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
[[alternative HTML version deleted]]
______________________________________________
[email protected] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
