[R] fitting curve to data

[Gabor Grothendieck]

As x goes from 200 to 400, y goes from ,004 to .016 so y is
quadrupling while x doubles -- quadratic growth.    Fitting
to a quadratic and plotting shows this to be the case.  Note
that for y to be quadratic in x it must be linear in the coefficients
of x so we can just use lm and don't need nls:

plot(y ~ x)
y.lm <- lm(y ~ poly(x, 2))
lines(x, fitted(y.lm))
y.lm

On Sun, Jan 11, 2009 at 9:19 PM, Nathan S. Watson-Haigh
<nathan.watson-haigh at csiro.au> wrote:
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> I have the following data:
>
>> y
>  [1] 0.000 0.004 0.008 0.016 0.024 0.032 0.044 0.064 0.072 0.088 0.108 0.140
> [13] 0.156 0.180 0.208 0.236 0.264 0.296 0.320 0.360 0.408 0.444 0.472 0.524
> [25] 0.576
>> x
>  [1]  100  200  300  400  500  600  700  800  900 1000 1100 1200 1300 1400 1500
> [16] 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500
>
> I'd like to plot the points and calculate a curved line of best fit. I know I need to use nls(), but
> I'm unsure how to begin....any pointers?
>
> Cheers,
> Nathan
>
>
> - --
> - --------------------------------------------------------
> Dr. Nathan S. Watson-Haigh
> OCE Post Doctoral Fellow
> CSIRO Livestock Industries
> Queensland Bioscience Precinct
> St Lucia, QLD 4067
> Australia
>
> Tel: +61 (0)7 3214 2922
> Fax: +61 (0)7 3214 2900
> Web: http://www.csiro.au/people/Nathan.Watson-Haigh.html
> - --------------------------------------------------------
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