Thanks for your quick reply.
I try the command as follows,
library(stats4) ## loading package stats4
ll <- function(change, ncp, df) {-sum(dt(x, ncp=ncp, df=df,
log=TRUE))}#-log-likelihood function
est<-mle(minuslog=ll, start=list(ncp=-0.3,df=2))
But the warnings appears as follows,
invalid class "mle" object: invalid object for slot "fullcoef" in class
"mle": got class "list", should be or extend class "numeric"
When I typed warnings(), I get
In dt(x, ncp = ncp, df = df, log = TRUE) :
full precision was not achieved in 'pnt'
Does anyone know how to solve it?
Thanks,
Kate
----- Original 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, 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
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?
dt() has a non-centrality parameter and a log parameter, so it would
simply be
ll <- function(x, ncp, df) sum(dt(x, ncp=ncp, df=df, log=TRUE))
Make sure you convert mu into the ncp properly; the man page says how ncp
is interpreted.
Duncan Murdoch
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