Computes the correlation between each tree-ring 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)
}),
…)a data.frame with series as columns and years as
rows such as that produced by read.rwl.
an even integral value giving length of segments in years (e.g., 20, 50, 100 years).
a non-negative integral value giving the base for locating the first segment (e.g., 1600, 1700, 1800 AD). Typically 0, 10, 50, 100, etc.
NULL or an integral value giving the filter length
for the hanning filter used for removal of low
frequency variation.
logical flag. If TRUE each series is
whitened using ar.
a number between 0 and 1 giving the critical value for the correlation test.
logical flag. If TRUE then a robust
mean is calculated using tbrm.
Can be either "pearson", "kendall", or
"spearman" which indicates the correlation coefficient to be
used. Defaults to "spearman". See cor.test.
logical flag indicating whether to make a
plot.
numeric scalar for the series labels on the
plot. Passed to axis.cex in axis.
logical flag. If TRUE, one year is
added to the base location of the first segment (e.g., 1601, 1701,
1801 AD).
NULL, a numeric vector or a
matrix-like object of numeric values, including a
data.frame. If NULL, a number of master chronologies,
one for each series in rwl, is built from
rwl using the leave-one-out principle. If a
vector, the function uses this as the master chronology. If
a matrix or data.frame, this object is used for
building the master chronology (no leave-one-out).
a numeric vector giving the years of
series. Defaults to names or rownames of
master coerced to numeric type.
other arguments passed to plot.
A list containing matrices spearman.rho,
p.val, overall, bins, vector
avg.seg.rho. 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 p-values on the correlation for each series by
bin. Matrix overall contains the average correlation and
p-value for each series. Matrix bins contains the years
encapsulated by each bin. The vector avg.seg.rho
contains the average correlation for each bin.
This function calculates correlation serially between each tree-ring
series and a master chronology built from all the other series in the
rwl object (leave-one-out 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., 100-year segments would
be overlapped by 50-years). 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 user-specified 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<U+2019>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 user-specified critical value. Red segments are those that correlate below the user-specified critical value and might indicate a dating problem.
# NOT RUN {
library(utils)
data(co021)
crs <- corr.rwl.seg(co021, seg.length = 100, label.cex = 1.25)
names(crs)
## Average correlation and p-value for the first few series
head(crs$overall)
## Average correlation for each bin
crs$avg.seg.rho
# }
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