David Winsemius wrote:
On May 30, 2009, at 9:36 AM, Uwe Ligges wrote:
John Poulsen wrote:
Hello,
I am using maximum likelihood to find the best parameters for a
model. This involves sometimes tweaking the starting values to find
a solution that converges.
I would like to automate the process so that when the optimizer runs
into an error it tweaks one of the parameters slightly, tries the fit
again, and then continues this until a solution if found.
I have been using try() to test if a fit will work (see below), but
how do I run a loop that says continue until class(m1) is not "try
error"?
m1<-mlefun(startvals, data=data)
if(class(m1)=="try-error"){startvals<-list(alpha=10,beta=1,loggamma=log(5),logk=log(exp(unlist(startvals[4]))+0.2))
mlefun(starvals, data)}
m1 <- mlefun(starvals, data=data)
while(class(m1) == "try-error"){
startvals <- list(alpha=10, beta=1, loggamma=log(5),
logk=log(exp(unlist(startvals[4]))+0.2))
m1 <- mlefun(starvals, data=data)
}
So this implicitly assumes that try() is wrapped around the code inside
mlefun?
Argh, thanks, I actually meant
try(mlefun(starvals, data=data))
each time.
Best,
Uwe
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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