 SSweibull() :  problems with step factor and singular gradient

Hello

I  am working with growth data of ~4000 tree seedlings and trying to fit  non-linear Weibull growth curves through the data of each plant. Since  they differ a lot in their shape, initial parameters cannot be set for  all plants. That’s why I use the self-starting function SSweibull(). 
However, I often got two error messages: 

1)
# Example
days <- c(163,168,170,175,177,182,185,189,196,203,211,217,224)
height <- c(153,161,171,173,176,173,185,192,195,187,195,203,201)
dat <- as.data.frame(cbind(days,height))
fit <- nls(y ~ SSweibull(x, Asym, Drop, lrc, pwr), data = dat, trace=T, control=nls.control(minFactor=1/100000))

Error in nls(y ~cbind(1, -exp(-exp(lrc)* x^pwr)), data = xy, algorithm = “plinear”, :                          
step factor 0.000488281 reduced below `minFactor` of 0.000976562

I  tried to avoid this error by reducing the step factor below the  standard minFactor of 1/1024 using the nls.control function (shown in  the example above). However, this didn’t work, as shown in the example  (minFactor still the standard).
Thus, does nls.control() not work for self-starting functions like SSweibull()? Or is there another explanation?

2)
In other cases, a second error message showed up:

Error in nls(y ~cbind(1, -exp(-exp(lrc)* x^pwr)), data = xy, algorithm = “plinear”, :                          
singular gradient

Is there a way to avoid the problem of a singular gradient?

I’d be very glad about helpful comments. Thanks a lot.
Aline
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