HistDAWass-package {HistDAWass}

Histogram-Valued Data Analysis

Description

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.

Details

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

Author(s)

Antonio Irpino <[e-mail]>

References

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

Examples

# 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
)