corr.rwl.seg  R Documentation 
Computes the correlation between each treering series in a rwl object.
corr.rwl.seg(rwl, seg.length = 50, bin.floor = 100, n = NULL,
prewhiten = TRUE, pcrit = 0.05, biweight = TRUE,
method = c("spearman", "pearson","kendall"),
make.plot = TRUE, label.cex = 1, floor.plus1 = FALSE,
master = NULL,
master.yrs = as.numeric(if (is.null(dim(master))) {
names(master)
} else {
rownames(master)
}),
...)
rwl 
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 

pcrit 
a number between 0 and 1 giving the critical value for the correlation test. 
biweight 

method 
Can be either 
make.plot 

label.cex 

floor.plus1 

master 

master.yrs 
a 
... 
other arguments passed to plot. 
This function calculates correlation serially between each treering
series and a master chronology built from all the other series in the
rwl
object (leaveoneout principle). Optionally, the
user may give a master
chronology (a vector
) as an
argument. In the latter case, the same master chronology is used for
all the series in the rwl
object. The user can also
choose to give a master
data.frame
(series as
columns, years as rows), from which a single master chronology is
built.
Correlations are done for each segment of the series where segments
are lagged by half the segment length (e.g., 100year segments would
be overlapped by 50years). The first segment is placed according to
bin.floor
. The minimum bin year is calculated as
ceiling(min.yr/bin.floor)*bin.floor
where
min.yr
is the first year in either the rwl
object or the userspecified master
chronology, whichever
is smaller. For example if the first year is 626 and
bin.floor
is 100 then the first bin would start in 700.
If bin.floor
is 10 then the first bin would start in 630.
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. For now, correlations are
Spearman’s rho calculated via cor.test
using
method = "spearman"
.
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 can change 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
.
The function is typically invoked to produce a plot where each segment for each series is colored by its correlation to the master chronology. Green segments are those that do not overlap completely with the width of the bin. Blue segments are those that correlate above the userspecified critical value. Red segments are those that correlate below the userspecified critical value and might indicate a dating problem.
A list
containing matrices spearman.rho
,
p.val
, overall
, bins
,
rwi
, vector avg.seg.rho
,
numeric seg.lag
, seg.length
, pcrit
,
label.cex
. An additional character
flags
is also returned if any segments fall below the
critical value. Matrix spearman.rho
contains the
correlations for each series by bin. Matrix p.val
contains the pvalues on the correlation for each series by
bin. Matrix overall
contains the average correlation and
pvalue for each series. Matrix bins
contains the years
encapsulated by each bin. The vector avg.seg.rho
contains the average correlation for each bin. Matrix rwi
contains the detrended rwl data, the numerics seg.lag
,
seg.length
, pcrit
, label.cex
are from the oroginal call and used to pass into plot.crs
.
Andy Bunn. Patched and improved by Mikko Korpela.
corr.series.seg
, skel.plot
,
series.rwl.plot
, ccf.series.rwl
,
plot.crs
library(utils)
data(co021)
crs < corr.rwl.seg(co021, seg.length = 100, label.cex = 1.25)
names(crs)
## Average correlation and pvalue for the first few series
head(crs$overall)
## Average correlation for each bin
crs$avg.seg.rho
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