Hello,
I'd like to fit a user defined function to a data set, but I have problems
to find my problem. The user defined function is a combination of two
rectangular functions, and the listing below gives an example for what I
want to do. The problem is, that I get the error message for fit1 and fit2
"Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at
initial parameter estimates". traceback() shows "stop("singular gradient
matrix at initial parameter estimates")" as the last step in both cases, so
I assume, that I make a fundamental mistake, because start values should be
OK. So my two questions are:
1) How do I fit only one parameter as tried with fit1 in the example?
2) How do I fit all parameters as tried with fit2 in the example?
Thanks,
Dirk
rm(list=ls(all=TRUE))
# rectangular function
g <- function (x, x0=0, P=1, w=0.5) {
.g <- function(.x, .x0=x0, .P=P, .w=w) {
.x <- .x - (.x %/% .P)*.P
if ( (.x >= x0 ) & (.x <= .x0 + .w) ) {return(1)} else
{return(0)}
}
return( unlist(lapply(x, .g)))
}
# combining two rectangular functions with an offset dx
f <- function (x, x0=0, P=2, w=0.5, dx=1) {
.f <- function(.x, .dx=dx, .P=P, .w=w)
g(x=(x0+.x), P=.P, w=.w) + g(x=(x0+.x+dx), P=.P, w=.w)
return( unlist(lapply(x, .f)))
}
# generate a data set
dataset <- curve(f(x, dx=0.65), 1, 5, n=1e3)
# add some noise
dataset$x <- dataset$x + rnorm(1e3, 0, 0.02)
dataset$y <- dataset$y + rnorm(1e3, 0, 0.05)
points(dataset, type='p', cex=0.67, col='blue')
# trying to fit only one parameter
fit1 <- nls(
y ~ f(x, x0=0, P=2, w=0.5, dx),
start = list(dx=0.6),
data = dataset
)
# trying to fit all parameters
fit2 <- nls(
y ~ f(x),
start = list(x0=0, P=2, w=0.5, dx=0.6),
data = dataset
)
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
