cclag_nosnr: Estimation of correlation coefficients and time lags of two...

In marcianito/UmbrellaEffect: Gravity data reduction procedure (related to the shielding of its building)

Description

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).

Usage

cclag_nosnr(data1, data2, data1.raw, data2.raw, nmax, normdata = T,
  mv_win = 96, ccfplot = F, resplot = F, sensor1 = "data1",
  sensor2 = "data2", ...)

Arguments

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.

Details

missing

References

Marvin Reich (2014), [e-mail]

Examples

outside.cor = ccf.zoo(besidesBuilding$mux43_04,besidesBuilding$mux43_08,5,T,T,T,24,17521)

marcianito/UmbrellaEffect documentation built on July 1, 2019, 8:30 p.m.