Hi,
I'm trying to make a regression of the form :
formula - y ~ Asym_inf + Asym_sup * ( (1 / (1 + (n1 * (exp( (tmid1-x)
/ scal1) )^(1/n1) ) ) ) - (1 / (1 + (n2 * (exp( (tmid2-x) / scal2)
)^(1/n2) ) ) ) )
which is a sum of the generalized logistic model proposed by richards.
with data such
You could try the brute force of nls2 package; however, note that you
have 8 parameters and only 16 points so you might look for a more
parsimonious model. Plotting it it seems somewhat gaussian in shape
so:
mod - nls(y ~ a * dnorm(x, b, c), start = c(a = mean(y)/dnorm(0, 0,
sd(x)), b = mean(x),
My question is how could I estimate those initial values so that the nls
fitting works.
You can't. Your parameters are almost certainly nonidentifiable (which is
what Gabor told you more gracefully).
Just because you believe in a complex (often mechanistic) nonlinear model
and have some data
Actually, the data that I used are measurements of plant growth during
an entire year.It is usual to model the growth with logistic models.
I have already tried the simple logistic model (which works). But the
problem is that with this model the inflexion point occurs half-way up
or down the
4 matches
Mail list logo
