Description Usage Arguments Details Value Author(s) References See Also Examples
This function calculates response and correlation functions from treering chronologies and monthly climatic data. Function parameters are bootstrapped to calculate their significance and confidence intervals.
1 2 
chrono 

clim 

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 an end month from previous year, a positive value denotes an 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. 
vnames 
character vector with variable names. defaults to corresponding column names of 
sb 
logical flag indicating whether textual status bar should be suppressed. Suppression is recommended for e.g. Sweave files. 
boot 
logical flag indicating whether bootstrap resampling is to be performed. If set to FALSE, no significance estimates and confidence intervals are provided. 
ci 
numerical value to set the test level for significance test (values 0.01, 0.05 and 0.1 are allowed); the confidence intervals are adapted accordingly. 
The functions dcc
and mdcc
clone the
functionality of programme DENDROCLIM2002 (Biondi and Waikul, 2004), and
will calculate bootstrapped (and nonbootstrapped) moving (mdcc
and static (dcc
) response and correlation functions in a similar
fashion 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 treering indices, and the
corresponding years as rownames
.
For climatic input data, there are three possibilities: Firstly, input
climatic data can be a data.frame
or matrix
consisting of
at least 3 rows for years, months and at least one climate parameter in
the given order. Secondly, input climatic data can be a single
data.frame
or matrix
in the style of the original
DENDROCLIM2002 input data, i.e. one parameter with 12 months in one row,
where the first column represents the year. Or thirdly, input climatic
data can be a list of several of the latter described data.frame
or matrices
. As an internal format dispatcher checks the format
automatically, it is absolutely necessary that in all three cases, only
complete years (months 112) are provided. It is not possible to mix
different formats in one go.
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. If boot
is set to FALSE, no significance and confidence
intevals are computed, the values are set to NA.
Christian Zang
Biondi, F. & Waikul, K. (2004) DENDROCLIM2002: A C++ program for statistical calibration of climate signals in treering chronologies. Computers & Geosciences 30:303311
Dixon, P.M. (2001) Bootstrap resampling. In: ElShaarawi, A.H., Piegorsch, W.W. (Eds.), The Encyclopedia of Environmetrics. Wiley, New York.
Guiot, J. (1991) The boostrapped response function. TreeRing Bulletin 51:3941
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  data(muc.clim) # climatic data
data(muc.spruce) # spruce data
# calculate and plot response function
dc.resp < dcc(muc.spruce, muc.clim)
dcplot(dc.resp)
# calculate and plot correlation function
dc.corr < dcc(muc.spruce, muc.clim, method = "corr")
dcplot(dc.corr)
# use modelled data for better response ;)
data(muc.fake)
dc.resp.fake < dcc(muc.fake, muc.clim)
dcplot(dc.resp.fake)
# use DENDROCLIM2002style data
data(rt.spruce)
data(rt.temp)
data(rt.prec)
dc.resp < dcc(rt.spruce, list(rt.temp, rt.prec), vnames =
c("Temperature", "Precipitation"))
dcplot(dc.resp)

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