search for: pargev

...1000 data set. I know this is very easy, but I got stuck finding the right and fastest way in running it. IID50=Riidf[1:50,1:1000] #where IID50 is a dataframe consist of 1000 time series(as column) and 50 time scales (row). #what I tried to do: estIID50=rep(NA,1000) for (i in 1:1000) estIID50[i]=pargev(lmom.ub(IID50[1:50,i])) #warning message In estIID50[i] = pargev(lmom.ub(IID50[1:50, i])) : number of items to replace is not a multiple of replacement length #pargev is a function from lmomco package. I would like to apply it to the 1000 set of time series that I have in the IID50, without ha...

...1800, 21600, 32100, 27000, 24800, 28000, 35000, 32000, 25000, 15800, 28800, 29900, 28000, 25600, 19700, 25700, 29500, 26800, 30000, 29500) # 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....

...para2<-vec2par(c( 821.0445 , 260.7590 ,-0.1),'gev') #then I generate some data series using the GEV vectors: why<-rgev(150,xi= -0.1000 ,mu= 821.0445,sigma= 260.7590 ) #where xi=shape parameter #then I try to estimate the GEV parameters by L-moments of the generated series whyr<-pargev(lmom.ub(why)) whyr #what I get was a very bias estimate of k (here xi is location) xi alpha kappa 835.7773068 275.4656939 0.1683969 #even when I replicates it 1000 times and fit into the distribution, the shape parameter is still not near to -0.1, but it is going near to th...

Hello, I am using the lmomco package (lmom.ub and pargev) to compute the GEV parameters (location, scale, and shape), which are used to estimate return values. I was wondering how/if I can calculate upper and lower confidence (CI_u, CI_l) intervals for each return frequency using the GEV parameters to fill-in the table below? Xi (location) = 35....

Hi all, I have a list of 20000 data, and the list look like below. I wonder what is the simplest way to extract 'kappa' value (or 'xi' or 'alpha' for the matter) from each of the data. How can I simply code it without having to change the list to a dataframe first? Many thanks! $X19997 xi alpha kappa 784.7718640 165.4065141 -0.2709599 $X19998