Hi, I am trying to use the nls() function to closely approximate a vector of
values, colC and I'm running into trouble. I am not sure how if I am asking
the program to do what I think its doing, because the same minimization in
Excel's Solver does not run into problems. If anyone can tell me what is
going wrong, and why I'm getting a singular convergence(7) error, please
tell me. I have also included, after the R code, the optimal answers
according to Excel. The reason I'm using R is because of the (relative)
ease of getting the standard errors on the coefficients, which I don't
believe Excel Solver does. I'm new to nonlinear optimization, so please
forgive any obvious things I've overlooked. Thank you very much for taking
a look!
Here is the R code, and error message:
ColumnA.data<-read.csv(file.choose()) #select ColA.csv
ColumnB.data<-read.csv(file.choose()) #select ColB.csv
ColumnC.data<-read.csv(file.choose()) #select ColC.csv
colA<-ColumnA.data[0:3600,]
colB<-ColumnB.data[0:3600,]
colC<-ColumnC.data[0:3600,]
i<-1:3600
cor.model<-nls(colC ~ exp( - beta2 * abs( colB[i] - colA[i] ) / 12 ) - ( 1 -
exp( - beta2 * abs( colB[i] - colA[i] ) / 12 ) ) * exp( - beta1 / ( min(
colB[i], colA[i] ) / 12 ) ) , start=list(beta1 = 0.37, beta2 = 0.06), trace
= T, control=nls.control(minFactor=1/4096) , alg="port",
lower=list(beta1=0.35,beta2=0.05))
0: 7.7890438: 0.370000 0.0600000
1: 7.4408010: 3.96197 0.0597763
2: 4.5308657: 3.96197 0.0500000
3: 4.5308657: 3.96197 0.0500000
Error in nls(colC ~ exp(-beta2 * abs(colB[i] - colA[i])/12) - (1 -
exp(-beta2 * :
Convergence failure: singular convergence (7)
The optimal answers, according to Excel are:
beta2= 0.0670690912936098
beta1=0.398341074464919
when I try to check the residuals myself with the following R code, I get
the same answer as when I calculate them in Excel:
colE<-function(i) {(colC[i]-exp( - beta2 * abs( colB[i] - colA[i] ) / 12 ) -
( 1 - exp( - beta2 * abs( colB[i] - colA[i] ) / 12 ) ) * exp( - beta1 / (
min( colB[i], colA[i] ) / 12 ) ) )^2}
Any ideas? Thank you for your help!
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