detrend.series {dplR} | R Documentation |
Detrend a tree-ring series by one of two methods, a smoothing spline or a statistical model. The series and fits are plotted by default.
detrend.series(y, y.name = NULL, make.plot = TRUE, method = c("Spline", "ModNegExp", "Mean"))
y |
a numeric vector. Usually a tree-ring series. |
y.name |
an optional character vector to name the series for plotting purposes. |
make.plot |
logical flag. Makes plots of the raw data and detrended data if TRUE. |
method |
a character vector to determine the detrending method. See details below. Possible values are “Spline”, “ModNegExp”, “Mean”, or subset of c(“Spline”, “ModNegExp”, “Mean”). |
This detrends and standardises a tree-ring series. The detrending is
the estimation and removal of the tree's natural biological growth trend.
The standardisation is done by dividing each series by the growth trend to
produce units in the dimensionless ring-width index (RWI). There are
currently three methods available for detrending although more are
certainly possible. The two methods implemented are a smoothing spline via
smooth.spline
(method = “Spline”) or a
modified negative exponential curve (method = “ModNegExp”).
The “Spline” approach uses an n-year spline where the frequency response is 0.50 at a wavelength of 0.67*n years. This attempts to remove the low frequency variability that is due to biological or stand effects.
The “ModNegExp”approach attempts to fit a classic nonlinear
model of biological growth of the form Y ~ a * exp(b*1:length(Y)) + k using
nls
. See Fritts (2001) for details about the
parameters. If a nonlinear model cannot be fit then a linear model is fit.
The “Mean”approach fits a horizontal line using the mean of the series.
These methods are chosen because they are commonly used in dendrochronology. It is, of course, up to the user to determine the best detrending method for their data. See the references below for further details on detrending.
A data.frame
containing the detrended series (y) according to the method(s)
used.
Andy Bunn
Cook, E.R. and Kairiukstis, L.A. (1990) Methods of Dendrochronology: Applications in the Environmental Sciences. Springer. ISBN-13: 978-0792305866.
Fritts, H.C. (2001) Tree Rings and Climate. Blackburn. ISBN-13: 978-1930665392.
# Using a plausible representation of a tree-ring series gt <- 0.5 * exp (-0.05 * 1:200) + 0.2 noise <- c(arima.sim(model = list(ar = 0.7), n = 200, mean = 1, sd = 0.5)) series <- gt * noise series.rwi <- detrend.series(y=series,y.name="Foo") # Use series CAM011 from the Campito dataset data(ca533) series <- ca533[,"CAM011"] names(series) <- rownames(ca533) series.rwi <- detrend.series(y = series, y.name = "CAM011")