WH.regression.GOF: Goodness of Fit indices for Multiple regression of histogram...

In Airpino/HistDAWass: Histogram-Valued Data Analysis

Goodness of Fit indices for Multiple regression of histogram variables based on a two component model and L2 Wasserstein distance

Description

It computes three goodness of fit indices using the results and the predictions of a regression done with WH.regression.two.components function.

Usage

WH.regression.GOF(observed, predicted)

Arguments

observed	A one column MatH object, the observed histogram variable
predicted	A one column MatH object, the predicted histogram variable.

Value

a list with the GOF indices

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)
# compute GOF indices
GOF.indices <- WH.regression.GOF(observed = BLOOD[, 1], predicted = Predicted.BLOOD)

Airpino/HistDAWass documentation built on Jan. 30, 2024, 7:53 p.m.