R: Individual Series Correlation Against a Master Chronology

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 `data.frame` with series as columns and years as rows such as that produced by `read.rwl`.
`n` | `NULL` or an integral value giving the filter length for the `hanning` filter used for removal of low frequency variation.
`prewhiten` | `logical` flag. If `TRUE` each series is whitened using `ar`.
`biweight` | `logical` flag. If `TRUE` then a robust mean is calculated using `tbrm`.
`method` | Can be either `"pearson"`, `"kendall"`, or `"spearman"` which indicates the correlation coefficient to be used. Defaults to `"spearman"`. See `cor.test`.

### 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

`rwl.stats`, `rwi.stats`

### 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])

[Package dplR version 1.7.1 Index]