Hi, Gabor: Thanks very much. Spencer
Gabor Grothendieck wrote:
On Sun, May 25, 2008 at 9:32 PM, Katharine Mullen <[EMAIL PROTECTED]> wrote:
Dear Spencer,
I just saw your post.
If the singular gradient happens during or after iteration one (that is,
not at the initial estimates), then calling summary on the nls output
would give standard error estimates on the parameters useful for
diagnostics. You could also call chol2inv(xx$m$Rmat()) where xx is the
object returned by nls to get an estimate of the inverse of the hessian;
you could use this estimate to proceed with the diagnostics you were
discussing.
Try this:
library(nls2)
DF1 <- data.frame(y=1:9, one=rep(1,9))
xx <- nls2(y~(a+2*b)*one, DF1, start = c(a=1, b=1), algorithm = "brute-force")
eigen(chol2inv(xx$m$Rmat()))
$values
[1] 5.070602e+31 0.000000e+00
$vectors
[,1] [,2]
[1,] -0.8944272 -0.4472136
[2,] 0.4472136 -0.8944272
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