# gumbelp: Posterior Distribution with GEV, where xi=0 In MCMC4Extremes: Posterior Distribution of Extreme Value Models in R

## Description

MCMC runs of posterior distribution of data with parameters of Generalized Extreme Value (GEV) density, in the particular case where xi=0 with parameters mu, sigma.

## Usage

 1 gumbelp(data, block, int=1000)

## Arguments

 data data vector block the block size. A numeric value is interpreted as the number of data values in each successive block. All the data is used, so the last block may not contain block observations. int number of iteractions selected in MCMC. The program selects 1 in each 10 iteraction, then thin=10. The first thin*int/3 iteractions is used as burn-in. After that, is runned thin*int iteraction, in which 1 of thin is selected for the final MCMC chain, resulting the number of int iteractions

## Value

An object of class gumbelp that gives a list containing the points of posterior distributions of mu and sigma of the gev distribution, the data, mean posterior, median posterior and the credibility interval of the parameters.

## Note

The non-informative prior distribution of these parameters are Normal(0,1000) for the parameter mu and Gamma(0.001,0.001) for the parameter sigma. During the MCMC runs, screen shows the proportion of iteractions made.

## Examples

 1 2 3 4 5 6 7 8 9 10 11 12 # Obtaining posterior distribution of a vector of simulated points x=rgev(200,xi=0.0001,mu=10,sigma=5) # Obtaning 600 points of posterior distribution ajuste=gumbelp(x,1,600) # Maxima of each month in river nidd data ## Not run: data(nidd.annual) ## Not run: out=gumbelp(nidd.annual,1,500) # Predictive distribution for 15 day maxima ibovespa returns ## Not run: data(ibovespa) ## Not run: postibv=gumbelp(ibovespa[,4],15,500)

### Example output

[1] 0.01111111
[1] 0.02222222
[1] 0.03333333
[1] 0.04444444
[1] 0.05555556
[1] 0.06666667
[1] 0.07777778
[1] 0.08888889
[1] 0.1
[1] 0.1111111
[1] 0.1222222
[1] 0.1333333
[1] 0.1444444
[1] 0.1555556
[1] 0.1666667
[1] 0.1777778
[1] 0.1888889
[1] 0.2
[1] 0.2111111
[1] 0.2222222
[1] 0.2333333
[1] 0.2444444
[1] 0.2555556
[1] 0.2666667
[1] 0.2777778
[1] 0.2888889
[1] 0.3
[1] 0.3111111
[1] 0.3222222
[1] 0.3333333
[1] 0.3444444
[1] 0.3555556
[1] 0.3666667
[1] 0.3777778
[1] 0.3888889
[1] 0.4
[1] 0.4111111
[1] 0.4222222
[1] 0.4333333
[1] 0.4444444
[1] 0.4555556
[1] 0.4666667
[1] 0.4777778
[1] 0.4888889
[1] 0.5
[1] 0.5111111
[1] 0.5222222
[1] 0.5333333
[1] 0.5444444
[1] 0.5555556
[1] 0.5666667
[1] 0.5777778
[1] 0.5888889
[1] 0.6
[1] 0.6111111
[1] 0.6222222
[1] 0.6333333
[1] 0.6444444
[1] 0.6555556
[1] 0.6666667
[1] 0.6777778
[1] 0.6888889
[1] 0.7
[1] 0.7111111
[1] 0.7222222
[1] 0.7333333
[1] 0.7444444
[1] 0.7555556
[1] 0.7666667
[1] 0.7777778
[1] 0.7888889
[1] 0.8
[1] 0.8111111
[1] 0.8222222
[1] 0.8333333
[1] 0.8444444
[1] 0.8555556
[1] 0.8666667
[1] 0.8777778
[1] 0.8888889
[1] 0.9
[1] 0.9111111
[1] 0.9222222
[1] 0.9333333
[1] 0.9444444
[1] 0.9555556
[1] 0.9666667
[1] 0.9777778
[1] 0.9888889
[1] 1

MCMC4Extremes documentation built on May 1, 2019, 8:50 p.m.