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Lines, Sines, and Curve Fitting 5 – a growth

12 Jan

From the comments in Curve Fitting 4:

If you want to try a different flavor of gum, consider simultaneously fitting exponential + sine models:

y1(t) = y1(0) * exp(kt)
y2(t) = A * sin(((t-b)/T) * 2 * pi)
y(t) = y1(t) + y2(t)

You’ve probably already thought of this, of course.

Ned’s been peeking ahead, so maybe I shouldn’t “reward” him. ;-) But here it is anyway. The procedures are similar. I fitted an exponential with a sine of the residuals, a sine with an exponential of the residuals, and fitting both a sine and exponential simultaneously. I know that there are pre-packaged R methods for fitting exponentials, but I use the method of looking for best fit in by looping through the parameter space.

The form of the exponential that I used was: y1= -a/100 + (bb/10) * exp((k/10000)*(YEARS-1880))

GISTEMP exp sine
Exp a = 149
Exp bb = 11
Exp k = 40

Sine Amplitude A = 8
Sine Phase Shift b = 24
Sine Period T = 56


GISTEMP sine exp
Sine Amplitude A = 16
Sine Phase Shift b = 24
Sine Period T = 50

Exp a = 130
Exp bb = 10
Exp k = 34


GISTEMP exp sine both

Exp a = 145
Exp bb = 11
Exp k = 37

Sine Amplitude A = 9
Sine Phase Shift b = 34
Sine Period T = 61

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4 Comments

Posted by on 2011 January 12 in GIStemp, LSCF

 

Tags: , ,

4 Responses to Lines, Sines, and Curve Fitting 5 – a growth

  1. Ned

    2011 January 12 at 11:36 am

    That was quick. It looks similar to my version. Thank you!

    I am curious about where you are leading us with all of this curve-fitting. Are you just doing “exploratory data analysis” here, or are you building up to something? No need to actually answer that if you don’t want to; I will wait and see.

     
  2. Ron Broberg

    2011 January 12 at 11:47 am

    I figured that before I went snipe-hunting, I should do some target practise first.

    Part of it is just to get better at R. Another is to get more familiar with equations which are statistically justifiable. A third is to walk slowly towards better tools: Fourier, Arima, ACF, PCA. Confidence intervals in complex eqns. Auto-correlation. There is lots of things right for me to learn some basics and not-so-basics.

    But, no, I don’t have a pre-planned conclusion in mind. This is stream-of-consciousness, baby-steps in the dark stuff. But I would like to be able to better analyze the claims of 1500,1100,800,180,60,22,11 or whatever cycles. So there will be some stuff along those lines eventually.

     

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