Hello Rainer,
I think that your problem is with trying to fit a logistic model to
data that don't support that model. Removing the first two points
from your data will work (but of course it may not represent reality).
The logistic function does not exhibit the kind of minimum that
your data suggest.
-Peter Ehlers
Rainer M Krug wrote:
Hi
I want to change a control parameter for an nls () as I am getting an error
message "step factor 0.000488281 reduced below 'minFactor' of 0.000976562".
Despite all tries, it seems that the control parameter of the nls, does not
seem to get handed down to the function itself, or the error message is
using a different one.
Below system info and an example highlighting the problem.
Thanks,
Rainer
version _
platform i486-pc-linux-gnu
arch i486
os linux-gnu
system i486, linux-gnu
status
major 2
minor 9.2
year 2009
month 08
day 24
svn rev 49384
language R
version.string R version 2.9.2 (2009-08-24)
sessionInfo()
R version 2.9.2 (2009-08-24)
i486-pc-linux-gnu
locale:
LC_CTYPE=en_ZA.UTF-8;LC_NUMERIC=C;LC_TIME=en_ZA.UTF-8;LC_COLLATE=en_ZA.UTF-8;LC_MONETARY=C;LC_MESSAGES=en_ZA.UTF-8;LC_PAPER=en_ZA.UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_ZA.UTF-8;LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] R.utils_1.2.0 R.oo_1.5.0 R.methodsS3_1.0.3 maptools_0.7-26
[5] sp_0.9-44 foreign_0.8-37
loaded via a namespace (and not attached):
[1] grid_2.9.2 lattice_0.17-25
#############################################
EXAMPLE:
dat <- data.frame(
x = 2006:2037,
y = c(143088, 140218, 137964,
138313, 140005, 141483, 142365,
144114, 145335, 146958, 148584,
149398, 151074, 152241, 153919,
155580, 157258, 158981, 160591,
162126, 163743, 165213, 166695,
168023, 169522, 170746, 172057,
173287, 173977, 175232, 176308,
177484)
)
nls( y ~ SSlogis(x, Asym, xmid, scal), data = dat, trace=TRUE)
(newMinFactor <- 1/(4*1024))
nls( y ~ SSlogis(x, Asym, xmid, scal), data = dat,
control=nls.control(minFactor=newMinFactor), trace=TRUE)
nls( y ~ SSlogis(x, Asym, xmid, scal), data = dat,
control=c(minFactor=newMinFactor), trace=TRUE)
(newMinFactor <- 4/1024)
nls( y ~ SSlogis(x, Asym, xmid, scal), data = dat,
control=nls.control(minFactor=newMinFactor), trace=TRUE)
nls( y ~ SSlogis(x, Asym, xmid, scal), data = dat,
control=c(minFactor=newMinFactor), trace=TRUE)
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