dplR (version 1.3.7)

ccf.series.rwl: Compute Cross Correlation between a Series and a Master Chronology

Description

Computes cross correlations between a tree-ring series and a master chronology built from a rwl object at user-specified lags and segments.

Usage

ccf.series.rwl(rwl,series, series.yrs=as.numeric(names(series)),
  seg.length=50,bin.floor=100,n=NULL, prewhiten = TRUE, biweight=TRUE,
  pcrit=0.05, lag.max=5, 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.

lag.max

an integer giving the maximum lag at which to calculate the ccf.

make.plot

logical flag indicating whether to make a plot.

...

other arguments passed to plot.

Value

• A list containing matrices ccf and bins. Matrix ccf contains the correlations between the series and the master chronology at the lags window given by lag.max. Matrix bins contains the years encapsulated by each bin.

Details

This function calculates the cross-correlation function between a tree-ring series and a master chronology built from a rwl object looking at corrlations lagged postively and negatively using link{ccf} at overlapping segments set by seg.length. For instance, with with lag.max set to 5, cross correlations would be calculated at for each segment with the master lagged at k=c(-5:5) years. The function is typically invoked to produce a plot. Correlations are calculcated for the first segment, then the second segment and so on. Correlations are only calculated for segments with complete overlap with the master chronology. Each series (inlcuding 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 rwl object using tbrm if biweight=TRUE and rowMeans if not. Note that detrending typically changes 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.

See Also

corr.rwl.seg, corr.series.seg, skel.plot, series.rwl.plot

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
  ccf.100=ccf.series.rwl(rwl=dat,series=flagged,seg.length=100)