seascorr(chrono, climate, var_names = NULL, timespan = NULL, complete = 9,
season_lengths = c(1, 3, 6), primary = 1, secondary = 2, ci = 0.05)data.frame containing a tree-ring
chronologies, e.g. as obtained by chron of package dplR.data.frame or matrix 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),character vector with variable
names. Defaults to corresponding column names of
data.frame clim.integer vector of length 2 specifying the
time interval (in years) to be considered for analysis. Defaults
to the maximum possible interval.integer scalar, month when tree-ring growth
is expected to have finished.numeric vector giving the lengths of
the seasons for variable groupingnumeric or name character of
primary climate variablenumeric or name character
of secondary climate variablenumeric scalar 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 'plot' function is used to obtain a plot of the results.
An object of class '"tc_seascorr"' is a list containing at least the following components:
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 `complete` parameter specifies the months of the current year in which
tree-growth is assumed to finish. This month marks the last month of the
first season, and starting from here, 14 different seasons are computed
for each specified season length in one-month steps. E.g., for a starting
value of 9 (current September) and season length of 3 months, the first
season comprises current July to current September, the second season
comprises current June to current August, and the last season comprises
previous June to previous August. This results in 14 seasons for a given
season length. An arbitrary number of season lengths can be specified.
The choice for primary vs. secondary variable can be made either via
numeric selection (the integer value 1 stands for the first variable in
the supplied climate data set), or by name ("temp", when one of the
variables is named "temp"). The correlation of the primary variable with
tree-growth is computed as the simple (Pearson) correlation coefficient,
while the influence of the secondary variable on tree-growth is computed
with the influence of the primary variable on tree-growth removed.
Like in the original seascorr program, the significance of each (partial)
correlation is evaluated using exact bootstrapping by circulant embedding
of the tree-ring data (Percival & Constantine, 2006).Percival DB, Constantine WLB (2006) Exact simulation of Gaussian Time Series from Nonparametric Spectral Estimates with Application to Bootstrapping. Statistics and Computing 16:25-35
sc <- seascorr(muc_fake, muc_clim)
sc
plot(sc)Run the code above in your browser using DataLab