On Thu, Feb 19, 2004 at 09:22:09AM -0800, Thomas Lumley wrote: >> So, what is the _right_ way for obtatining SE? Why two those formulas above >> differ? > > If you are maximising a likelihood then the covariance matrix of the > estimates is (asymptotically) the inverse of the negative of the Hessian. > > The standard errors are the square roots of the diagonal elements of the > covariance. > > So if you have the Hessian you need to invert it, if you have the > covariance matrix, you don't.

Yes, the covariance matrix is inverse of the Hessian, that's clear. But my queston is, why in the first example: > sqrt(diag(2*out$minimum/(length(y) - 2) * solve(out$hessian))) The 2 in the line above represents the number of parameters. A 95% confidence interval would be the parameter estimate +/- 1.96 SE. We can superimpose the least squares fit on a new plot: - we don _not_ use simply 'sqrt(diag(solve(out$hessian)))', how in the second example, but also include in some way "number of parameters" == 2? What does '2*out$minimum/(length(y) - 2)' multiplier mean? Thanks! -- WBR, Timur. ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html