There's at least one package that can do zero-inflated gamma regression
(Rfast2::zigamma). I'm not sure it's ML, though.
On Thu, Jan 19, 2023 at 10:17 AM Jeff Newmiller
wrote:
> Beware of adding a constant... the magnitude of the constant used can have
> an outsized impact on the answer
Another situation for the presence of 0 is about dosage when
concentration is below the detection limit. It is not necessary to
discretize the data. We propose a method here:
Salvat-Leal I, Cortés-Gómez AA, Romero D, Girondot M (2022) New method
for imputation of unquantifiable values using
Beware of adding a constant... the magnitude of the constant used can have an
outsized impact on the answer obtained. See e.g.
https://gist.github.com/jdnewmil/99301a88de702ad2fcbaef33326b08b4
On January 19, 2023 3:49:29 AM PST, peter dalgaard wrote:
>Not necessarily homework, Bert. There's a
Not necessarily homework, Bert. There's a generic issue with MLE and rounded
data, in that gamma densities may be 0 at the boundary but small numbers are
represented as 0, making the log-likelihood -Inf.
The cleanest way out is to switch to a discretized distribution in the
likelihood, so
Is this homework? This list has a no-homework policy.
-- Bert
On Tue, Jan 10, 2023 at 8:13 AM Nyasha wrote:
>
> Please how can one go about this one? I don't know how to go about it.
>
> [[alternative HTML version deleted]]
>
> __
>
Please how can one go about this one? I don't know how to go about it.
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the
Hi Bert,
thanks for the quick reply. I spent a while searching before I posted, and
also read through the documentation for the mle fn and the maxLik and
bbmle packages. As you say, it seems likely I'm reinventing something
standard, but nothing I can find quite seems to do what I need. Hence
Are you familiar with R resources you can search?
1. CRAN task views:
https://cran.r-project.org/web/views/
2. For searching: https://rseek.org/
Searching on "maximum likelihood" there appeared to bring up relevant
resources.
3. RStudio resources: https://education.rstudio.com/
Note: RStudio
I'm fairly new to R. The language is amazing, but I'm having trouble
navigating packages. I have a solution that handles the problems I'm
working on, but I don't know if it could be solved more cleanly with mle,
bbmle, maxLik, etc..
Here's an example problem first. I have run many WAV files
Hi R-Users,
I am trying to estimate 95%-le VaR (Value-at-Risk) of a portfolio using
Extreme Value Theory. In particular, I'll use the Frechet distribution
(heavy left tail),
I have data on percentage returns ( R_t) for T = 5000 past dates. This data
has been divided into g = 50 non-overlapping
Hello,
I am going to estimate the parameter of the count model: pr(N=n)=
integration{B(x, alpha)-C(x,alpha)} by maximum likelihood estimation.
n-c(0,1,2,3) and F- (0,3,4,5) are the vectors of values and observed
frequency respectively. The function C(x,alpha) is not defined for n=0, but
Dear Sir/Madam
I am trying to get the Maximum Likelihood Estimation of a parameter in a
probability mass function. The problem is my pmf which includes a summation and
one integral. it is not similar to other known pdfs or pmfs such as normal,
exponential, poisson, .
Does anybody know
Dear Pari
On 21 December 2014 at 06:59, pari hesabi statistic...@hotmail.com wrote:
I am trying to get the Maximum Likelihood Estimation of a parameter
in a probability mass function. The problem is my pmf which includes
a summation and one integral. it is not similar to other known pdfs or
Thanks, Rolf and Ben,
Both solutions worked, but I finished by contraining parameters values using
optim().
Best,Bernardo Niebuhr
Em Segunda-feira, 8 de Dezembro de 2014 18:36, Rolf Turner
r.tur...@auckland.ac.nz escreveu:
I know nothing about the bbmle package and its mle2()
Dear all,
I am fitting models to data with mle2 function of the bbmle package.In
specific, I want to fit a power-law distribution model, as defined here
(http://arxiv.org/pdf/cond-mat/0412004v3.pdf), to data.
However, one of the parameters - xmin -, must be necessarily greater than zero.
What
I know nothing about the bbmle package and its mle2() function, but it
is a general truth that if you need to constrain a parameter to be
positive in an optimisation procedure a simple and effective approach is
to reparameterize using exp().
I.e. represent xmin as exp(lxmin) (say) and use
Rolf Turner r.turner at auckland.ac.nz writes:
I know nothing about the bbmle package and its mle2() function, but it
is a general truth that if you need to constrain a parameter to be
positive in an optimisation procedure a simple and effective approach is
to reparameterize using
HiI have a probability mass function similar to pr(N=n)=
integral(((2-x)^n)*(exp(ax-2))) - integral (((5-ax)^n)), both integrals are
defined over the interval(0,2) with respect to x. I am going to estimate the
parameter (a) with method of maximum likelihood estimation. The loglikelihood
is
Hi,
The problem I have is that the standard errors for the estimates doesn't make
any sense. Here is the background:
The values in vector a are seen as the true values and I want to estimate them
using mle. I make 100 disturbed vectors from a by adding noise, N(0,sigma^2).
For every disturbed
Hello
I know how to use R for estimating a parameter by using MLE if I have a simple
function f(x,a). I am trying to design a program for a complicated function
such as: g(.)=sum(integral(f(x,a,t,k))) where (a) is a parameter(needs to be
estimated) , integral depends on (t) and sum is over
Dear all:
I am writing the following small function for a probit likelihood.
As indicated, in order to avoid p=1 or p=0, I defined some precisions.
I feel however, that there might be a better way to do this.
Any help is greatly appreciated.
Hello,
You write a function of two arguments, 'par' and 'data' and do not use
them in the body of the function. Furthermore, what are b0, b1x and y?
Also, take a look at ?.Machine. In particular, couldn't you use
precision0 - .Machine$double.eps
precision1 - 1 - .Machine$double.eps
instead
Hallo,
I'm working with the mle function and I would like to ask you a couple
of questions.
My goal is to construct the historical value of v1(t), v2(t) and
v3(t) using the maximum likelihood estimation.
So, I need to optimize the following log-likelihood:
sum(E1_f[t,]*(v1*teta1[] +
Hi everyone,
I'm writing a thesis about financial copulas (gaussian and t-student
copulas) but i have problems about the R-code. I'll explain better: i
downloaded 10 time series about financial indeces and i have to apply the
gaussian copula. First i have to divide the ranks of the osservations by
click here
This is a link http://www.w3schools.com
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Thank you!!!
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Zoraida zmorales at ingellicom.com writes:
I need to estimate the parameters for negative binomial distribution (pdf)
using maximun likelihood, I also need to estimate the parameter for the
Poisson by ML, which can be done by hand, but later I need to conduct a
likelihood ratio test between
I need to estimate the parameters for negative binomial distribution (pdf)
using maximun likelihood, I also need to estimate the parameter for the
Poisson by ML, which can be done by hand, but later I need to conduct a
likelihood ratio test between these two distributions and I don't know how
to
Homework?? We don't do homework here. If not, ?optim or look at the CRAN
Optimize task view for optimizers. There is even a maxLik package that
might be useful.
-- Bert
On Mon, Jul 2, 2012 at 8:58 PM, Ali Tamaddoni alicivilizati...@gmail.comwrote:
Hi All
I have a data frame called nbd
Hi All
I have a data frame called nbd with two columns (x and T). Based on this
dataset I want to find the parameters of a distribution with the following
log-liklihood function and with r and alpha as its parameters:
log(gamma(nbd$x+r))-log(gamma(r))+r*log(alpha)-(r+nbd$x)*log(nbd$T+alpha)
Dear Mr. Matthieu Stigler
i so excited for your package 'tsDyn'.
firstly introduce myself, i student at Gadjah Mada University,Indonesia.
i'am new user of R and applying it for solving Bi-Variate ( interest rate
and inflation ) with threshold vector error correction model.
now, i writing my final
HI Berend,
Thank you for your reply.
2011/4/13 Berend Hasselman b...@xs4all.nl
Questions:
1. why are you defining Bo within a loop?
2. Why are you doing library(nleqslv) within the loop?
Yes, I see what you mean. There's no reason for defining that within the
loop.
Doing both those
On 14-04-2011, at 09:00, Kristian Lind wrote:
HI Berend,
Thank you for your reply.
..
Finally the likelihood function at the end of your code
#Maximum likelihood estimation using mle package
library(stats4)
#defining loglikelighood function
#T - length(v)
#minuslogLik -
Albyn and others,
Thank you for your replies.
In order to be more specific I've constructed my program. I know it's long
and in some places quite messy. It works until the last part where the
log-likelihood function has to be defined and maximized wrt the parameters.
The log-likelihood has the
Questions:
1. why are you defining Bo within a loop?
2. Why are you doing library(nleqslv) within the loop?
Doing both those statements outside the loop once is more efficient.
In your transdens function you are not using the function argument
parameters, why?
Shouldn't there be a
Hi there,
I'm trying to solve a ML problem where the likelihood function is a function
of two numerical procedures and I'm having some problems figuring out how to
do this.
The log-likelihood function is of the form L(c,psi) = 1/T sum [log (f(c,
psi)) - log(g(c,psi))], where c is a 2xT matrix of
Hi Kristian
The obvious approach is to treat it like any other MLE problem: evaluation
of the log-likelihood is done as often as necessary for the optimizer
you are using: eg a call to optim(psi,LL,...) where LL(psi) evaluates
the log likelihood at psi. There may be computational
to clarify: by if you knew that LL(psi+eps) were well approximated
by LL(psi), for the values of eps used to evaluate numerical
derivatives of LL.
I mean the derivatives of LL(psi+eps) are close to the derivatives of LL(psi),
and perhaps you would want the hessian to be close as well.
Dear List,
This problem is more a statistic one than a R one.
Any one can recommend me some references website or online paper on
maximum likelihood estimation?I'm now working on that,while still
doubt how to prove that the estimated parameters are normal
distributed.
Thanks for your time and
Check out Casella and Berger's Statistical Inference. ISBN 978-81-315-0394-2
or http://en.wikipedia.org/wiki/Maximum_likelihood as an online reference.
--Mark J. Lamias
From: Ning Cheng wakinchaue...@gmail.com
To: r-help@r-project.org
Sent: Sunday, February 27, 2011 3:19 PM
Subject: [R] MLE
I am partial to Gary King's book:
Unifying Political Methodology: The Likelihood Theory of Statistical Inference
(University of Michigan Press, 1998)
Cheers
David Cross
d.cr...@tcu.edu
www.davidcross.us
On Feb 27, 2011, at 2:19 PM, Ning Cheng wrote:
Dear List,
This problem is more a
Hi,
I am having problems carrying out a mle for 3 parameters in a non-homogenous
poisson process.
I am trying to use the optim function to minimise the -ve log-likelihood.
When I use assumed values of my three parameters (20,1,1) the -ve
log-likelihood function returns a value of 1309122 but I
Hi,
I am looking for some help regarding the use of the mle function.
I am trying to get mle for 3 parameters (theta0, theta1 and theta2) that
have been defined in the the log-likelihood equation as theta0=theta[1],
theta1=theta[2] and theta2=theta[3].
My R code for mle is:
mle(Poisson.lik,
Hi,
I am looking for some help regarding the use of the mle function.
I am trying to get mle for 3 parameters (theta0, theta1 and theta2) that
have been defined in the the log-likelihood equation as theta0=theta[1],
theta1=theta[2] and theta2=theta[3].
My R code for mle is:
mle(Poisson.lik,
Hello,
is there somebody who can help me with my question (see below)?
Antje
On 1 February 2011 09:09, Antje Niederlein niederlein-rs...@yahoo.de wrote:
Hello,
I tried to use mle to fit a distribution(zero-inflated negbin for
count data). My call is very simple:
mle(ll)
ll() takes the
Hello,
is there somebody who can help me with my question (see below)?
Antje
On 1 February 2011 09:09, Antje Niederlein niederlein-rs...@yahoo.de wrote:
Hello,
I tried to use mle to fit a distribution(zero-inflated negbin for
count data). My call is very simple:
mle(ll)
ll() takes
Hello,
I tried to use mle to fit a distribution(zero-inflated negbin for
count data). My call is very simple:
mle(ll)
ll() takes the three parameters, I'd like to be estimated (size, mu
and prob). But within the ll() function I have to judge if the current
parameter-set gives a nice fit or
Hey,
I've got a problem with the estimation of a multivariate t-distribution.
I've got 200 observations vor 20 variables.
Now I want to estimate the parameters of the densityfunction of the
multivarate t-distribution with mean=0.
I found a function mst.mle in the package sn, but here it is
Folks,
I'm kind of newbie in R, but with some background in Matlab and VBA
programming. Last month I was implementing a Maximum Likelihood Estimation
in Matlab, but the algorithms didn't converge. So my academic advisor
suggested using R. My problem is: estimate a mean reverting jump diffusion
...@r-project.org] On
Behalf Of jckval
Sent: Monday, January 04, 2010 5:53 PM
To: r-help@r-project.org
Subject: [R] MLE optimization
Folks,
I'm kind of newbie in R, but with some background in Matlab and VBA
programming. Last month I was implementing a Maximum Likelihood Estimation
in Matlab
Hello
On 1/4/10, jckval jcnogueirafi...@gmail.com wrote:
Alternatively, could anyone suggest a good MLE tutorial and package?
Search for MLE on Rseek.org and among other results check the Task
Views. Also, search for MLE in vignettes on RSiteSearch [1].
[1]
://n4.nabble.com/user/SendEmail.jtp?type=nodenode=998666i=2]
On
Behalf Of jckval
Sent: Monday, January 04, 2010 5:53 PM
To: [hidden
email]http://n4.nabble.com/user/SendEmail.jtp?type=nodenode=998666i=3
Subject: [R] MLE optimization
Folks,
I'm kind of newbie in R, but with some background
Brian Ripley sometimes on this list or elsewhere suggested to
reparametrize as 1/k. I have used that with good results. But you
should be aware that
usually data contains very little information about k, so thhat
if you do not have a lot more than 100 observations you coukld
be out of luck. You
Given X1,...,Xn ~ t_k(mu,sigma) student t distribution with k degrees
of freedom, mean mu and standard deviation sigma, I want to obtain the
MLEs of the three parameters (mu, sigma and k). When I try traditional
optimization techniques I don't find the MLEs. Usually I just get
k-infty. Does
k - infinity gives the normal distribution. You probably don't care
much about the difference between k=1000 and k=10, so you might
try reparametrizing df on [1,infinity) to a parameter on [0,1]...
albyn
On Thu, Dec 10, 2009 at 02:14:26PM -0600, Barbara Gonzalez wrote:
Given X1,...,Xn ~
Thank you.
I actually found fitdistr() in the package MASS, that estimates the
df, but it does a very bad job. I know that the main problem is that
the t distribution has a lot of local maxima, and of course, when
k-infty we have the Normal distribution, which has nice and easy to
obtain MLEs.
I
Hello Liang,
Besides looking at ?optim, you may also want to look into this nice
working example www.mayin.org/ajayshah/KB/R/documents/mle/mle.html
Regards,
Francisco
Francisco J. Zagmutt
Vose Consulting
1643 Spruce St., Boulder
Boulder, CO, 80302
USA
www.voseconsulting.com
Liang Wang
Hi, dear R users
I am a newbie in R and I need to use the method of meximum likelihood to fit a
Weibull distribution to my survival data. I use optim as follows:
optim(c(1.14,0.25),weibull.like,mydata=mydata,method=L-BFGS-B,hessian = TRUE)
My question is: how do I setup the constraints that
Please read ?optim and about its arguments
lower, upperBounds on the variables for the L-BFGS-B method.
Uwe Ligges
Liang Wang wrote:
Hi, dear R users
I am a newbie in R and I need to use the method of meximum likelihood to fit a Weibull
distribution to my survival data. I use optim as
In general Poisson data consists of a pair of numbers (y,n), where y is
the event count for the unit and n is the size of the unit. The Poisson
MLE is sum(y)/sum(n). A general example is county level data where y is
the number of events (rare cancer) and n is the county size. Two
special cases
On Oct 26, 2009, at 11:25 PM, ankush...@yahoo.com wrote:
Hi,
I am using the fitdistr of MASS to get the MLE for the lambda of a
Poisson distribution.
When i run the fitdistr command, i get an output that looks like -
lambda
3.75
(0.03343)
Couple of questions -
1. is the
What is wrong with using
mean(x)
to get the MLE of the poisson lambda?
Kjetil
On Tue, Oct 27, 2009 at 9:17 AM, David Winsemius dwinsem...@comcast.net wrote:
On Oct 26, 2009, at 11:25 PM, ankush...@yahoo.com wrote:
Hi,
I am using the fitdistr of MASS to get the MLE for the lambda of a
Kjetil Halvorsen wrote:
What is wrong with using
mean(x)
to get the MLE of the poisson lambda?
and
mean(x)/length(x)
to get its estimated variance.
-Peter Ehlers
Kjetil
On Tue, Oct 27, 2009 at 9:17 AM, David Winsemius dwinsem...@comcast.net wrote:
On Oct 26, 2009, at 11:25 PM,
lambda?
Thanks again!
Ankush
From: Peter Ehlers ehl...@ucalgary.ca
To: Kjetil Halvorsen kjetilbrinchmannhalvor...@gmail.com
Sent: Tue, October 27, 2009 10:15:31 AM
Subject: Re: [R] MLE for lambda of Poisson distribution using fitdistr
Kjetil Halvorsen wrote
Ehlers ehl...@ucalgary.ca
To: Kjetil Halvorsen kjetilbrinchmannhalvor...@gmail.com
Cc: r-h...@stat.math.ethz.ch; ankush...@yahoo.com
Sent: Tue, October 27, 2009 10:15:31 AM
Subject: Re: [R] MLE for lambda of Poisson distribution using fitdistr
Kjetil Halvorsen wrote:
What is wrong with using
... gamlss has a great
documentation, but it's a bit overwhelming.
Kind regards
Susanne
Susanne Balzer
PhD Student
Institute of Marine Research
N-5073 Bergen, Norway
Phone: +47 55 23 69 45
susanne.balzer at imr.no
www.imr.no
[R] MLE for noncentral t distribution
Hi,
I am using the fitdistr of MASS to get the MLE for the lambda of a Poisson
distribution.
When i run the fitdistr command, i get an output that looks like -
lambda
3.75
(0.03343)
Couple of questions -
1. is the MLE 0.03343 for the lambda of the given distribution then?
I am using mle as a wrapper from optim( ). How would I extract the
convergence code, to know that optim( ) converged properly?
Thanks,
Stephen Collins, MPP | Analyst
Global Strategy | Aon Benfield
[[alternative HTML version deleted]]
__
Stephen Collins wrote:
I am using mle as a wrapper from optim( ). How would I extract the
convergence code, to know that optim( ) converged properly?
The return value from optim is contained in the details slot, so
f...@details$convergence
[1] 0
--
O__ Peter Dalgaard
Stephen Collins-6 wrote:
I am using mle as a wrapper from optim( ). How would I extract the
convergence code, to know that optim( ) converged properly?
library(stats4)
example(mle)
slotNames(fit1)
f...@details
f...@details$convergence
--
View this message in context:
INTRODUCTION TO THE PROBLEM
I am trying to fit a distribution to a dataset. The distribution that I
am currently considering is the (3-parameter) Singh-Maddala (Burr)
distribution. The final model will fix the mean of the distribution to a
given value and estimate the remaining parameters
Is there a way to code the mle() function in library stats4 such that it
switches optimizing methods midstream (i.e. BFGS to Newton and back to
BFGS, etc.)?
Thanks,
Stephen Collins, MPP | Analyst
Health Benefits | Aon Consulting
[[alternative HTML version deleted]]
Just wanted to thank everyone for their help, I think I mostly managed to
solve my problem.
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On 08-Apr-09 23:39:36, Ted Harding wrote:
On 08-Apr-09 22:10:26, Ravi Varadhan wrote:
EM algorithm is a better approach for maximum likelihood estimation
of finite-mixture models than direct maximization of the mixture
log-likelihood. Due to its ascent properties, it is guaranteed to
Hello everyone,
I'm trying to use mle from package stats4 to fit a bi/multi-modal
distribution to some data, but I have some problems with it.
Here's what I'm doing (for a bimodal distribution):
# Build some fake binormally distributed data, the procedure fails also with
real data, so the
_nico_ wrote:
Hello everyone,
I'm trying to use mle from package stats4 to fit a bi/multi-modal
distribution to some data, but I have some problems with it.
Here's what I'm doing (for a bimodal distribution):
# Build some fake binormally distributed data, the procedure fails also
, of course.
-- Bert
Bert Gunter
Genentech Nonclinical Biostatistics
650-467-7374
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Ben Bolker
Sent: Wednesday, April 08, 2009 12:47 PM
To: r-help@r-project.org
Subject: Re: [R] MLE
Ben Bolker wrote:
Here's some tweaked code that works.
[cut]
Thanks, that saved me a few headaches. I also find out the answer to my
(dumb) question #5, which is obviously to call f with the returned
parameters or use the logLik function.
I will have a look at the mixture model
_nico_ wrote:
Hello everyone,
I'm trying to use mle from package stats4 to fit a bi/multi-modal
distribution to some data, but I have some problems with it.
Here's what I'm doing (for a bimodal distribution):
# Build some fake binormally distributed data, the procedure fails also with
real
of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University
Ph. (410) 502-2619
email: rvarad...@jhmi.edu
- Original Message -
From: Bert Gunter gunter.ber...@gene.com
Date: Wednesday, April 8, 2009 4:14 pm
Subject: Re: [R] MLE for bimodal distribution
To: 'Ben Bolker
On 08-Apr-09 22:10:26, Ravi Varadhan wrote:
EM algorithm is a better approach for maximum likelihood estimation
of finite-mixture models than direct maximization of the mixture
log-likelihood. Due to its ascent properties, it is guaranteed to
converge to a local maximum. By theoretical
: rvarad...@jhmi.edu
- Original Message -
From: ted.hard...@manchester.ac.uk (Ted Harding)
Date: Wednesday, April 8, 2009 7:43 pm
Subject: Re: [R] MLE for bimodal distribution
To: r-h...@stat.math.ethz.ch
On 08-Apr-09 22:10:26, Ravi Varadhan wrote:
EM algorithm is a better approach
I am a student (and very to new to R) working on a senior design project that
is attempting to determine the demand distributions for single copy
newspaper draws at individual sales outlet locations. Our sales data is
right-censored, because sell-outs constitute a majority of the data, and we
Dears,
Any help?
Thanks,
LFRC
LFRC wrote:
Dears,
I'm trying to find the parameters (a,b, ... l) that optimize the function
(Model)
described below.
1) How can I set some constraints with MLE2 function? I want to set p10,
p20,
p30, p1p3.
2) The code is giving the following
Dears,
I'm trying to find the parameters (a,b, ... l) that optimize the function
(Model)
described below.
1) How can I set some constraints with MLE2 function? I want to set p10,
p20,
p30, p1p3.
2) The code is giving the following warning.
Warning: optimization did not converge (code 1)
May I ask one statistics related question please? I have one query on MLE
itself. It's property says that, for large sample size it is normally
distributed [i.e. asymptotically normal]. On the other hand it is Efficient as
well. My doubt is, how this two asymptotic properties exist
Greetings, all
I am having difficulty getting the fitdistr() function to return without
an error on my data. Specifically, what I'm trying to do is get a
parameter estimation for fracture intensity data in a well / borehole.
Lower bound is 0 (no fractures in the selected data interval), and upper
Fox, Aaron wrote:
Greetings, all
I am having difficulty getting the fitdistr() function to return without
an error on my data. Specifically, what I'm trying to do is get a
parameter estimation for fracture intensity data in a well / borehole.
Lower bound is 0 (no fractures in the selected data
Fox, Aaron Afox at golder.com writes:
Greetings, all
I am having difficulty getting the fitdistr() function to return without
an error on my data. Specifically, what I'm trying to do is get a
parameter estimation for fracture intensity data in a well / borehole.
Lower bound is 0 (no
]
k To: kate [EMAIL PROTECTED]
k Cc: r-help@r-project.org
k Sent: Thursday, May 08, 2008 10:02 AM
k Subject: Re: [R] MLE for noncentral t distribution
On Thu, 8 May 2008, kate wrote:
I have a data with 236 observations. After plotting the histogram, I
found
, log) : generates NaNs
k Thanks a lot,
k Kate
k - Original Message -
k From: Prof Brian Ripley [EMAIL PROTECTED]
k To: kate [EMAIL PROTECTED]
k Cc: r-help@r-project.org
k Sent: Thursday, May 08, 2008 10:02 AM
k Subject: Re: [R] MLE for noncentral t
I have a data with 236 observations. After plotting the histogram, I found that
it looks like non-central t distribution. I would like to get MLE for mu and
df.
I found an example to find MLE for gamma distribution from fitting
distributions with R:
library(stats4) ## loading package stats4
On 5/8/2008 10:34 AM, kate wrote:
I have a data with 236 observations. After plotting the histogram, I found that it looks like non-central t distribution. I would like to get MLE for mu and df.
I found an example to find MLE for gamma distribution from fitting distributions
with R:
On Thu, 8 May 2008, kate wrote:
I have a data with 236 observations. After plotting the histogram, I
found that it looks like non-central t distribution. I would like to get
MLE for mu and df.
So you mean 'non-central'? See ?dt.
I found an example to find MLE for gamma distribution from
Message -
From: Duncan Murdoch [EMAIL PROTECTED]
To: kate [EMAIL PROTECTED]
Cc: r-help@r-project.org
Sent: Thursday, May 08, 2008 9:46 AM
Subject: Re: [R] MLE for noncentral t distribution
On 5/8/2008 10:34 AM, kate wrote:
I have a data with 236 observations. After plotting the histogram
QRMlib has routines for fitting t distributions. Have a look at that
package. Also sn has routines for skew-t distributions
David Scott
On Thu, 8 May 2008, kate wrote:
I have a data with 236 observations. After plotting the histogram, I found that
it looks like non-central t
Hi just wondering if there is a package that can get the maximum likelihood
or method of moments estimator for distributions with censored data? The
distributions I'm interested in are: Exponential, pareto, beta, gamma and
lognormal.
Look at the survreg function in the survival library.
Le mar. 22 janv. à 17:47, Thomas Downey a écrit :
Hi just wondering if there is a package that can get the maximum
likelihood
or method of moments estimator for distributions with censored
data? The
distributions I'm interested in are: Exponential, pareto, beta,
gamma and
Hi just wondering if there is a package that can get the maximum likelihood
or method of moments estimator for distributions with censored data? The
distributions I'm interested in are: Exponential, pareto, beta, gamma and
lognormal.
--
View this message in context:
Hello,
I'm trying to obtain a maximum likelyhood estimate of a gaussian model
by the MLE command, as I did with a Poisson model:
x - rep(1:2,each=500)
y - rnorm(length(x), mean=10+3*x, sd=1)
glm1 - glm(y ~ x , family=gaussian())
library(stats4)
func1 - function(alfa=10, beta=3, sigma=1)
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