You need better starting values. Try setting b3=1 and solving using
lm using that result as your starting values in nls:
> cc <- coef(lm(log(q1)~d1,data=x))
> cc <- c(cc, 1)
> names(cc) <- c("b1", "b2", "b3")
> nls(log(q1)~b1+(b2/b3)*(d1^b3-1),data=x,start=cc,trace=TRUE)
225.0784 : 1.515604 4.329543 1.000000
0.1243237 : 5.807118 8.966687 -13.977948
0.08707836 : 5.807118 10.295483 -13.329564
0.08704864 : 5.807118 10.298223 -13.430181
0.08704864 : 5.807118 10.298319 -13.430735
Nonlinear regression model
model: log(q1) ~ b1 + (b2/b3) * (d1^b3 - 1)
data: x
b1 b2 b3
5.807 10.298 -13.431
residual sum-of-squares: 0.08705
Number of iterations to convergence: 4
Achieved convergence tolerance: 1.27e-09
On 9/21/07, Dietrich Trenkler <[EMAIL PROTECTED]> wrote:
> Hallo HelpeRs,
>
> I try to reconstruct some results from an econometric text book
> (Heij et al. (2004), pp. 218-20).
>
> For the data
>
> > x <- structure(list(q1 = c(345, 331, 320, 314, 299, 395, 415,
> 490, 547, 656, 628, 627), d1 = c(1, 1, 1, 1, 1, 1, 1.05,
> 1.05, 1.05, 1.15, 1.15, 1.15)), .Names = c("q1", "d1"), row.names =
> as.integer(c(NA,
> 12)), class = "data.frame")
>
> I tried to estimate a nonlinear regression model using nls(). I get
>
> >
> nls(log(q1)~b1+(b2/b3)*(d1^b3-1),data=x,start=list(b1=0,b2=1,b3=1),trace=TRUE)
> 246.132 : 0.0000 1.0000 1.0000 51.0907
> Error in qr.solve(QR.B, cc) : singular matrix 'a' in solve
>
> However, using:
>
> > jjf <- function(x){z <- log(q1)-x[1]+(x[2]/x[3])*(d1^x[3]-1);sum(z*z)}
> > optim(c(0,1,1),jjf)
>
> rendered (some of) the results desired in a jiffy. What am I doing
> wrong?
>
> Dietrich
>
> --
> Dietrich Trenkler c/o Universitaet Osnabrueck
> Rolandstr. 8; D-49069 Osnabrueck, Germany
> email: [EMAIL PROTECTED]
>
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>
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