Great suggestions, Thomas. Consider the following:
> y <- as.matrix(data.frame(y1=rep(1:4, each=2), y2=0.01*rnorm(8)))
> x <- 1:8
> coef(lm(y~x))
y1 y2
(Intercept) 0.3571429 0.011181184
x 0.4761905 -0.001528493hth. spencer graves
Thomas W Blackwell wrote:
Martin -
My recollection is that if the left hand side in a model formula is a matrix (in your case an [n x 100] matrix) then either lm() or glm() will return a matrix of coefficients. These are the point estimates for a multivariate regression (meaning, multivariate response). I hunted just a bit: help("lm"), help("glm"), help.search("multivariate"), but I have not found where this behavior is documented in R. I'm sure it is documented somewhere.
- tom blackwell - u michigan medical school - ann arbor -
On Fri, 27 Jun 2003, Martin Wegmann wrote:
hello,
I only want to get the slope of a linear regression of ca. 100 variables against time.
I can do for each response (100 times) var1.lm <- lm(response~predictor)
but I thought that there might be an easier way of doing this. If I am including more variables it is doing a multiple regression and the output (slope) differs.
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