I think you have chosen a model that is ill-suited to the data.
My initial thoughts were simply that the issue was the usual nls()
"singular gradient" (actually jacobian if you want to be understood in
the optimization community) woes, but in this case the jacobian really
is bad.
My quick and dirty tries give some insight, but do not provide a
satisfactory answer. Note that the last two columns of the nlxb summary
are the gradient and the Jacobian singular values, so one can see how
bad things are.
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 <- try(nls(y ~ SSweibull(x, Asym, Drop, lrc, pwr), data = dat,
trace=T, control=nls.control(minFactor=1/100000)))
## failed
fdata<-data.frame(x=days, y=height)
require(nlmrt)
strt2<-c(Asym=250, Drop=1, lrc=1, pwr=1)
fit2<-nlxb(y ~ Asym - (Drop * ( exp(-(exp(lrc)*(x^pwr))))), data=fdata,
start=strt2, trace=TRUE)
strt3<-c(Asym=250, Drop=.5, lrc=.1, pwr=2)
fit3<-nlxb(y ~ Asym - (Drop * ( exp(-(exp(lrc)*(x^pwr))))), data=fdata,
start=strt3, trace=TRUE)
strt4<-c(Asym=200, Drop=.5, lrc=.1, pwr=2)
fit4<-nlxb(y ~ Asym - (Drop * ( exp(-(exp(lrc)*(x^pwr))))), data=fdata,
start=strt4, trace=TRUE, masked=c("Asym"))
d50<-days-160
fd2<-data.frame(x=d50, y=height)
fit5<-nlxb(y ~ Asym - (Drop * ( exp(-(exp(lrc)*(x^pwr))))), data=fd2,
start=strt3, trace=TRUE)
fit5
John Nash
On 13-10-04 02:19 AM, r-help-requ...@r-project.org wrote:
Message: 40
Date: Thu, 3 Oct 2013 20:49:36 +0200
From:aline.fr...@wsl.ch
To:r-help@r-project.org
Subject: [R] SSweibull() : problems with step factor and singular
gradient
Message-ID:
<of669fa420.9ef643ed-onc1257bf9.00676b04-c1257bf9.00676...@wsl.ch>
Content-Type: text/plain
SSweibull() : Â 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 =
“plinearâ€�, :             Â
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 =
“plinearâ€�, :             Â
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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