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 wro
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
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