In my data, sample mean =-0.3 and the histogram looks like t distribution;
therefore, I thought non-central t distribution may be a good fit. Anyway, I
try t distribution to get MLE. I found some warnings as follows; besides, I
got three parameter estimators: m=0.23, s=4.04, df=1.66. I want to simulate
the data with sample size 236 and this parameter estimates. Is the command
rt(236, df=1.66)? Where should I put m and s when I do simulation?
m s df
0.2340746 4.0447124 1.6614823
(0.3430796) (0.4158891) (0.2638703)
Warning messages:
1: In dt(x, df, log) : generates NaNs
2: In dt(x, df, log) : generates NaNs
3: In dt(x, df, log) :generates NaNs
4: In log(s) : generates NaNs
5: In dt(x, df, log) : generates NaNs
6: In dt(x, df, log) : generates NaNs
Thanks a lot,
Kate
----- Original Message -----
From: "Prof Brian Ripley" <[EMAIL PROTECTED]>
To: "kate" <[EMAIL PROTECTED]>
Cc: <r-help@r-project.org>
Sent: Thursday, May 08, 2008 10:02 AM
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 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 "fitting
distributions with R":
library(stats4) ## loading package stats4
ll<-function(lambda,alfa) {n<-200
x<-x.gam
-n*alfa*log(lambda)+n*log(gamma(alfa))-(alfa-
1)*sum(log(x))+lambda*sum(x)} ## -log-likelihood function
est<-mle(minuslog=ll, start=list(lambda=2,alfa=1))
Is anyone how how to write down -log-likelihood function for noncentral t
distribution?
Just use dt. E.g.
library(MASS)
?fitdistr
shows you a worked example for location, scale and df, but note the
comments. You could fit a non-central t, but it would be unusual to do
so.
Thanks a lot!!
Kate
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--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
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Oxford OX1 3TG, UK Fax: +44 1865 272595
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