Your data will have all sorts of patterns (diurnal, seasonal) in
addition to long term trend. I'd start by smoothing out the cyclic
patterns with loess or gam, then use a secant approximation to the
slope on the smoothed series.
albyn
On Fri, Jul 24, 2009 at 06:13:00PM +0530, Yogesh Tiwari
wrote:> Dear R Users,
> If a variable, say CO2(ppm), is varying with time. Then how to calculate
CO2
> (ppm) growth rate /a-1
> I have CO2 time series (1991-2000), as:
>
> time, year, month, day, hour, min, sec, lat, long, height, CO2
> 1991.476722 1991 6 24 0 5 0 -38.93 145.15 4270 353.680
> 1991.476741 1991 6 24 0 15 0 -39.20 145.22 4270 353.950
> 1991.476747 1991 6 24 0 18 0 -39.43 145.28 4270 353.510
> -----------------------------------------------
> 2000.740788 2000 9 28 3 5 0 -38.00 145.00 2280 366.750
> 2000.740794 2000 9 28 3 8 0 -38.00 145.00 1830 366.550
> 2000.740803 2000 9 28 3 13 0 -38.00 145.00 1220 370.550
>
> --
> Yogesh K. Tiwari (Dr.rer.nat),
> Scientist,
> Indian Institute of Tropical Meteorology,
> Homi Bhabha Road,
> Pashan,
> Pune-411008
> INDIA
>
> Phone: 0091-99 2273 9513 (Cell)
> : 0091-20-25904350 (O)
> Fax : 0091-20-258 93 825
>
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