# RegioGEV: Regional (or local) parameter and quantile estimation In flood: Statistical Methods for the (Regional) Analysis of Flood Frequency

## Description

Calculates regional (or local) parameters of a generalized extreme value (GEV) distribution using (trimmed) L-moments (see TLMoments and parameters) from a vector or matrix of observation. Based on these parameters, a p-quantile of the GEV will be calculated for the jth station.

## Usage

 1 RegioGEV(x, p, j = 1, leftrim = 0, rightrim = 0, na.rm = TRUE, ...)

## Arguments

 x vector or matrix of observations (rows: observations, d columns: stations). p a probability. j quantile and parameter estimation for the jth station (jth column of x). Irrelevant if is x is a vector. leftrim integer indicating lower trimming parameter (≥ 0). rightrim integer indicating upper trimming parameter (≥ 0). na.rm Should missing values be removed? ... additional arguments, see TLMoments.

## Details

The optimal weights will be calculated as described in "Kinsvater, Fried and Lilienthal (2015): Regional extreme value index estimation and a test of tail homogeneity, Environmetrics, DOI: 10.1002/env.2376, Section 3.2". If it's not possible to calculate optimal weights (negative eigenvaules of an estimated covarinace matrix), simple weights will be calculated: w_j=n_j/sum_{j=1}^d n_j

## Value

List of

• quant quantile calculation from an estimated GEV with a regional shape-parameter.

• param estimated parameter vector from a GEV (using L-moments or trimmed L-moments).

• w optimal or simple weighting (just returned if x is a matrix).

## Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 library("evd") # sample observations of 75 years at one station: x <- rgev(75) # x is a vector RegioGEV(x=x, p=0.95) x2 <- c(NA, NA, x[1:60], NA, x[61:75]) # vector of observations with missing values RegioGEV(x=x2, p=0.95) # NAs will be removed # sample observations of 100 years at 4 stations: set.seed(1053) x <- matrix(rgev(400, 2, 1, 0.3), ncol=4) RegioGEV(x=x, p=0.9, j=3, leftrim=0, rightrim=0) # optimal weighting RegioGEV(x=x, p=0.9, j=3, leftrim=0, rightrim=1) # optimal weighting # With missing values: x2 <- x x2[c(54, 89, 300)] <- NA RegioGEV(x=x2, p=0.9, j=3, leftrim=0, rightrim=0) # sample again observations of 100 years at 4 stations: set.seed(958) x <- matrix(rgev(400, 2, 1, 0.3), ncol=4) RegioGEV(x=x, p=0.9, j=3, leftrim=0, rightrim=0) # simple weighting

flood documentation built on May 30, 2017, 8:25 a.m.