| interseries.cor {dplR} | R Documentation |
Individual Series Correlation Against a Master Chronology
Description
This function calculates the correlation between a series and a master chronology.
Usage
interseries.cor(rwl,n=NULL,prewhiten=TRUE,biweight=TRUE,
method = c("spearman", "pearson", "kendall"))
Arguments
rwl |
a |
n |
|
prewhiten |
|
biweight |
|
method |
Can be either |
Details
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).
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.
Value
a data.frame with correlation values and p-values given from
cor.test
Author(s)
Andy Bunn, patched and improved by Mikko Korpela
See Also
Examples
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])