similar to: Constrained regression

Dear R-users, I am working on a problem that I am currently not able to solve efficiently. It is about multiplying one column of a matrix with only a certain number of rows of another matrix. Let me illustrate my problem with an example: n.obs = 800 n.rowsperobs = 300 n.param = 23 Designmat = matrix(rnorm(n.obs*n.rowsperobs*n.param),ncol=n.param) Betamat =

Hello everyone, I have 3 variables Y, X1 and X2. Each variables lies between 0 and 1. I want to do a constrained regression such that a>0 and (1-a) >0 for the model: Y = a*X1 + (1-a)*X2 I tried the help on the constrained regression in R but I concede that it was not helpful. Any help is greatly appreciated -- Thanks, Jim. [[alternative HTML version deleted]]

Good evening, I have been asked to investigate the pros and cons of using SAS vs. R in a web application. Either SAS or R would be the engine used to make some very simple calculations and to produce graphs, preferably in png format. The advantages of R are pretty obvious as there would be no licensing issues. The only drawback I can see is that when calling it in batch (using R CMD BATCH), a

Dear Rusers, I have met a difficult problem on explaining the differences of principal component analysis(PCA) between R,S-PLUS and SAS/STATA/SPSS, which wasn't met before. Althought they have got the same eigenvalues, their coeffiecients were different. First, I list my results from R,S-PLUS and SAS/STATA/SPSS, and then show the original dataset, hoping sb. to try and explain it.

> From: "Carlos Alzola" <calzola at cox.net> > To: <r-help at r-project.org> > Date: Tue, 8 Sep 2009 23:19:11 -0400 > Subject: [R] SAS vs. R in web application > Good evening, > > I have been asked to investigate the pros and cons of using SAS vs. R in a web application. Either SAS or R would be the engine used to make some very simple calculations and to

Dear all, I am interested to calculate confidence interval for fitted values in general for non-linear regressions. Lets say we have y=f(x1,x2,..xN) where f() is a non-linear regression. I would like to calculate a confidence interval for new prediction f(a1,..,aN). I am aware of techniques for calculating confidence intervals for coeffiecients in specific non-linear regressions and with them