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
I am trying to fit some data using Generalized Pareto Distribution in R
using extRemes package(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?
[[alternative HTML
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
sity
> College Station, TX 77840-4352
>
>
> - Original Message -
>
> From: "Lauren Vogric"
> To: r-help@r-project.org
> Sent: Friday, July 13, 2012 1:36:43 PM
> Subject: [R] Fitting data and removing outliers
>
> What I'm trying to do is create
way you suggest.
-
David L Carlson
Associate Professor of Anthropology
Texas A&M University
College Station, TX 77840-4352
- Original Message -
From: "Lauren Vogric"
To: r-help@r-project.org
Sent: Friday, July 13, 2012 1:36:43 PM
Subject:
What I'm trying to do is create best fit line in R for a set of data points
and then remove all the outliers to re-create a best fit. I can't use IQR
because the outliers I have in mind are easily within the range, but way out of
line for the best fit, which is ruining the fit. I'd rather thro
Hi,
Is there a way to fit a set of data with specified constraints (ie specified
the percentile values at say 99.9%) to a GEV distribution?
Thanks
--
View this message in context:
http://r.789695.n4.nabble.com/Fitting-data-with-constraints-to-GEV-distribution-tp4528418p4528418.html
Sent from
Dear All,
I am trying to fit some data to a Pareto distribution and would like to
estimate the parameters with the fitting. I have come across some options so
far. Unfortunately I haven't managed to get any of them to make the right
fits (as is evident when I check with the goodness of fit). On
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
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 fitdistr to estimate the value of \lambda.
1) Would it be c
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
essage-
> From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
> project.org] On Behalf Of sarahkm
> Sent: Wednesday, November 12, 2008 11:50 AM
> To: r-help@r-project.org
> Subject: [R] Fitting data to a sigmoidal curve
>
>
> Hi-
> I'm a biologist trying to figure
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
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 population. My question is: How do I fit my data to
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(
Hi, I have written out the log-likelihood function to fit some data I have
(called ONES20) to the non-central chi-squared distribution.
>library(stats4)
>ll<-function(lambda,k){x<-ONES20;
25573*0.5*lambda-25573*log(2)-sum(-x/2)-log((x/lambda)^(0.25*k-0.5))-log(besselI(sqrt(lambda*x),0.5*
Does anybody have any experience fitting data to the non-central chi-squared or
chi-squared distribution? I am trying to fit some data to this distribution but
there is error after error.
audaces fortuna iuvat
-
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