Displaying 3 results from an estimated 3 matches for "pelgev".
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pegged
2010 Feb 22
1
lmom: plotting log Pearson Type III
...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.est <- lis...
2008 Dec 16
2
Parameter Estimation - Generalized Extreme Value Distribution
Dear R helpers,
How do you estimate the (Location, Scale, Shape) parameters of Generalized Extreme Value distribution using R?
I have tried VGAM but just not able to write the R script.
Please advise.
With regards
Maithili
2012 Oct 12
0
(no subject)
Hi All,
I am performing GEV analysis on temperature/precipitation data using L
moments:
dim(data)
[1] 145 192 156
Lmoments <- apply(data, 1:2, function(x) samlmu(x,nmom=4,sort.data=TRUE))
params_GEV <- apply(Lmoments,2:3,pelgev)
location <- params_GEV[1,,] (xi)
scale <- params_GEV[2,,] (alpha)
shape <- params_GEV[3,,] (k)
I want to use the ks.boot function but I am unsure of how to implement it
using the pgev distribution.
For example:
ks.test(data[1,1],pgev,shape[1,1],location[1,1],scale[1,1])
ks.b...