# MSClaio2008: Model Selection Criteria In nsRFA: Non-Supervised Regional Frequency Analysis

## Description

Model selection criteria for the frequency analysis of hydrological extremes, from Laio et al (2008).

## Usage

 ```1 2 3 4 5 6 7 8``` ``` MSClaio2008 (sample, dist=c("NORM","LN","GUMBEL","EV2","GEV","P3","LP3"), crit=c("AIC", "AICc", "BIC", "ADC")) ## S3 method for class 'MSClaio2008' print(x, digits=max(3, getOption("digits") - 3), ...) ## S3 method for class 'MSClaio2008' summary(object, ...) ## S3 method for class 'MSClaio2008' plot(x, ...) ```

## Arguments

 `sample` data sample `dist` distributions: normal `"NORM"`, 2 parameter log-normal `"LN"`, Gumbel `"GUMBEL"`, Frechet `"EV2"`, Generalized Extreme Value `"GEV"`, Pearson type III `"P3"`, log-Pearson type III `"LP3"` `crit` Model-selection criteria: Akaike Information Criterion `"AIC"`, Akaike Information Criterion corrected `"AICc"`, Bayesian Information Criterion `"BIC"`, Anderson-Darling Criterion `"ADC"` `x` object of class `MSClaio2008`, output of `MSClaio2008()` `object` object of class `MSClaio2008`, output of `MSClaio2008()` `digits` minimal number of "significant" digits, see 'print.default' `...` other arguments

## Details

The following lines are extracted from Laio et al. (2008). See the paper for more details and references.

Model selection criteria

The problem of model selection can be formalized as follows: a sample of n data, D=(x1, ..., xn), arranged in ascending order is available, sampled from an unknown parent distribution f(x); Nm operating models, Mj, j=1, ..., Nm, are used to represent the data. The operating models are in the form of probability distributions, Mj = gj(x, O*), with parameters O* estimated from the available data sample D. The scope of model selection is to identify the model Mopt which is better suited to represent the data, i.e. the model which is closer in some sense to the parent distribution f(x).

Three different model selection criteria are considered here, namely, the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and the Anderson-Darling Criterion (ADC). Of the three methods, the first two belong to the category of classical literature approaches, while the third derives from a heuristic interpretation of the results of a standard goodness-of-fit test (see Laio, 2004).

Akalike Information Criterion

The Akaike information Criterion (AIC) for the j-th operational model can be computed as

AICj = -2 ln (Lj(O*)) + 2 pj

where

Lj(O*) = prod (gj(xi, O*))

is the likelihood function, evaluated at the point O = O* corresponding to the maximum likelihood estimator of the parameter vector O and pj is the number of estimated parameter of the j-th operational model. In practice, after the computation of the AICj, for all of the operating models, one selects the model with the minimum AIC value, AICmin.

When the sample size, n, is small, with respect to the number of estimated parameters, p, the AIC may perform inadequately. In those cases a second-order variant of AIC, called AICc, should be used:

AICcj = -2 ln (Lj(O*)) + 2 pj (n/(n - pj - 1))

Indicatively, AICc should be used when n/p < 40.

Bayesian Information Criterion

The Bayesian Information Criterion (BIC) for the j-th operational model reads

BICj = -2 ln (Lj(O*)) + ln(n) pj

In practical application, after the computation of the BICj, for all of the operating models, one selects the model with the minimum BIC value, BICmin.

Anderson-Darling Criterion

The Anderson-Darling criterion has the form:

ADCj = [0.0403 + 0.116 ((0.2 epsj)/betaj)^(etaj/0.851) (ADj - 0.2 epsj / epsj

if 1.2 epsj >= ADj, where ADj is the discrepancy measure characterizing the criterion, the Anderson-Darling statistic `A2` in `GOFlaio2004`, and epsj, betaj and etaj are distribution-dependent coefficients that are tabled by Laio [2004, Tables 3 and 5] for a set of seven distributions commonly employed for the frequency analysis of extreme events. In practice, after the computation of the ADCj, for all of the operating models, one selects the model with the minimum ADC value, ADCmin.

## Value

`MSClaio2008` returns the value of the criteria `crit` (see Details) chosen applied to the `sample`, for every distribution `dist`.

`plot.MSClaio2008` plots the empirical distribution function of `sample` (Weibull plotting position) on a log-normal probability plot, plots the candidate distributions `dist` (whose parameters are evaluated with the maximum likelihood technique, see `MLlaio2004`, and highlights the ones chosen by the criteria `crit`.)

## Note

For information on the package and the Author, and for all the references, see `nsRFA`.

`GOFlaio2004`, `MLlaio2004`.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38``` ```data(FEH1000) sitedata <- am[am[,1]==53004, ] # data of site 53004 serieplot(sitedata[,4], sitedata[,3]) MSC <- MSClaio2008(sitedata[,4]) MSC summary(MSC) plot(MSC) sitedata <- am[am[,1]==69023, ] # data of site 69023 serieplot(sitedata[,4], sitedata[,3]) MSC <- MSClaio2008(sitedata[,4], crit=c("AIC", "ADC")) MSC summary(MSC) plot(MSC) sitedata <- am[am[,1]==83802, ] # data of site 83802 serieplot(sitedata[,4], sitedata[,3]) MSC <- MSClaio2008(sitedata[,4], dist=c("GEV", "P3", "LP3")) MSC summary(MSC) plot(MSC) # short sample, high positive L-CA sitedata <- am[am[,1]==40012, ] # data of site 40012 serieplot(sitedata[,4], sitedata[,3]) MSC <- MSClaio2008(sitedata[,4]) MSC summary(MSC) plot(MSC) # negative L-CA sitedata <- am[am[,1]==68002, ] # data of site 68002 serieplot(sitedata[,4], sitedata[,3]) MSC <- MSClaio2008(sitedata[,4]) MSC summary(MSC) plot(MSC) ```