You can record all arguments and return values of the
calls that optim(par,fn) makes to fn with a function
like the following. It takes your function and makes
a new function that returns the same thing but also
records information it its environment. Thus, after
optim is done you can see its path to the optimum.
trackFn <- function (fn)
{
X <- NULL
VALUE <- NULL
force(fn)
function(x) {
X <<- rbind(X, x)
val <- fn(x)
VALUE <<- c(VALUE, val)
val
}
}
E.g.,
> fn <- function(x)sum( (sin(x)-c(-1,0,1)) ^ 2 )
> optim(c(0,0,0), tfn <- trackFn(fn))
$par
[1] -1.583466e+00 6.726235e-05 1.558809e+00
$value
[1] 1.612853e-08
$counts
function gradient
146 NA
$convergence
[1] 0
$message
NULL
> with(environment(tfn), length(VALUE)) # agrees w/ counts above
[1] 146
> with(environment(tfn), plot(VALUE, log="y"))
> with(environment(tfn), matplot(X, VALUE, log="y", type="l"))
You could also record warning messages by including a call
to withCallingHandlers() that stashed the warning in a list.
Recall trackFn each time you call optim().
Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com
> -----Original Message-----
> From: r-help-boun...@r-project.org
> [mailto:r-help-boun...@r-project.org] On Behalf Of Zhang,Yanwei
> Sent: Thursday, September 09, 2010 11:54 AM
> To: r-help@r-project.org
> Subject: [R] Help on simple problem with optim
>
> Dear all,
>
> I ran into problems with the function "optim" when I tried to
> do an mle estimation of a simple lognormal regression. Some
> warning message poped up saying NANs have been produced in
> the optimization process. But I could not figure out which
> part of my code has caused this. I wonder if anybody would
> help. The code is in the following and the data is in the attachment.
>
>
> da <- read.table("da.txt",header=TRUE)
>
> # fit with linear regression using log transformation of the
> response variable
> fit <- lm(log(yp) ~ as.factor(ay)+as.factor(lag),data=da)
>
> # define the log likelihood to be maximized over
> llk.mar <- function(parm,y,x){
> # parm is the vector of parameters
> # the last element is sigma
> # y is the response
> # x is the design matrix
> l <- length(parm)
> beta <- parm[-l]
> sigma <- parm[l]
> x <- as.matrix(x)
> mu <- x %*% beta
> llk <- sum(dnorm(y, mu, sigma,log=TRUE))
> return(llk)
> }
>
> # initial values
> parm <- c(as.vector(coef(fit)),summary(fit)$sigma)
> y <- log(da$yp)
> x <- model.matrix(fit)
>
> op <- optim(parm, llk.mar,
> y=y,x=x,control=list(fnscale=-1,maxit=100000))
>
>
> After running the above code, I got the warning message:
> Warning messages:
> 1: In dnorm(x, mean, sd, log) : NaNs produced
> 2: In dnorm(x, mean, sd, log) : NaNs produced
>
>
> I would really appreciate if anybody would help to point out
> the problem with this code or tell me how to trace it down
> (using "trace"?)?
> Many thanks in advance.
>
>
>
>
>
>
>
> Wayne (Yanwei) Zhang
> Statistical Research
> CNA
>
>
>
>
>
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