Description Details Author(s) References Examples

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 <[email protected]>

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 https://doi.org/10.1007/s11634-014-0176-4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
# 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 )
``` |

HistDAWass documentation built on March 20, 2018, 5:04 p.m.

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