WH.regression.two.components.predict: Multiple regression analysis for histogram variables based on...

In HistDAWass: Histogram-Valued Data Analysis

Multiple regression analysis for histogram variables based on a two component model and L2 Wasserstein distance

Description

Predict distributions using the results of a regression done with WH.regression.two.components function.

Usage

WH.regression.two.components.predict(data, parameters)

Arguments

data	A MatH object (a matrix of distributionH) explantory part.
parameters	A named vector with the parameter from a WH.regression.two.components model

Value

a MatH object, the predicted histograms

References

Irpino A, Verde R (in press 2015). Linear regression for numeric symbolic variables: a least squares approach based on Wasserstein Distance. ADVANCES IN DATA ANALYSIS AND CLASSIFICATION, ISSN: 1862-5347, DOI:10.1007/s11634-015-0197-7
An extended version is available on arXiv repository arXiv:1202.1436v2 https://arxiv.org/abs/1202.1436v2

Examples

# do regression
model.parameters <- WH.regression.two.components(data = BLOOD, Yvar = 1, Xvars = c(2:3))
# do prediction
Predicted.BLOOD <- WH.regression.two.components.predict(data = BLOOD[, 2:3], 
                                                        parameters = model.parameters)

HistDAWass documentation built on Sept. 26, 2022, 5:06 p.m.