Thanks, Christos. Another relevant question:

If  I want to include the interaction term consisting of V2 and V3 (they are
numeric vectors), should I use:

y ~ 1 + V2:V3 or y ~ 1 + I(V2*V3)

or both are good?

-Luke

On 4/17/06, Christos Hatzis <[EMAIL PROTECTED]> wrote:
>
> If you want the quadratic term, you need to pass it as an argument in
> function I():
>
> y ~ 1 + V1 + V3 + I(V3^2)
>
> This is documented in ?formula
>
> -Christos
>
> -----Original Message-----
> From: [EMAIL PROTECTED]
> [mailto:[EMAIL PROTECTED] On Behalf Of Luke
> Sent: Monday, April 17, 2006 12:08 AM
> To: [email protected]
> Subject: [R] interaction terms in formula of lm or glm
>
> I would like to include pairwise interaction terms for lm(). For example,
> I
> want to include the quadratic term of variable "V3".
>
> > my.formula
> y ~ 1 + V1 + V3 + V3:V3
>
> > my.data
>    y V1 V2  V3
> 1 31  1 42 140
> 2 32  0 43 120
> 3 33  0 57 150
> 4 34  0 55 132
>
> > foo <- lm(my.formula, data = my.data)
>
> > foo$coefficients
> (Intercept)          V1          V3
> 29.47368421 -2.15789474  0.02631579
>
> Why do the foo coefficients not include V3:V3 ?
>
> I thought that the variable "V3:V3" has the values of squares of V3
> elements, that is,
> V3:V3
> 140*140
> 120*120
> ....
> Am I wrong?
>
> If I specify a fouth varaible "V4" with square values of V3, and the
> formula
> is y ~ 1 + V1 + V3 + V4, it seems that the foo will give me different in
> and
> out-sample predictions.
>
> -Luke
>
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>
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