annualnoise: Annual noise color

In discharge: Fourier Analysis of Discharge Data

Description

The autocovariance function is estimated for the annual maxima in the series. An autoregressive model of the order of the highest significant lag is fit, using the Yule-Walker method to estimate the parameters. The function is transformed into the frequency domain, yielding an estimate theta.a of the annual noise color.

Usage

annualnoise(x)

Arguments

x	A numeric vector of annual extremes. A streamflow object may also be used. If input is streamflow the function uses annual maximum discharge.

Details

To determine the order of the AR model, the ACF is calculated at all lags less than or equal to the highest power of 2 less than the length of the series. The order of the AR model is the lag with the highest significantly non-zero autocorrelation.

Value

An object of S3 class annualnoise with the following attributes:

auto.corr	Sample autocorrelation.
lm.fit	lm object from regression of log power spectrum on log frequency.
interval	Upper and lower bounds of a 95% acceptance region when ρ=0.
log.log	Matrix with log frequency and log power spectrum.
reg.stats	Slope and intercept of regression of log power spectrum on log frequency, where slope is the annual noise color (theta.a).
order	Indicates order of fitted AR model.
fit.ar	Object of class ar summarizing the fitted AR model.

Examples

data(sycamore)
sycamore.flows<-asStreamflow(sycamore,river.name="Sycamore Creek")
syc.ar<-annualnoise(sycamore.flows)

discharge documentation built on May 2, 2019, 5:54 a.m.