search for: regrssion

Hi, I am trying to use piecewise linear regression to approximate a nonlinear function. Actually, I don't know how many linear functions I need, therefore, I want build an array of regression models to automate the approximation job. Could you please give me any clue? Attached is ongoing code: rawData = scan("c:/zyang/mass/data/A01/1.PRN", what=list(numeric(),numeric())); len =

...rm a liner regression: y=b0 + b1*x + b2*z There are two ways this can be done, the usual way, as a single regression, fit1<-lm(y~x+z) or by doing two regressions. In the first regression we could have y as the dependent variable and x as the independent variable fit2<-lm(y~x). The second regrssion would be a regression in which the residuals from the first regression would be the depdendent variable, and the independent variable would be z. fit2<-lm(fit2$residuals~z) I would think the two methods would give the same p value and the same beta coefficient for z. The don't. Can someone...

...To: Leeds, Mark (IED) Subject: R question I am using the "predict" function after I have done a simultaneous estimation of a system using "systemfit". fitsur is the output of systemfit and "system" is my system of equations, "SUR" is the Seemingly Unrelated Regrssions method of estimation. fitsur<-systemfit("SUR",system) I am supposed to be able to use the estimated coefficients for out-of-sample prediction using a different data set than the one originally used for estimation. That is my XSf data frame. Unfortunately whether I include that optio...