effective_ts_length: degrees of freedom of an irregular autocorrelated time...
In krehfeld/nest: Non-equally spaced time series analysis

Description Usage Arguments Value Author(s) References See Also Examples

Get the effective length, or degrees of freedom, of an irregular autocorrelated time series, using different methods.

1	effective_ts_length(X, method = "tau")

`X`	A zoo-time-series object.
`method`	One of "tau", "mudelsee", or "simple"

Approximated number of degrees of freedom, estimated using the autocorrelation of the time series.

Kira Rehfeld, with contribution from Thom Laepple.

von Storch, H., and Zwiers, F.W., 1999. Statistical Analysis in Climate Research. Cambridge University Press [Page number to be checked] Dawdy, D.R., and Matalas, N.C., 1964, Statistical and probability analysis of hydrologic data,part III: Analysis of variance, covariance and time series, in Ven Te Chow, ed., Handbook of applied hydrology, a compendium of water-resources technology: New York, McGraw-Hill Book Company, p. 8.68-8.90. Mudelsee,M., 2010. Climate Time Series Analysis - Classical Statistical and Bootstrap Methods. Springer ISBN: 9789048194827, 474 p [Page number for reference to be checked]

nexcf_ci

## Generate one gamma-distributed and one regular time axis
tx<-generate_t(dt=1,tmin=0,tmax=100,method="gamma")
ty<-generate_t(dt=1,tmin=0,tmax=100,method="linear")
## Simulate one coupled AR1 process (see reference for details)
Proc<-car(tx,ty,coupl_strength=0.5,phi=0.5,lag=0,nsur=1)
## Bind the results to zoo time series
x<-zoo(Proc$x,order.by=tx)
y<-zoo(Proc$y,order.by=ty)
## Estimate the autocorrelation
phi.est=nexcf(x,lag=1,h=0.25)

plot(x)
lines(y,col="red")

rxy=nexcf(x,y,lag=0,h=0.25)
rx1=nexcf(x,lag=1)
ry1=nexcf(y,lag=1)

nexcf_ci(x,y)


effective_ts_length(x,method="tau") # adapted from von Storch/Zwiers 1999
effective_ts_length(x,method="mudelsee") # after Mudelsee, 2010
effective_ts_length(x,method="simple") # using both time directions