chron.ars {dplR}

Build ARSTAN Chronology

Description

This function builds three varieties of the mean-value chronology, including the ARSTAN chronology, typically from a data.frame of detrended ring widths as produced by detrend.

Usage

chron.ars(x, biweight=TRUE, maxLag=10, firstAICmin=TRUE, 
  verbose=TRUE, prewhitenMethod=c("ar.yw","arima.CSS-ML"))

Arguments

x

a data.frame of (usually detrended) ring widths with rownames(x) containing years and colnames(x) containing each series ID such as produced by read.rwl

biweight

logical flag. If TRUE then a robust mean is calculated using tbrm.

maxLag

an integer giving the maximum lag to consider in the AR pooling.

firstAICmin

logical flag. If TRUE the final AR order is elected using the first AIC minimum otherwise the order is selected by the overall minimum.

verbose

logical flag. If TRUE the function prints information from the AR modeling to the screen.

prewhitenMethod

a character vector to determine the AR model fitting. See details below. Possible values are either "ar.yw" or "arima.CSS-ML". Can be abbreviated. Defaults to "ar.yw".

Details

This produces three mean-value chronologies: standard, residual, and ARSTAN. Users unfamiliar with the concept behind the ARSTAN method should look to Cook (1985) for background and inspiration.

The standard chronology is the (biweight) mean value across rows and identical to chron.

The residual chronology is the prewhitened chronology as described by Cook (1985) and uses uses multivariate autoregressive modeling to determine the order of the AR process. It's important to note that residual chronology produced here is different than the simple residual chronology produced by chron which returns the residuals of an AR process using a naive call to ar. But in practice the results will be similar. For more on the residual chronology in this function, see pp. 153-154 in Cook's 1985 dissertation.

The ARSTAN chronology builds on the residual chronology but returns a re-whitened chronology where the pooled AR coefficients from the multivariate autoregressive modeling are reintroduced. See references for details.

The order of the AR model is selected from the pooled AR coefficients by AIC using either the first (local) AIC minimum otherwise or the overall minimum considering the maximum lag (argument maxLag).

Once the AR order is determined an AR(p) model is fit using either ar via the Yule-Walker method or by arima via conditional-sum-of-squares to find starting values, then maximum likelihood. It is possible that the model will not converge in which case a warning is produced. The AR fitting is determined via prewhitenMethod and defaults to using ar.

Value

A data.frame with the standard, residual, and ARSTAN chronologies. The sample depth is also included.

Author(s)

Andy Bunn with contributions from Kevin Achukaitis and Ed Cook. Much of the function is a port of Cook's FORTRAN code.

References

Cook, E. R. and Kairiukstis, L. A., editors (1990) Methods of Dendrochronology: Applications in the Environmental Sciences. Springer. ISBN-13: 978-0-7923-0586-6.

Cook, E. R. (1985). A Time Series Analysis Approach to Tree Ring Standardization. PhD thesis, The University of Arizona.

See Also

chron, crn.plot, ar, arima

Examples

library(graphics)
library(utils)
data(co021)
co021.rwi <- detrend(rwl = co021, method = "AgeDepSpline")
co021.crn <- chron.ars(co021.rwi)
plot(co021.crn,xlab="Year",ylab="RWI",add.spline=TRUE,nyrs=20)
cor(co021.crn)