ggevp function

Posterior Distribution with Parameters of Dual Gamma Generalized Extreme Value Distribution

MCMC runs of posterior distribution of data with parameters of Dual Gamma Generalized Extreme Value Distribution density, with parameters mu, sigma and xi.

ggevp(data, block, int=1000, delta)

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.
• delta: additional shape parameter of GGEV extension

Returns

An object of class ggevp that gives a list containing the points of posterior distributions of mu, sigma and xi of the dual gamma generalized extreme value distribution, the data, mean posterior, median posterior and the credibility interval of the parameters.

References

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

See Also

plot.ggevp, summary.ggevp

Examples

# Obtaining posterior distribution of a vector of simulated points
w=rggev(300,0.1,10,5,0.5)

# Obtaning 500 points of posterior distribution with delta=0.5
ajust=ggevp(w,1,200,0.5)