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...# estimate moments moments = samlmu(mackenzieRiver, sort.data = TRUE) log.moments <- samlmu( log(mackenzieRiver), sort.data = TRUE ) # estimate parameters parGEV <- pelgev(moments) # GEV parPE3 <- pelpe3(moments) # Pearson parLPE3 <- pelpe3(log.moments) # log Pearson # plot result evplot(mackenzieRiver, rp.axis = TRUE) evdistq(quagev, parGEV, col = 'black') evdistq(quape3, parPE3, col = 'blue') # estimate 1:100 yr event flood.est <- list( GEV = quagev(0.99, parGEV), PE3 = quape3(0.99, parPE3), LogPE3 = exp(quape...

Hey, I have a set of income data which I'd like to fit to a gamma distribution. How can I estimate the two parameters of the gamma distribution for a vector, e.g. c(2039L, 2088L, 5966L, 2353L, 1966L, 2312L, 3305L, 2013L, 3376L, 3363L, 3567L, 4798L, 2032L, 1699L, 3001L, 2329L, 3944L, 2568L, 1699L, 4545L) I sense this will be a very easy one-liner, but my searching didn't come up with

Hi, I have been having difficulties in finding packages/ codes that simplify plotting of a GEV fitted to dataset (by L-moments) that would print out graph comprising dataset versus gumbel reduced variate n return period at the same. Anyone can help me on this? Thanks. [[alternative HTML version deleted]]