How about I make a design matrix as follows: 1 d1,1 0 0 0 0 0 0 0 0 0 1 d1,2 0 0 0 0 0 0 0 0 0 1 d1,3 0 0 0 ... ...
0 0 0 0 0 0 ... ... 1 d1,12 The above matrix will work for the 1st row of Y data; d1, 1 means the 1st column of the 1st row of data; d1, 12 means the 12th column of the 1st row of data. But now since I have N samples(rows) of Y data; how do I conceptually do a 3D design matrix? On 11/15/06, John Fox <[EMAIL PROTECTED]> wrote: > > Dear Michael, > > > -----Original Message----- > > From: Michael [mailto:[EMAIL PROTECTED] > > Sent: Wednesday, November 15, 2006 2:40 PM > > To: John Fox > > Cc: [email protected] > > Subject: Re: [R] how to create this design matrix? > > > > There are 12 response variables, columns 1 to 12 are response > > variables, i.e., these are y's, they all regress to the 13th > > column, which is the predictor, i.e. the X. > > > > Right. > > > Let's take column 1, call this Y1, and there are n rows(n > > samples) of it, > > > > I need Y1= b0_1 + b1_1* X + epsilon, where X is the 13th column > > > > Similarly, for column 1 to column 12, we do the above, > > > > Y12= b0_12 + b1_12 * X + epsilon, where Y12 is the 12th column, > > > > they all have different b0's and b1's. > > Right. > > > Totally there are 24 b0's and b1's. > > > > Yes. > > > I want a group regression, not separated regression... > > > > I'm not sure what you mean by a "group regression" rather than "separated > regressions." The multivarite linear regression that I suggested will give > you 12 slopes and 12 intercepts. They are exactly what you'd get from 12 > individual least-squares regression of each Y on X, but the multivariate > regression can also give you, e.g., the covariances among all of the > coefficients (if you want them). > > John > > > Thanks > > > > > > > > > > On 11/15/06, John Fox < [EMAIL PROTECTED]> wrote: > > > > Dear Michael, > > > > This looks like a multivariate simple regression -- > > that is, 12 response > > variables, one predictor. If the data are in the matrix > > X, then lm(X[,1:12] > > ~ X[,13]) should do the trick. > > > > I hope this helps, > > John > > > > -------------------------------- > > John Fox > > Department of Sociology > > McMaster University > > Hamilton, Ontario > > Canada L8S 4M4 > > 905-525-9140x23604 > > http://socserv.mcmaster.ca/jfox > > -------------------------------- > > > > > -----Original Message----- > > > From: [EMAIL PROTECTED] > > > [mailto: [EMAIL PROTECTED] > > <mailto:[EMAIL PROTECTED]> ] On Behalf Of Michael > > > Sent: Wednesday, November 15, 2006 12:23 AM > > > To: [email protected] > > > Subject: [R] how to create this design matrix? > > > > > > Hi all, > > > > > > I have a multiple-linear regression problem. > > > > > > There are 13 columns of data, the whole data matrix is: n x > > > 13, where n is the number of samples. > > > > > > Now I want to regress EACH of the first 12 columns onto the > > > 13th column, with 2-parameter linear model y_i = b0 + b1 * > > > x_i, where i goes from 1 to n, and b0 is the intercept. > > > > > > How do I create a design matrix to do the 12-column > > > regression collectively all at once using multiple > > linear regressions? > > > > > > Thanks a lot > > > > > > [[alternative HTML version deleted]] > > > > > > ______________________________________________ > > > [email protected] mailing list > > > https://stat.ethz.ch/mailman/listinfo/r-help > > > PLEASE do read the posting guide > > > http://www.R-project.org/posting-guide.html > > < http://www.R-project.org/posting-guide.html> > > > and provide commented, minimal, self-contained, > > reproducible code. > > > > > > > > > > > > [[alternative HTML version deleted]] ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
