[R] SSweibull() : problems with step factor and singular gradient
Prof J C Nash (U30A)
nashjc at uottawa.ca
Fri Oct 4 15:28:57 CEST 2013
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-request at r-project.org wrote:
> Message: 40
> Date: Thu, 3 Oct 2013 20:49:36 +0200
> From:aline.frank at wsl.ch
> To:r-help at r-project.org
> Subject: [R] SSweibull() : problems with step factor and singular
> gradient
> Message-ID:
> <OF669FA420.9EF643ED-ONC1257BF9.00676B04-C1257BF9.00676B07 at 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. Thatâ€™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 = â€œplinearâ€, : Â Â Â Â Â Â Â Â Â Â Â Â 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 didnâ€™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 = â€œplinearâ€, : Â Â Â Â Â Â Â Â Â Â Â Â Â
> singular gradient
>
> Is there a way to avoid the problem of a singular gradient?
>
> Iâ€™d be very glad about helpful comments. Thanks a lot.
> Aline
> [[alternative HTML version deleted]]
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