric between quantile functions. The package contains unsupervised classification techniques, least square regression and tools for histrogram-valued data and for histogram time series.
Package: | HistDAWass |
Type: | Package |
Version: | 0.1.1 |
Date: | 2014-09-17 |
License: | GPL (>=2) |
Depends: | methods |
~~ An overview of how to use the package, including the most important functions ~~
Antonio Irpino <antonio.irpino@unicampania.it>
Irpino, A., Verde, R. (2015) Basic
statistics for distributional symbolic variables: a new metric-based
approach, Advances in Data Analysis and Classification, Volume 9, Issue 2, pp 143–175.
DOI doi: 10.1007/s11634-014-0176-4
# Generating a list of distributions a <- vector("list", 4) a[[1]] <- distributionH( x = c(80, 100, 120, 135, 150, 165, 180, 200, 240), p = c(0, 0.025, 0.1, 0.275, 0.525, 0.725, 0.887, 0.975, 1) ) a[[2]] <- distributionH( x = c(80, 100, 120, 135, 150, 165, 180, 195, 210, 240), p = c(0, 0.013, 0.101, 0.255, 0.508, 0.718, 0.895, 0.961, 0.987, 1) ) a[[3]] <- distributionH( x = c(95, 110, 125, 140, 155, 170, 185, 200, 215, 230, 245), p = c(0, 0.012, 0.041, 0.154, 0.36, 0.595, 0.781, 0.929, 0.972, 0.992, 1) ) a[[4]] <- distributionH( x = c(105, 120, 135, 150, 165, 180, 195, 210, 225, 240, 260), p = c(0, 0.009, 0.035, 0.081, 0.186, 0.385, 0.633, 0.832, 0.932, 0.977, 1) ) # Generating a list of names of observations namerows <- list("u1", "u2") # Generating a list of names of variables namevars <- list("Var_1", "Var_2") # creating the MatH Mat_of_distributions <- MatH( x = a, nrows = 2, ncols = 2, rownames = namerows, varnames = namevars, by.row = FALSE )
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