# summary.ggevp: Summarizing Posterior Distribution with Parameters of GGEV In MCMC4Extremes: Posterior Distribution of Extreme Value Models in R

## Description

summary method for class "ggevp"

## Usage

 ```1 2``` ```## S3 method for class 'ggevp' summary(object, ...) ```

## Arguments

 `object` an object of class `"ggevp"`, usually, a result of a call to `ggevp`. `...` further arguments passed to or from other methods.

## Value

The function `summary.ggevp` computes and returns a list of summary statistics of the posterior distribution given in `object`.

 `postmean` mean posterior `postmedian` median posterior `postCI` credibility interval `fitm` fit measures for standard GGEV model

## References

Nascimento, F. F.; Bourguigon, M. ; Leao, J. S. (2015). Extended generalized extreme value distribution with applications in environmental data. HACET J MATH STAT.

`ggevp`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```# Obtaining posterior distribution of a vector of simulated points w=rggev(300,0.4,10,5,0.5) # Obtaning 600 points of posterior distribution with delta=0.5 fit=ggevp(w,1,200,0.5) a=summary(fit) # Choice the best delta from a Grid of possible values as Nascimento et al. (2015) ## Not run: fitmeasures=summary(fit)\$fitm ## Not run: delta=seq(0.1,2,0.2) ## Not run: results=array(0,c(length(delta),4)) ## Not run: for (i in 1:length(delta)) ## Not run: {ajust=ggevp(w,1,200,delta[i]) ## Not run: results[i,]=summary(ajust)\$fitm} # As commented in Nascimento 2015 paper, a criteria to choice the best delta would be # create a grid of values of theta and choose the best according the lowest fit measures ## Not run: resultsb=cbind(delta,results) ## Not run: colnames(resultsb)=c("delta","AIC","BIC","pD","DIC") ```