Hi Lucas,

You may find some of these examples useful (towards the end):

http://elkhartgroup.com/rmodels.php

For example in your case you could be using b splines instead of an 11th
order polynomial, or use thin plate regression splines from the mgcv
package.  I will also humbly suggest that ggplot2 overlaying observed
values with predicted lines is a more elegant way to visualize the data and
the results.

Cheers,

Josh



On Sat, Apr 27, 2013 at 8:48 AM, Lucas Holland <hollandlucas@gmail.com>wrote:

> Hey all,
>
> I'm performing polynomial regression. I'm simulating x values using
> runif() and y values using a deterministic function of x and rnorm().
>
> When I perform polynomial regression like this:
>
> fit_poly <- lm(y ~ poly(x,11,raw = TRUE))
>
> I get some NA coefficients. I think this is due to the high correlation
> between say x and x^2 if x is distributed uniformly on the unit interval
> (as is the case in my example). However, I'm still able to plot a
> polynomial fit like this:
>
> points(x, predict(fit_poly), type="l", col="green", lwd=2)
>
> What I'm interested in finding out is, how R handles the NA values I get
> for some coefficients (and how that affects the polynomial I see plotted).
>
> Thanks!
>
> ______________________________________________
> R-help@r-project.org 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.
>



-- 
Joshua Wiley
Ph.D. Student, Health Psychology
University of California, Los Angeles
http://joshuawiley.com/
Senior Analyst - Elkhart Group Ltd.
http://elkhartgroup.com

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