R help - Jun 2009 - Confidence Bands in Polynomial Regression

Hello R users,

Given a linear (in the parameters) regression model where one predictor x
interacts with time and time*time (ie, a quadratic effect of time t):
y = b0 + b1(x) + b2(t) + b3(t^2) + b4(x*t) + b5(x*t^2) + e,

I would like to construct 95% confidence bands (optimally, shaded) around
this function:

*dy* = b1 + b4(t) + b5(t^2)
*dx*

That is, the partial effect of x on y changing over time t

Is this possible with predict() or perhaps another function?


Thank you very much
Ben

-- 
PhD Candidate
Center for Cognitive Science
University of Toronto

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On Mon, Jun 15, 2009 at 6:57 PM, Ben Amsel<benamsel at gmail.com>
wrote:
> ?Hello R users,
>
> Given a linear (in the parameters) regression model where one predictor x
> interacts with time and time*time (ie, a quadratic effect of time t):
> y = b0 + b1(x) + b2(t) + b3(t^2) + b4(x*t) + b5(x*t^2) + e,
>
> I would like to construct 95% confidence bands (optimally, shaded) around
> this function:
>
> *dy* = b1 + b4(t) + b5(t^2)
> *dx*
>
> That is, the partial effect of x on y changing over time t
>
> Is this possible with predict() or perhaps another function?
>
>
> Thank you very much
> Ben
Hi Ben,

Check out the 'Design' package, it has all kinds of convenience
functions for plotting these type of things.

install.packages('Design', dep=TRUE)
?ols

Cheers,
Dylan