I should have said:
See also
?model.matrix
-- Bert
On Mon, Oct 31, 2011 at 9:00 AM, flokke wrote:
> I know the lm() function, but I'd like to make my 'own' regression analysis
> by using matrix algebra.
> Thats why I wrote the function,
> but I dont know what values to pick to make it suitable
Well, this just shows how much you don't understand. There is A LOT under
the hood here, extending back to work in the 70's (Wilkinson and Rogers
(1973) "Symbolic description of factorial models for analysis of variance"
) and probably before. See also McCullagh and Nelder's "Generalized Linear
Mo
I know the lm() function, but I'd like to make my 'own' regression analysis
by using matrix algebra.
Thats why I wrote the function,
but I dont know what values to pick to make it suitable for every dataset.
If I pick a statistical model like lm(y~x) as input, the function would not
know what o
Have you seen the lm() function? I think it does what you are talking
about, but the vagueries of your statement make it hard for me to give
a concrete answer. It does worry me to think of you having one
function to do all your regressions analysis ever for any problem
whatsoever
Michael
On M
Dear all,
I have a small question.
I would like to write a function for a regression analysis that an be
applied to every dataset.
Now my problem is that I do not know what I have to implement then as input
for the function
mytest <- function (x,y) {
beta <- sol
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