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 ggpl
Inline.
-- Bert
On Sat, Apr 27, 2013 at 8:48 AM, Lucas Holland 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
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 h
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