Description Usage Arguments Details References Examples
THIS IS A NEW VERSION..ojo!! has to be decided and merged afterwards!!!!
or replace old timeshift()-function !?
This function is based on standard time series decomposition and afterwards cross-correlation estimation between the two input datasets.
The main aim is to find dominant time shifts between two time series (e.g. soil moisture, climate data, precipitation stations, etc).
1 2 |
data1, data2 |
Timeseries of type .zoo. |
nmax |
Number of correlation values output. |
mv_win |
window width used for signal to noise detection. value is numerical and represents data-timesteps. |
ccfplot |
logical. Plots for each time series interval the cross-correlation plots. Default is F, own output plots are provided. Should be kept FALSE. |
resplot |
logical. Plots an overall plot with results of correlation coefficients, lagtimes and signal to noise relationships of the complete datasets. |
sensor1, sensor2 |
names used for the input datasets for plotting and in the output table. |
... |
Further parameters passed to internal functions. |
norm |
logical. Use normalized data (default) or not. |
missing
Marvin Reich (2014), mreich@gfz-potsdam.de
1 | outside.cor = ccf.zoo(besidesBuilding$mux43_04,besidesBuilding$mux43_08,5,T,T,T,24,17521)
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