dplR (version 1.3.7)

corr.series.seg: Compute Correlation between a Series and a Master Chronology

Description

Compute correlation between a tree-ring series and a master chronology by segment.

Usage

corr.series.seg(rwl,series,series.yrs=as.numeric(names(series)),
  seg.length=50,bin.floor=100,n=NULL, prewhiten = TRUE, biweight=TRUE,
  pcrit=0.05, make.plot = TRUE,...)

Arguments

rwl

a data.frame with series as columns and years as rows such as that produced by read.rwl.

series

a numeric vector. Usually a tree-ring series.

series.yrs

a numeric vector giving the years of series. Defaults to as.numeric(names(series)).

seg.length

an even integer giving length of segments in years (e.g., 20, 50, 100 years).

bin.floor

a positive integer giving the base for locating the first segment (e.g.,1600, 1700, 1800 AD). Typically 0, 10, 50, 100, etc.

n

NULL or an integer 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.

pcrit

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

make.plot

logical flag indicating whether to make a plot.

...

other arguments passed to plot.

Value

• A list containing matrices bins, moving.rho, and vectors spearman.rho, p.val, and overall. Matrix bins contains the years encapsulated by each bin (segments). Matrix moving.rho contains the moving correlation and p-value for a moving average equal to seg.length. Vector spearman.rho contains the correlations each series by bin and p.val contains the p-values. Vector overall contains the average correlation and p-value.

Details

This function calculates the correlation a tree-ring series and a master chronology built from a rwl object. Correlations are done by segment (see below) and with a moving correlation with length equal to the seg.length. The function is typically invoked to produce a plot.

See Also

corr.series.seg skel.plot series.rwl.plot ccf.series.rwl

Examples

data(co021)
  dat=co021
  #create a missing ring by deleting a year of growth in a random series
  flagged=dat$'641143'
  flagged=c(NA,flagged[-325])
  names(flagged)=rownames(dat)
  dat$'641143'=NULL
  seg.100=corr.series.seg(rwl=dat,series=flagged,seg.length=100,biweight=FALSE)