[R] regression for several responses

[Spencer Graves]

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 -0.001528493

hth.  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.
> 
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://www.stat.math.ethz.ch/mailman/listinfo/r-help