[R] nls -- singular gradient problem

[kv]

Hi list,

i see this question is quiet a regular feature, 
but searching the past instances i could not 
find an answer to my specific problem.

Simply, trying to optimize this model gives 
a singular gradient problem -- tough optim()
seems to be able to solve it  would like to 
do these things in nls().

Treated<-Puromycin[Puromycin$state=="treated",]
weighted.MM<-function(resp,conc,K){
	pred<-K[1]+(1-exp(K[2]))*conc
        (resp-pred)
}
Pur.wt<-nls(~weighted.MM(rate,conc,K),data=Treated,start=list(K=c(0,0.1)))


Please advise,

Best,

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