chron.stabilized {dplR}  R Documentation 
This function builds a variance stabilized meanvalue chronology, typically from a
data.frame
of detrended ring widths as produced by
detrend
.
chron.stabilized(x, winLength, biweight = TRUE, running.rbar = FALSE)
x 
a 
winLength 
a odd 
biweight 

running.rbar 

The variance of a mean chronology depends on the variance of the individual samples, the number of series averaged together, and their interseries correlation (Wigley et al. 1984). As the number of series commonly decreases towards the beginning of a chronology averaging introduces changes in variance that are a solely an effect of changes in sample depth.
Additionally, timedependent changes in interseries correlation can cause artificial variance changes of the final mean chronology. The function chron.stabilized
accounts for both temporal changes in the interseries correlation and sample depth to produce a mean value chronology with stabilized variance.
The basic correction centers around the use of the effective independent sample size, Neff
, which considers sample replication and mean interseries correlation between the samples at every time. This is defined as: Neff = n(t) / 1+(n(t)1)rbar(t)
where n(t)
is the number of series at time t
, and rbar
is the interseries correlation (see interseries.cor
). Multiplication of the mean time series with the square root of Neff
at every time t
theoretically results in variance that is independent of sample size. In the limiting cases, when the rbar
is zero or unity, Neff
obtains values of the true sample size and unity, respectively.
An object of of class crn
and data.frame
with the variance stabilized chronology, running interseries correlation ('if running.bar=TRUE
), and the sample depth.
Original code by David Frank and adapted for dplR by Stefan Klesse. Patched and improved by Andy Bunn.
Frank, D, Esper, J, Cook, E, (2006) On variance adjustments in treering chronology development. Tree rings in archaeology, climatology and ecology, TRACE 4, 56–66
Frank, D, Esper, J, Cook, E, (2007) Adjustment for proxy number and coherence in a largescale temperature reconstruction. Geophysical Research Letters 34
Wigley, T, Briffa K, Jones P (1984) On the Average Value of Correlated Time Series, with Applications in Dendroclimatology and Hydrometeorology. J. Climate Appl. Meteor., 23, 201–213
library(graphics)
library(utils)
data(co021)
co021.rwi < detrend(co021,method = "Spline")
co021.crn < chron(co021.rwi)
co021.crn2 < chron.stabilized(co021.rwi,
winLength=101,
biweight = TRUE,
running.rbar = FALSE)
yrs < time(co021)
plot(yrs,co021.crn$std,type="l",col="grey")
lines(yrs,co021.crn2$adj.crn,col="red")