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)
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:
scal
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(h
Vogric" , r-help@r-project.org
Sent: Friday, July 13, 2012 3:15:25 PM
Subject: Re: [R] Fitting data and removing outliers
They are due to measurement error, sample of a different population, or
... ? What is the unusual event? Does it explain something important
about the system that you
, July 13, 2012 3:24 PM
To: David L Carlson
Cc: Lauren Vogric; r-help@r-project.org
Subject: Re: [R] Fitting data and removing outliers
Do you have a good reason to throw these points out?
On Fri, Jul 13, 2012 at 2:17 PM, David L Carlson wrote:
I didn't actually see any question in this posting
Do you have a good reason to throw these points out?
On Fri, Jul 13, 2012 at 2:17 PM, David L Carlson wrote:
> I didn't actually see any question in this posting, but instead of removing
> the outliers consider using a robust linear model.
>
> library(MASS)
> ?rlm
>
> The TeachingDemos package h
I didn't actually see any question in this posting, but instead of removing the
outliers consider using a robust linear model.
library(MASS)
?rlm
The TeachingDemos package has a data set called outliers to show what can
happen when you iteratively remove "outliers" in the way you suggest.
Serebrenik, A. tue.nl> writes:
>
> Dear all,
>
> I have a set of data which seem to be distributed almost exponentially but
> only on [0;1]. I guess that the probability distribution in this case
> would look like
>
> \frac{lambda}{1-e^{-\lambda}) e^{-\lambda x}
>
> I would like to use fitdis
Greg Snow wrote:
Sarah,
Doing:
RSiteSearch('gompertz', restrict='functions')
At the command prompt gives several promising results.
Hope this helps,
--
Gregory (Greg) L. Snow Ph.D.
And you can also do:
nobs <- length(data$salam.size.observed)
fn<-function(p){
salam.size.mod
Sarah,
Doing:
> RSiteSearch('gompertz', restrict='functions')
At the command prompt gives several promising results.
Hope this helps,
--
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
[EMAIL PROTECTED]
801.408.8111
> -Original Message-
> From: [EMAIL PR
Try this command from within R:
RSiteSearch("gompertz")
On Wed, Nov 12, 2008 at 1:50 PM, sarahkm <[EMAIL PROTECTED]> wrote:
>
> Hi-
> I'm a biologist trying to figure out the growth rate of salamanders in
> different ponds. I collected individuals from various populations at
> different dates, an
On 13/11/2008, at 7:50 AM, sarahkm wrote:
Hi-
I'm a biologist trying to figure out the growth rate of salamanders in
different ponds. I collected individuals from various populations at
different dates, and using the size and date collected, I want to
figure out
the growth curve of each popu
This is what I might do:
> y <- rchisq( 1000, df=10, ncp=2 )
> library( stats4 )
> res <- mle( function(x,z) -sum( dchisq(y, x, z , log=TRUE ) ), start=list(
> x=5, z=5 ) )
> coef(res)
x z
10.355711 1.586123
>
> ## or just to keep clear of boundary constraints:
>
> res <- mle(
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