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 logis
> 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
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),
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
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