This function calculates moving response and correlation functions from tree-ring chronologies and monthly climatic data. The calculation is performed repeatedly for consecutive time windows. Function parameters may be bootstrapped to calculate their significance and confidence intervals.
mdcc(chrono, clim, method = "response", start = 4, end = 9, timespan =
NULL, vnames = NULL, sb = TRUE, win.size = 25, win.offset =
1, startlast = TRUE, boot = FALSE, ci = 0.05)data.frame containing a tree-ring chronologies, e.g. as obtained by chron of package dplR.
data.frame with climatic data in monthly resolution, with year, month and climate parameters in columns. All columns except year and month will be recognized as parameters for response or correlation function.
string specifying the calculation method. Possible values are “response” and “correlation”. Partial strings are ok.
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.
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.
integer vector of length 2 specifying the time interval (in years) to be considered for analysis. Defaults to the maximum possible interval.
character vector with variable names. defaults to corresponding column names of data.frame clim.
logical flag indicating whether textual status bar should be suppressed. Suppression is recommended for e.g. Sweave files.
integer giving the window size for each recalculation.
integer giving the number of years between each window start.
logical flag indicating whether the first window should start at the rear end (youngest part of the series) or not.
logical flag indicating whether bootstrap resampling is to be performed.
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.
A list containing data.frames for coefficients and
confidence intervals for each parameter and time window used for the
moving functions.
The functions dcc and mdcc clone the
functionality of programme DENDROCLIM2002 (Biondi and Waikul, 2004), and
will calculate bootstrapped (and non-bootstrapped) 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 tree-ring 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 1-12) 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.
The window size for moving response and correlation functions is set via
win.size, and the distance from one window start to the next is
set with the parameter win.offset. Parameter startlast
indicates, wether the first window is started from the rear (youngest
part) of the series or not.
Bootstrapping (boot) is by default disabled to get the results
faster.
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
# NOT RUN {
data(muc.clim) # climatic data
data(muc.spruce) # spruce data
# calculate and plot moving response function
dc.mov1 <- mdcc(muc.spruce, muc.clim)
mdcplot(dc.mov1)
# calculate and plot moving correlation function with different window
# parameters
data(rt.spruce)
data(rt.temp)
data(rt.prec)
dc.mov2 <- mdcc(rt.spruce, list(rt.temp, rt.prec),
vnames = c("temp", "prec"),
method = "corr", win.size = 20,
win.offset = 5)
mdcplot(dc.mov2)
# }
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