I am no expert on nls() but since I haven't seen any replies to your
post, I'll chip in:
(1) I am mystified as to why nls() is giving that error about a
"singular" (???)
gradient.
(2) That being said, I think your parameterisation of the objective function
is a bit flaky. I would use
a*log(1+exp(b*x - tau)) # Using "x" instead of the Windoze-ese
"myday".
This is equivalent to your (c,r,tt) parameterisation with
c = a*b
r = b
tt = tau/b
(3) However I still get that error message from nls() with this new
parameterisation.
(4) I tried the optimize() function and *that* seems to work without
complaint.
With my parameterisation the Nelder-Mead (default) and the BFGS methods
give
very similar results with a minumum sum of squares equal to 4.909448 and
4.904986
respectively.
With your parameterisation the Nelder-Mead method gives a minimum sum of
squares
equal to 4.972705 --- not as good, and the BFGS method (which is more
like what nls()
uses) gives 219.79 --- right out to lunch.
The plots of the fitted curves for the two fits with my parameterisation
are visually
indistinguishable and are visually indistiguishable from a straight line
fit. (Which raises
the question --- why are you using such a complicated model?
The Nelder-Mead curve from your parameterisation is "close" to those from my
parameterisation, but is definitely different. The BFGS curve from your
parameterisation
is off the plot region.
Summary:
* I have no idea why nls() is throwing an error.
* Your parameterisation is bad.
* A better parameterisation can be readily fitted to your data
using optimize().
* The model is probably too complicated and inappropriate for these
data.
HTH
cheers,
Rolf Turner
On 01/02/12 03:11, ram basnet wrote:
Dear R users,
I am struggling to fit expo-linear equation to my data using "nls" function. I
am always getting error message as i highlighted below in yellow color:
Theexpo-linear equation which i am interested to fit my data:
response_variable = (c/r)*log(1+exp(r*(Day-tt))), where "Day" is time-variable
my response variable
rl<- c(2,1.5,1.8,2,2,2.5,2.6,1.5,2.4,1.7,2.3,2.4,2.2,2.6,
2.8,2,2.5,1.8,2.4,2.4,2.3,2.6,3,2,2.6,1.8,2.5,2.5,
2.3,2.7,3,2.2,2.6,1.8,2.5,2.5,2.3,2.7,3,2.2)
myday<- rep(c(3,5,7,9,10), each = 8) # creating my predictor time-variable
mydata<- data.frame(rl,myday) # data object
# fitting model equation in "nls" function, when i assigned initial value for
tt = 0.6,
CASE-I:
mytest<- nls(rl ~ (c/r)*log(1+exp(r*(myday-tt))), data = mydata,
+ na.action = na.omit,
+ start = list(c = 2.0, r = 0.05, tt = 0.6),algorithm = "plinear")
Error in numericDeriv(form[[3L]], names(ind), env) :
Missing value or an infinity produced when evaluating the model
CASE - II:
When i assigned initial value for tt = 1:
mytest<- nls(rl ~ (c/r)*log(1+exp(r*(myday-tt))), data = mydata,
+ na.action = na.omit,
+ start = list(c = 2.0, r = 0.5, tt = 1),algorithm = "plinear")
Error in nls(rl ~ (c/r) * log(1 + exp(r * (myday - tt))), data = mydata, :
singular gradient
I am getting the yellow-color highlighted error message (see above). Truely speaking, i have not so much experienced with fitting specific model equation in R-package.
I have following queries:
1. Does any one can explain me what is going wrong here ?
2. Importantly, how can i write above equation into "nls" functions ?
I will be very thankful to you, if any one can help me.
I am looking for your cooperations.
Thanks
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