metric)
In this document we describe the main features of the HistDAWass package. The name is the acronym for Histogram-valued Data analysis using Wasserstein metric. The implemented classes and functions are related to the analysis of data tables containing histograms in each cell instead of the classical numeric values.
In this document we describe the main features of the HistDAWass package. The name is the acronym for Histogram-valued Data analysis using Wasserstein metric. The implemented classes and functions are related to the anlysis of data tables containing histograms in each cell instead of the classical numeric values.
What is the L2 Wasserstein metric?
given two probability density functions f and g, each one has a cumulative distribution function F and G and thei respectively quantile functions (the inverse of a cumulative distribution function) Qf and Qg. The L2 Wasserstein distance is
The implemented classes are those described in the following table
Class | wrapper function for initializing | Description |
---|---|---|
distributionH |
distributionH(x,p) |
A class describing a histogram distibution |
MatH |
MatH(x, nrows, ncols,rownames,varnames, by.row ) |
A class describing a matrix of distributions |
TdistributionH |
TdistributionH() |
A class derived from distributionH equipped with a timestamp or a time window |
HTS |
HTS() |
A class describing a Histgram-valued time series |
library(HistDAWass)
mydist=distributionH(x=c(0,1,2),p=c(0,0.3,1))
data2hist functions
mean
standard deviation
skewness
kurthosis
The average hisogram of a column
The standard deviation of a variable
The covarince matrix of a MatH
The correlation matrix of a MatH
plot of a MatH
plot of a HTS
Clustering
Kmeans
Adaptive distance based Kmeans
Fuzzy cmeans
Fuzzy cmeans based on adaptive Wasserstein distances
Kohonen batch self organizing maps
Kohonen batch self organizing maps with Wasserstein adaptive distances
Hierarchical clustering
Dimension reduction techniques
Principal components analysis of a single histogram variable
Principal components analysis of a set of histogram variables (using Multiple Factor Analysis)
Smoothing
Moving averages
Exponential smoothing
Forecasting
A two component model for a linear regression using Least Square method