pW-Criteria: Identification criteria allow estimating the relative degree...

Description Usage Arguments Value Author(s) References Examples

Description

This function determine what probability distribution function has the best goodness-of-fit to observations, number of parameters and the quality of taking in account different information criterias that are evaluated in the criteria proposed by (Siena et al., 2017).

Usage

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p.criteria(metrics = .., critnames = .., pdfnames = ..)

Arguments

metrics:

a numeric matrix with the values of criterias AIC, BIC, AICc and KIC (rows) evaluated by each PDFs (columns).

critnames:

a character vector with the names of information criteria evaluated.

pdfnames:

a character vector with the names of PDFs evaluated.

Value

Provided a matrix with values of pW criteria by each PDFs and information criterias.

Author(s)

Adriana Pina <[email protected]> and David Zamora <[email protected]> Water Resources Engineering Research Group - GIREH

References

Siena, M., Riva, M., Giamberini, M., & Gouze, P. (2017). Statistical modeling of gas-permeability spatial variability along a limestone core. Spatial Statistics. https://doi.org/10.1016/j.spasta.2017.07.007

Examples

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# Example of five PDFs and their respectives values of four information criterias.
data(fractures.crit)

pW.1 <- p.criteria(metrics = fractures.crit, critnames = rownames(fractures.crit), pdfnames = colnames(fractures.crit))
pW.1
# GEV: AIC= 100, BIC= 100, AICc= 1, KIC= 99 

dazamora/IDFtool documentation built on March 20, 2018, 8:56 p.m.