[R] strange `nls' behaviour

[Joerg van den Hoff]

I initially thought, this should better be posted to r-devel
but alas! no response. so  I  try  it  here.  sory  for  the
lengthy explanation but it seems unavoidable. to quickly see
the problem simply copy the litte example below and execute

f(n=5)

which  crashes. called with n !=  5 (and of course n>3 since
there are 3 parameters in the model...) everything is as  it
should be.

in detail:
I  stumbled over the follwing _very_ strange behaviour/error
when using `nls' which  I'm  tempted  (despite  the  implied
"dangers") to call a bug:

I've  written a driver for `nls' which allows specifying the
model and the data vectors using arbitrary  symbols.   these
are  internally  mapped  to  consistent names, which poses a
slight complication when using `deriv' to  provide  analytic
derivatives. the following fragment gives the idea:

#-----------------------------------------
f <- function(n = 4) {

   x <- seq(0, 5, length  = n)

   y <- 2 * exp(-1*x) + 2; 
   y <- rnorm(y,y, 0.01*y)

   model <- y ~ a * exp (-b*x) + c

   fitfunc <- deriv(model[[3]], c("a", "b", "c"), c("a", "b", "c", "x"))

   #"standard" call of nls:
   res1 <- nls(y ~ fitfunc(a, b, c, x), start = c(a=1, b=1, c=1))

   call.fitfunc <- 
   c(list(fitfunc), as.name("a"), as.name("b"), as.name("c"), as.name("x"))
   call.fitfunc <- as.call(call.fitfunc)
   frml <- as.formula("y ~ eval(call.fitfunc)")

   #"computed" call of nls:
   res2 <- nls(frml, start = c(a=1, b=1, c=1))

   list(res1 = res1, res2 = res2)
}
#-----------------------------------------

the  argument  `n'   defines  the number of (simulated) data
points x/y which are going to be fitted by some model ( here
y ~ a*exp(-b*x)+c )

the first call to `nls' is the standard way of calling `nls'
when knowing all the variable and parameter names.

the second call (yielding `res2') uses a constructed formula
in `frml' (which in this example is of course not necessary,
but  in  the general case 'a,b,c,x,y' are not a priori known
names).

now, here is the problem: the call

f(4)

runs fine/consistently, as does every call with n > 5.

BUT: for n = 5 (i.e. issuing f(5))

the second fit leads to the error message:

"Error in model.frame(formula, rownames, variables, varnames, extras, extranames,  : 
	invalid type (language) for variable 'call.fitfunc'"

I  cornered  this  to a spot in `nls' where a model frame is
constructed in variable `mf'.  the parsing/constructing here
seems  simply  to  be messed up for n = 5: `call.fitfunc' is
interpreted as variable.

I,  moreover, empirically noted that the problem occurs when
the total number of  parameters  plus  dependent/independent
variables  equals  the number of data points (in the present
example a,b,c,x,y).

so it is not the 'magic' number of 5 but rather the identity
of data vector length and number of parameters+variables  in
the model which leads to the problem.

this  is  with  2.5.0  (which  hopefully  is  not considered
ancient) and MacOSX 10.4.10.

any ideas?

thanks

joerg