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 > > [[alternative HTML version deleted]] > > ______________________________________________ > 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. > ______________________________________________ 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.
