HistDAWass (version 1.0.8)

distributionH-class: Class distributionH.

Description

Class "distributionH" desfines an histogram object The class describes a histogram by means of its cumulative distribution function. The methods are develoved accordingly to the L2 Wasserstein distance between distributions.

A histogram object can be created also with the function distributionH(...), the costructor function for creating an object containing the description of a histogram.

Usage

# S4 method for distributionH
initialize(
  .Object,
  x = numeric(0),
  p = numeric(0),
  m = numeric(0),
  s = numeric(0)
)
distributionH(x = numeric(0), p = numeric(0))

Value

A distributionH object

Arguments

.Object

the type ("distributionH")

x

a numeric vector. it is the domain of the distribution (i.e. the extremes of bins).

p

a numeric vector (of the same lenght of x). It is the cumulative distribution function CDF.

m

(optional) a numeric value. Is the mean of the histogram.

s

(optional) a numeric positive value. It is the standard deviation of a histogram.

Objects from the Class

Objects can be created by calls of the form new("distributionH", x, p, m, s).

Author

Antonio Irpino

Details

Class distributionH defines a histogram object

References

Irpino, A., Verde, R. (2015) Basic statistics for distributional symbolic variables: a new metric-based approach Advances in Data Analysis and Classification, DOI 10.1007/s11634-014-0176-4

See Also

meanH computes the mean. stdH computes the standard deviation.

Examples

#---- initialize a distributionH object mydist
# from a simple histogram
# ----------------------------
# | Bins    |  Prob  | cdf   |
# ----------------------------
# | [1,2)   |  0.4   | 0.4   |
# | [2,3]   |  0.6   | 1.0   |
# ----------------------------
# | Tot.    |  1.0   | -     |
# ----------------------------
mydist <- new("distributionH", c(1, 2, 3), c(0, 0.4, 1))
str(mydist)
# OUTPUT
# Formal class 'distributionH' [package "HistDAWass"] with 4 slots
#   ..@ x: num [1:3] 1 2 3 the quantiles
#   ..@ p: num [1:3] 0 0.4 1 the cdf
#   ..@ m: num 2.1 the mean
#   ..@ s: num 0.569 the standard deviation
# or using
mydist <- distributionH(x = c(1, 2, 3), p = c(0, 0.4, 1))