Hi,
I am trying to fit a 4p logistic to this data, using nls function. The function didn't freely converge; however, it converged if I put a lower and an upper bound (in algorithm port). Also, the b1.A parameter always takes value of the upper bound, which is very strange. Has anyone experienced about non-convergent of nls and how to deal with this kind of problem?
Thank you very much.
########################################################################3
y x
1 0.8924619 -0.31875876
2 1.1814749 -0.21467016
3 1.6148266 0.06069784
4 2.2091363 0.54032947
5 2.7019079 1.04921802
6 3.0679585 1.60745502
9 0.9436973 -0.31875876
10 1.2201133 -0.21467016
11 1.6470043 0.06069784
12 2.2090048 0.54032947
13 2.6864857 1.04921802
14 3.0673523 1.60745502
new.cont=nls.control(maxiter = 10000, tol = 1e-05, minFactor = 1e-08,
printEval = FALSE, warnOnly = FALSE)
b0.A=.9*min(DAT$y)
b1.A=1.1*max(DAT$y)-b0.A
b2.A=-1*mean(DAT$x)
b3.A=1
b0.A
b1.A
b2.A
b3.A
nls.mdl.A=nls(y~b0 + b1/(1+exp(-b2-b3*x)),data=DAT,start = list(b0=b0.A, b1=b1.A, b2=b2.A, b3=b3.A), lower=-10, upper=10, algorithm="port",trace=T,control=new.cont)
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