nls troubles: Missing value or an infinity produced when evaluating the model
minpack.lm to the rescue:
library(minpack.lm)
curve.nlslrc = nlsLM(photolrc ~ Am*(1-((1-(Rd/Am))^(1-(PARlrc/LCP)))),
start=list(Am=(max(photolrc)-min(photolrc)),
Rd=-min(photolrc),
LCP= (max(photolrc)-1)),
data = curvelrc)
coef(curve.nlslrc)
# Am Rd LCP
#8.011311 1.087484 -20.752957
plot(photolrc ~ PARlrc, data = curvelrc)
lines(0:1300,
predict(curve.nlslrc,
newdata = data.frame(PARlrc = 0:1300)))
If you pass start = list(Am = 8, Rd = 1, LCP = -20)
to nls
you also get a successful fit.
I don't know if the parameter values are sensible estimates considering the science behind this. Can LCP be negative?
The problems are:
- we need better initial values
- according to a comment by the poster we need to constrain LCP to be positive.
To do that we can use nls2 to get better starting values followed by using nls with the port algorithm to enforce a lower bound for LCP. Note that LCP hit the constraint boundary.
library(nls2)
# get starting value fit
st <- data.frame(Am = c(1, 10), Rd = c(-10, 10), LCP = c(0.5, 10))
fo <- photolrc ~ Am*(1-((1-(Rd/Am))^(1-(PARlrc/LCP))))
fm2 <- nls2(fo, start = st, alg = "brute")
# nls fit
fm <- nls(fo, start = coef(fm2), lower = c(-Inf, -Inf, 0.1), algorithm = "port")
giving:
> fm
Nonlinear regression model
model: photolrc ~ Am * (1 - ((1 - (Rd/Am))^(1 - (PARlrc/LCP))))
data: parent.frame()
Am Rd LCP
7.919374 -0.007101 0.100000
residual sum-of-squares: 0.1858
Algorithm "port", convergence message: relative convergence (4)