Looping until a solution is found

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)
}