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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