goodFIT: Computing of goodness-of-fit metrics and information...

Description Usage Arguments Value Author(s) References Examples

View source: R/goodFIT.R

Description

This function computing of goodness-of-fit for continuous univariate distributions using tests: Kolmogorov-Smirnov, Anderson-Darling and Cramer-von Mises. It is based on goftest package. Moreover information criteria are evaluated: Akaike's Information Criterion and Bayesian Information Criterion, Akaike's Information Criterion with bias correction and Kashyap bayesian Information Criterio by means of InfoCRIT function.

Usage

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goodFIT(Station = ..., Type = ..., Intensity = ..., Parameters = ...,
  M.fit = ..., Dura = .., Plot = ..., Resolution = 300, SAVE = FALSE)

Arguments

Station:

a character specifying a name or number of pluviographic station where data were measurement, and it use to save results in *.xls format.

Type:

a character specifying a name of probability distribution function fitted (see selecDIST) by fitDISTRI function.

Intensity:

a numeric vector with intensity values for a specific time duration in different return periods.

Parameters:

a list with three elements: i) Type of distribution function ii) fitted parameters, and iii) source to call specfic function in the lmomco package.

M.fit:

a character specifying a name of fit method employed on pdf, just three options are available: L-moments (Lmoments), Probability-Weighted Moments (PWD), and Maximum Likelihood (MLE).

Dura:

a character specifying a time duration of the Intensity, (e.g. 30 min). This parameter is used to save results.

Plot:

a number (1) to determine if it will be plotted density curves both empirical as modeled (pdf). If any other number is used graphs will not appear.

Resolution:

a number to determine the resolution that the plot function will used to save graphs. It has two options: 300 and 600 ppi. See resoPLOT.

SAVE:

a logical value. TRUE will save Plot, FALSE will just show it.

Value

A data frame with statistics values of goodness of fit tests and its respective p-value, moreover information criteria are evaluated:

Author(s)

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

References

Hurvich, C. M., & Tsai, C. L. (1989). Regression and time series model selection in small samples. Biometrika, 76(2), 297-307.

Kashyap, R. L. (1982). Optimal choice of AR and MA parts in autoregressive moving average models. IEEE Transactions on Pattern Analysis and Machine Intelligence, (2), 99-104.

Examples

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# Meteorology station in the Airport Farfan in Tulua, Colombia.
data(Intgum5min)
data(Pargumbel)
# not plotted
test.fit <- goodFIT(Station = "2610516", Type = "Gumbel", Intensity = Intgum5min,
                    Parameters = Pargumbel,M.fit = "Lmoments", Dura ="5_min", Plot = 0)
 

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