[R] gradient option in 'nlm' function
Dear list members, I am trying to use "nlm"
function to maximize a mixture likelihood of beta densities. There are five unknown
parameters in the likelihood. Since I can get the analytic gradient, I attach the
"gradient" attribute in my target likehood function. The code is as the following
target <- function(x) { resp
<- grad <- rep(0,5) ## I have 5
paramters grad[1] <- ...; grad[2] <- ...; grad[3] <-
...; grad[4] <- ...; grad[5] <- ... attr(resp,
"gradient") <- grad resp }nlm(targ,
c(1,2,3,4,5))The R gave me this error
message "Error
in nlm(targ, c(1,2,3,4,5)) : probable coding error in analytic
gradient"^^I ran my
code for defining gradient separately, and there seemed to be no coding error. I
provided other options for nlm() function and it gave me the same error message. I
removed the gradient part and let nlm() do the numerical derivative, it ran but the
algorithm was not converging. I want to know if nlm can
handle multiple parameters problems, and if yes, was there any error in my code? How
do I properly provide the gradient for my function? Thanks
a
lot, Yuan Ji, Ph.D.Assistant ProfessorDepartment of BistatisticsThe University of Texas M.D. Anderson Cancer Center1515 Holcombe Blvd. - Unit 447Houston, TX 77030-4009(713)794-4153__
[EMAIL PROTECTED] mailing list
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[R] gradient option in "nlm" function
Dear list members,
I am trying to use "nlm" function to maximize a mixture likelihood of beta
densities. There are five unknown parameters in the likelihood. Since I can
get the analytic gradient, I attach the "gradient" attribute in my target
likehood function. The code is as the following
> target <- function(x)
> { resp <-
> grad <- rep(0,5)## I have 5 paramters
> grad[1] <- ...; grad[2] <- ...; grad[3] <- ...; grad[4] <- ...; grad[5]
<- ...
> attr(resp, "gradient") <- grad
> resp
> }
> nlm(targ, c(1,2,3,4,5))
The R gave me this error message
"Error in nlm(targ, c(1,2,3,4,5)) : probable coding error in analytic
gradient"
I ran my code for defining gradient separately, and there seemed to be no
coding error. I provided other options for nlm() function and it gave me
the same error message. I removed the gradient part and let nlm() do the
numerical derivative, it ran but the algorithm was not converging.
I want to know if nlm can handle multiple parameters problems, and if yes,
was there any error in my code? How do I properly provide the gradient for
my function?
Thanks a lot,
Yuan Ji, Ph.D.
Assistant Professor
Department of Bistatistics
The University of Texas M.D. Anderson Cancer Center
1515 Holcombe Blvd. - Unit 447
Houston, TX 77030-4009
(713)794-4153
__
[EMAIL PROTECTED] mailing list
https://www.stat.math.ethz.ch/mailman/listinfo/r-help
Re: [R] gradient option in "nlm" function
Oh, that's a typo. I passed the function target. Seems to me R requires
some kind of specific syntex.
Yuan Ji, Ph.D.
Assistant Professor
Department of Bistatistics
The University of Texas M.D. Anderson Cancer Center
1515 Holcombe Blvd. - Unit 447
Houston, TX 77030-4009
(713)794-4153
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On Mon, 17 Nov 2003 [EMAIL PROTECTED] wrote:
> Dear list members,
>
> I am trying to use "nlm" function to maximize a mixture likelihood of
beta
> densities. There are five unknown parameters in the likelihood. Since I
can
> get the analytic gradient, I attach the "gradient" attribute in my target
> likehood function. The code is as the following
>
> > target <- function(x)
> > { resp <-
> > grad <- rep(0,5)## I have 5 paramters
> > grad[1] <- ...; grad[2] <- ...; grad[3] <- ...; grad[4] <- ...;
grad[5]
> <- ...
> > attr(resp, "gradient") <- grad
> > resp
> > }
> > nlm(targ, c(1,2,3,4,5))
It would probably help if you passed the function target() to
nlm(), rather than passing targ().
> The R gave me this error message
>
> "Error in nlm(targ, c(1,2,3,4,5)) : probable coding error in analytic
> gradient"
>
> I ran my code for defining gradient separately, and there seemed to be no
> coding error. I provided other options for nlm() function and it gave me
> the same error message. I removed the gradient part and let nlm() do the
> numerical derivative, it ran but the algorithm was not converging.
>
> I want to know if nlm can handle multiple parameters problems, and if
yes,
> was there any error in my code? How do I properly provide the gradient
for
> my function?
>
> Thanks a lot,
>
> Yuan Ji, Ph.D.
>
> Assistant Professor
> Department of Bistatistics
> The University of Texas M.D. Anderson Cancer Center
> 1515 Holcombe Blvd. - Unit 447
> Houston, TX 77030-4009
> (713)794-4153
>
__
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