View source: R/ccf.series.rwl.R
ccf.series.rwl  R Documentation 
Computes crosscorrelations between a treering series and a master chronology built from a rwl object at userspecified lags and segments.
ccf.series.rwl(rwl, series, series.yrs = as.numeric(names(series)),
seg.length = 50, bin.floor = 100, n = NULL,
prewhiten = TRUE, biweight = TRUE, pcrit = 0.05,
lag.max = 5, make.plot = TRUE,
floor.plus1 = FALSE, series.x = FALSE, ...)
rwl 
a 
series 
a 
series.yrs 
a 
seg.length 
an even integral value giving length of segments in years (e.g., 20, 50, 100 years). 
bin.floor 
a nonnegative integral value giving the base for locating the first segment (e.g., 1600, 1700, 1800 AD). Typically 0, 10, 50, 100, etc. 
n 

prewhiten 

biweight 

pcrit 
a number between 0 and 1 giving the critical value for the correlation test. 
lag.max 
an integral value giving the maximum lag at which to
calculate the 
make.plot 

floor.plus1 

series.x 

... 
other arguments passed to plot. 
This function calculates the crosscorrelation function between a
treering series and a master chronology built from rwl
looking at correlations lagged positively and negatively using
ccf
at overlapping segments set by
seg.length
. For instance, with lag.max
set
to 5, crosscorrelations would be calculated at for each segment with
the master lagged at k = 5:5
years.
The cross correlations are calculated calling
ccf
as
ccf(x=master, y=series, lag.max=lag.max, plot=FALSE)
if series.x
is
FALSE
and as ccf(x=series, y=master, lag.max=lag.max, plot=FALSE)
if
series.x
is TRUE
. This argument was introduced in dplR version 1.7.0.
Different users have different expectations about how missing or extra rings are
notated. If switch.x = FALSE
the behavior will be like COFECHA where a missing
ring in a series produces a negative lag in the plot rather than a positive lag.
Correlations are calculated for the first segment, then the second segment and so on. Correlations are only calculated for segments with complete overlap with the master chronology.
Each series (including those in the rwl
object) is
optionally detrended as the residuals from a hanning
filter with weight n
. The filter is not applied if
n
is NULL
. Detrending can also be done via
prewhitening where the residuals of an ar
model are
added to each series mean. This is the default. The master chronology
is computed as the mean of the rwl
object using
tbrm
if biweight
is TRUE
and
rowMeans
if not. Note that detrending typically changes the
length of the series. E.g., a hanning
filter will
shorten the series on either end by floor(n/2)
. The
prewhitening default will change the series length based on the
ar
model fit. The effects of detrending can be seen with
series.rwl.plot
.
A list
containing matrices ccf
and
bins
. Matrix ccf
contains the correlations
between the series and the master chronology at the lags window given
by lag.max
. Matrix bins
contains the years
encapsulated by each bin.
Andy Bunn. Patched and improved by Mikko Korpela.
Bunn, A. G. (2010) Statistical and visual crossdating in R using the dplR library. Dendrochronologia, 28(4), 251–258.
corr.rwl.seg
, corr.series.seg
,
skel.plot
, series.rwl.plot
library(utils)
data(co021)
dat < co021
## Create a missing ring by deleting a year of growth in a random series
flagged < dat$"641143"
flagged < c(NA, flagged[325])
names(flagged) < rownames(dat)
dat$"641143" < NULL
ccf.100 < ccf.series.rwl(rwl = dat, series = flagged, seg.length = 100)
## Not run:
flagged2 < co021$"641143"
names(flagged2) < rownames(dat)
ccf.100.1 < ccf.series.rwl(rwl = dat, seg.length = 100,
series = flagged2)
## Select series by name or column position
ccf.100.2 < ccf.series.rwl(rwl = co021, seg.length = 100,
series = "641143")
ccf.100.3 < ccf.series.rwl(rwl = co021, seg.length = 100,
series = which(colnames(co021) == "641143"))
identical(ccf.100.1, ccf.100.2) # TRUE
identical(ccf.100.2, ccf.100.3) # TRUE
## End(Not run)
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