I know "optim" should do a minimisation, therefor I used as the
optimisation function
opt.power <- function(val, x, y) {
a <- val[1];
b <- val[2];
sum(y - b/(2*pi*a^2*gamma(2/b))*exp(-(x/a)^b));
}
I call: (with xm and ym the data from the table)
a1 <- c(0.2, 100)
opt <- optim(a1, opt.power, method="BFGS", x=xm, y=ym)
but no optimisation of the parameter in a1 takes place.
Any ideas?
It looks to me like your optimising the _average_ of the differences
between y and the function, so as long as positive and negative
differences balance out you get a cost value of 0 (and you can make it
even smaller if the fitted function is much larger than the actual y
values, so all differences are negative).
You probably wanted to minimise the squared errors:
sum((y - b/(2*pi*a^2*gamma(2/b))*exp(-(x/a)^b)))^2)
Best regards,
Stefan Evert
[ stefan.ev...@uos.de | http://purl.org/stefan.evert ]
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