> -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] > Sent: Monday, August 04, 2008 5:15 PM > To: Zhang Yanwei - Princeton-MRAm > Subject: RE: [R] Multivariate Regression with Weights > > the systemfit package can do that and the documentation is quite nice > also.
The systemfit() function of the systemfit package can use different weights for the different *equations* but NOT different weights for different *observations* (if argument "method" is "SUR", "WLS", "3SLS" or "W2SLS"). I guess that you can divide the first equation by x_1 and the second equation by x_2 to obtain y_1^* = 1 + x_2^* + e_1^* y_2^* = 1 + x_1^* + e_2^* with y_1^* = y_1 / x_1 x_2^* = x_2 / x_1 e_1^* = e_1 / x_1 y_2^* = y_2 / x_2 x_1^* = x_1 / x_2 e_2^* = e_2 / x_2 In this case, you should have homoscedastic error terms. Hence, you can use systemfit to estimate the system of the modified equations. > On Mon, Aug 4, 2008 at 4:39 PM, Zhang Yanwei - Princeton-MRAm wrote: > > Hi all, > > I'd like to fit a multivariate regression with the variance of the > > error term porportional to the predictors, like the WLS in the > > univariate case. > > y_1~x_1+x_2 > > y_2~x_1+x_2 > > var(y_1)=x_1*sigma_1^2 I guess that you mean var(e_1). Right? > > var(y_2)=x_2*sigma_2^2 > > cov(y_1,y_2)=sqrt(x_1*x_2)*sigma_12^2 > > > > How can I specify this in R? Is there a corresponding function to the > > univariate specification lm(y~x,weights=x)?? Thanks. Unfortunately, systemfit has not (yet) a "weights" argument. (If you add this feature, I would be happy if you send me the code that I can include this feature in the official version.) Best wishes, Arne -- Arne Henningsen [EMAIL PROTECTED] http://www.arne-henningsen.name
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