Hello,
Why quantile(train, 0.9) ? If you use quantile(train) it seems to fit
the data much better. You haven't posted a data example so I've made up one.
library(eva) # needed for rgpd()
library(extRemes)
set.seed(1)
train <- rgpd(1e3, scale = 0.9, shape = -0.4)
thresh90 <- quantile(train)
model <- fevd(train,threshold =thresh90,type="GP")
model
x <- seq(0, 3, length.out = 1e3)
hist(train, prob = TRUE)
lines(x, dgpd(x, scale = 0.7595442, shape = -0.3707967), col = "red")
Hope this helps,
Rui Barradas
Em 27-11-2016 16:01, TicoR escreveu:
Let the train be the data set consisting of numbers that I need to fit.
Code is as follows:
library(extRemes)
thresh90 <- quantile(train, 0.90)
model<-fevd(train,threshold =thresh90,type="GP")
Model returns the following :
Negative Log-Likelihood Value: 317.7561
Estimated parameters:
scale shape
0.8858284 -0.3596079
Standard Error Estimates:
scale shape
0.04377744 0.03194536
Estimated parameter covariance matrix.
scale shape
scale 0.001916464 -0.001236952
shape -0.001236952 0.001020506
On Sun, Nov 27, 2016 at 9:08 PM, Rui Barradas <ruipbarra...@sapo.pt
<mailto:ruipbarra...@sapo.pt>> wrote:
Hello,
Please provide us with a reproducible example. A data exampla would
be nice and some working code, the code you are using to fit the data.
Rui Barradas
Em 27-11-2016 15:04, TicoR escreveu:
I am trying to fit some data using Generalized Pareto
Distribution in R
using extRemes
package(https://cran.r-project.org/web/packages/extRemes
<https://cran.r-project.org/web/packages/extRemes>) I
am able to get the parameters for the distribution. How would I
get the
simulated values for the model using the parameters?
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