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)
}
Uwe Ligges
This seems like it should be easy... but I am stymied. Thanks for your
help
John
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