dendroclim {bootRes} | R Documentation |
This function calculates response and correlation functions from tree-ring chronologies and monthly climatic data. Function parameters are bootstrapped to calculate their significance and confidence intervals.
dendroclim(chrono, clim, method = "response", start = -6, end = 9, timespan = NULL)
chrono |
data.frame containing a tree-ring chronologies, e.g. as obtained by chron of package dplR.
|
clim |
data.frame with climatic data in monthly resolution, with year, month, temperature and precipitation in columns.
|
method |
string specifying the calculation method. Possible values are “response” and “correlation”. Partial strings are ok. |
start |
integer value to determine the first month to be used as a predictor in the response or correlation function. A negative value denotes a start month from previous year, a positive value denotes a start month from current year. |
end |
integer value to determine the last month to be used as a predictor in the response or correlation function. A negative value denotes a end month from previous year, a positive value denotes a end month from current year. |
timespan |
integer vector of length 2 specifying the time interval (in years) to be considered for analysis. Defaults to the maximum possible interval. |
In its current state this function is a clone of programme DENDROCLIM2002 (Biondi and Waikul, 2004), and will calculate bootstrapped response and correlation functions in a similar manner as described in the above mentioned paper. In case of response function analysis 1000 bootstrap samples are taken from the original distribution and an eigen decomposition of the standardized predictor matrix is performed. Nonrelevant eigenvectors are removed using the PVP criterion (Guiot, 1990), principal component scores are then calculated from the matrices of reduced eigenvectors and standardized climatic predictors. Response coefficients are found via singular value decomposition, and tested for significance using the 95% percentile range method (Dixon, 2001). In case of correlation function analysis, the coefficients are Pearson's correlation coefficients. The same method for significance testing is applied.
Input chronology data can be a data.frame
such as produced by function chron
of package dplR. It has to be a data.frame
with at least one column containing the tree-ring indices, and the corresponding years as rownames
. Input climatic data has to be a data.frame
or matrix
consisting of 4 rows for years, months, mean monthly temperature and total monthly precipitation in the given order.
The window for response/correlation function analysis is specified via start
and end
, where e.g. -4 means previous April etc.
A data.frame
with a response/correlation coefficient for each parameter, its significance (coded as 0/1) and its 95% confidence intervall.
Christian Zang
Biondi, F. & Waikul, K. (2004) DENDROCLIM2002: A C++ program for statistical calibration of climate signals in tree-ring chronologies. Computers & Geosciences 30:303-311
Dixon, P.M. (2001) Bootstrap resampling. In: El-Shaarawi, A.H., Piegorsch, W.W. (Eds.), The Encyclopedia of Environmetrics. Wiley, New York.
Guiot, J. (1991) The boostrapped response function. Tree-Ring Bulletin 51:39-41
data(muc.clim) # climatic data data(muc.spruce) # spruce data # calculate and plot response function dc.resp <- dendroclim(muc.spruce, muc.clim) dcplot(dc.resp) # calculate and plot correlation function dc.corr <- dendroclim(muc.spruce, muc.clim, method = "corr") dcplot(dc.corr) # use modelled data for better response ;-) data(muc.fake) dc.resp.fake <- dendroclim(muc.fake, muc.clim) dcplot(dc.resp.fake)