They are not 'stripped': nothing says that because you named the starting
value that other values will be named.
You are misusing nls(): the documented way would be to have
data.x <- 1:50
data.y <- pi*data.x + rnorm(50,sd=20)
fitting.fn <-function(x, a, b) a + b*x
nls(data.y ~ fitting.fn(data.x, a, b),data=data.frame(data.x,data.y),
start=list(a=0, b=0),trace=TRUE,control=nls.control(tol=1e-8))
On Tue, 22 Jul 2008, Mark Payne wrote:
Dear R-dev,
I have been having some problems with regards to names in the parameter
vector being stripped when passed to the objective function when using
nls(). I was relieved to find that it wasn't me, and that this behaviour has
previously been reported in optim() also. See eg
https://stat.ethz.ch/pipermail/r-devel/2006-March/036710.html
Note that you are quoting a comment about a different function in a long
obsolete version of R, with a very different interface. That's called
'clutching at straws'.
The solution at that time was to make a change so that vector passed into
the objective function was named (eg see following two discussions).
The problem that I am having is virtually identical, but is occuring with
nls() instead of optim(). To illustrate the problem, I have put together the
following example code (see bottom). Basically, it is doing a linear least
squares by hand, but it also displays the names associated on the parameters
vector - if you run it, you will see that the names are there for the first
few function evaluations, but after the first "step", the names are dropped.
I was wondering if I could please ask for a similar fix as that applied to
optim() to be carried across to nls() also?
Many thanks,
Mark
#Setup environment
rm(list=ls())
counter <- 1
#Objective function for nls
fitting.fn <-function(x,params) {
#The model - so that it works
y <- params[1] + x*params[2]
#Referring to the parameters this way stops working after the first "step"
# y <- params["a"] + x*params["b"]
#Display information about function eval and parameter names
cat(paste("Evaluation # :",counter,"\t Names :"))
print(names(params))
counter <<- counter +1
return(y)
}
#Synthetic data
data.x <- 1:50
data.y <- pi*data.x + rnorm(50,sd=20)
#Fit objective function
ips <- c(a=0,b=0)
nls("data.y~fitting.fn(data.x,params)",data=data.frame(data.x,data.y),
start=list(params=ips),trace=TRUE,control=nls.control(tol=1e-8))
[[alternative HTML version deleted]]
______________________________________________
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
______________________________________________
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
