I have always used lsfit() for this, but have been told that lm() is
preferred, and I note the help for lm() states
If 'response' is a matrix a linear model is fitted separately by
least-squares to each column of the matrix.
Reid Huntsinger
-----Original Message-----
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Heng Sun
Sent: Friday, October 14, 2005 11:56 AM
To: [email protected]
Subject: [R] run many linear regressions against the same independent
variables in batch
R function
lm(response ~ term)
allows me to run a linear regression on a single response vector. For
example, I have recent one year historical prices for a stock and S&P
index. I can run regression of the stock prices (as response vector)
against the S&P index prices (as term vector).
Now assume I have 1000 stocks to run the above regressions (against the
same S&P index prices). The only way I know is that I write a loop. Within
each loop I do the regression for one stock price.
Is there a batch method to run the 1000 regressions in one shot? Note that
this functionality is available in SAS (the SAS procedure "reg").
Actually, some times we run such regressions for about 300K securities.
Performing regressions in loop takes a long time. On the contrary, running
on SAS is much faster.
Thank you in advance.
Heng Sun
212-855-5754
Director
Quantitative Risk
Depository Trust and Clearing Corporation
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