View source: R/interseries.cor.R
interseries.cor  R Documentation 
This function calculates the correlation between a series and a master chronology.
interseries.cor(rwl,n=NULL,prewhiten=TRUE,biweight=TRUE,
method = c("spearman", "pearson", "kendall"))
rwl 
a 
n 

prewhiten 

biweight 

method 
Can be either 
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).
Each series 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
.
This function produces the same output of the overall
portion of
corr.rwl.seg
. The mean correlation value given is sometimes
referred to as the “overall interseries correlation” or the “COFECHA
interseries correlation”. This output differs from the rbar
statistics given by rwi.stats
in that rbar
is
the average pairwise correlation between series where this is the
correlation between a series and a master chronology.
a data.frame
with correlation values and pvalues given from
cor.test
Andy Bunn, patched and improved by Mikko Korpela
rwl.stats
, rwi.stats
library(utils)
data(gp.rwl)
foo < interseries.cor(gp.rwl)
# compare to:
# corr.rwl.seg(rwl=gp.rwl,make.plot=FALSE)$overall
# using pearson's r
foo < interseries.cor(gp.rwl,method="pearson")
# two measures of interseries correlation
# compare interseries.cor to rbar from rwi.stats
gp.ids < read.ids(gp.rwl, stc = c(0, 2, 1))
bar < rwi.stats(gp.rwl, gp.ids, prewhiten=TRUE)
bar$rbar.eff
mean(foo[,1])
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