On Mon, 4 Jun 2001 14:54:56 -0400, "Jiwu Rao" <[EMAIL PROTECTED]> wrote: > Hi > > I performed a regression analysis on a model nonlinear in parameters. The > function is: > q = ( k* P^n ) + (k2 * P2^n2) > where P and P2 are independent variables, k, n, k2, n2 are parameters. > The estimates and their variances can be obtained, as well as correlation > between any two parameters. > > The question is: how do I estimate the standard error in the first term of > the equation? That is, what is the error in estimating w = k* P^n? 1) What is this question supposed to mean? How do *you* want to interpret the error in adding two variables to an equation, if it were an ordinary multiple regression? In terms of 'error', does it answer your question, to drop out the whole term, and compare the fuller Fit (4 or 5 variables) with a model having 2 variables less? That probably gives you a statistical test if you are fitting by Least squares, or by Maximum likelihood. 2) If your correlations between parameters are nearly 1.0 (as you go on to say), that suggests you don't have the model in an elegant form. Reparameterize. The form of q= (k * P^n) looks like a power-transformation. If you are trying to solve for the Box-Cox transformation, or to do something similar, I think you want a component that multiplies or divides by n, as part of the constant before P. That should get rid of some of the correlation. 3) Are you really committed to that equation? Who around you knows enough that they should commit you to that equation? - Ask *them* what the proper parameterization should be, since the version yielding correlations near to 1.0 is (at best) a mistake from not paying enough attention. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html ================================================================= Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =================================================================
