Description Usage Arguments Examples
View source: R/CorIrregTimser.R
estimating the time scale dependent correlation of irregularly sampled time series
1 2 3 4 5 | CorIrregTimser(timser1, timser2, detr, method = c("InterpolationMethod",
"DirectFiltering", "IntegrandInterpolationMethod"),
appliedFilter = c("gauss", "runmean", "lowpass"), fc, tn = seq(from = 10,
to = max(c(index(timser1), index(timser2))), by = 10), dt,
int.method = c("linear", "nearest"), k = 5, filt.output)
|
timser1, timser2 |
time series (zoo-objects) |
detr |
TRUE for removing a linear trend, else FALSE |
method |
method to handle irregularity of sampling (DirectFiltering, IntegrandInterpolationMethod, InterpolationMethod) |
appliedFilter |
time domain filter (gauss, runmean, lowpass) |
fc |
cut-off frequency of the applied filter |
tn |
output vector (time) of the filtered data (only used in case of DirectFiltering and IntegrandInterpolationMethod) |
dt |
regular inter-observation time step of the interpolation (only used in case of InterpolationMethod) |
int.method |
kind of interpolation (linear, nearest neighbor) (only used in case of InterpolationMethod) |
k |
scaling factor to define the sharpness of the lowpass |
filt.output |
TRUE for returning correlation ($cor) and filter results ($ft1, $ft2), FALSE for only returning correlation |
1 2 3 | timeseries1 <- zoo(rnorm(100), order.by=sort(runif(100,min=1,max=1000)))
timeseries2 <- zoo(rnorm(100), order.by=sort(runif(100,min=1,max=1000)))
CorIrregTimser(timser1=timeseries1, timser2=timeseries2, detr=FALSE, method="InterpolationMethod", appliedFilter="gauss", fc=1/200, tn=NA, dt=10, int.method="linear", k=NA, filt.output=TRUE)
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