Hi,
The error message is clear in that the gradient cannot be evaluated at your
starting value for the parameters.
Is your likelihood a smooth function of parameters? If so, then provide a
different starting value. If it is not smooth, then you may have to use a
method that does not depend on gradients, such as Nelder-Mead.
Can you provide a reproducible example, which would help us dig deeper into
your problem?
Ravi.
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Ravi Varadhan, Ph.D.
Assistant Professor, The Center on Aging and Health
Division of Geriatric Medicine and Gerontology
Johns Hopkins University
Ph: (410) 502-2619
Fax: (410) 614-9625
Email: rvaradhan at jhmi.edu
Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html
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-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of John-Paul Taylor
Sent: Friday, April 03, 2009 8:30 AM
To: r-help at r-project.org
Subject: Re: [R] Geometric Brownian Motion Process with Jumps
Hi,
I have been using maxLik to do some MLE of Geometric Brownian Motion Process
and everything has been going fine, but know I have tried to do it with
jumps. I have create a vector of jumps and then added this into my
log-likelihood equation, know I am getting a message:
NA in the initial gradient
My codes is hear
#
n<-length(combinedlr)
j<-c(1,2,3,4,5,6,7,8,9,10)
gbmploglik<-function(param){
mu<-param[1]
sigma<-param[2]
lamda<-param[3]
nu<-param[4]
gama<-param[5]
logLikVal<- - n*lamda - .5*n*log(2*pi) + sum(log(sum(for(j in
1:10)(cat((lamda^j/factorial(j))*(1/((sigma^2+j*gama^2)^.5)*exp( -
(combinedlr-mu-j*nu)^2/2*(sigma^2+j*gama^2))))))))
logLikVal
}
rescbj<- maxLik(gbmploglik, grad = NULL, hess = NULL, start=c(0,1,1,1,1),
method = "Newton-Raphson")
summary(rescbj)
#
I am also was wondering if anyone know if there was a package that dealt
with Geometric Brownian Motion Process augmented with jumps. Then I could
just put that into my code and might resolve the issue.
Any suggest as to how to resolved this issue, are greatly apprecaited.
Yours truly,
JP
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