Thanks for the replies.
I was just thinking that, for a two variable example, doing
X<-cbind(x1,x2,x1*x2)
lm(y~X)
would work. So maybe that's what I'll do. This also allows me to pick
and choose which interactions to include.
Cheers
Bill
On Sun, Dec 5, 2010 at 8:19 PM, William Simpson
wrote:
>
Hi Bill,
If you can put all (and only) your variables into a dataframe, (for example:
X <- data.frame(y, x1, x2, x3)
)
then another alternative to David's solution would be:
lm(y ~ .^3, data = X)
'.' will expand to every column except y, and then the ^3 will get you
up to 3-way interactions.
C
On Dec 5, 2010, at 3:19 PM, William Simpson wrote:
Suppose I have x variables x1, x2, x3 (however in general I don't know
how many x variables there are). I can do
X<-cbind(x1,x2,x3)
lm(y ~ X)
This fits the no-interaction model with b0, b1, b2, b3.
How can I get lm() to fit the model that incl
Suppose I have x variables x1, x2, x3 (however in general I don't know
how many x variables there are). I can do
X<-cbind(x1,x2,x3)
lm(y ~ X)
This fits the no-interaction model with b0, b1, b2, b3.
How can I get lm() to fit the model that includes interactions when I
pass X to lm()? For my example
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