gen.sigplusnoise.wge: Generate data from a signal-plus-noise model

In tswge: Time Series for Data Science

Generate data from a signal-plus-noise model

Description

Generate a realization from the model x(t)=coef[1]*cos(2*pi*freq[1]*t+psi[1])+coef[2]*cos(2*pi*freq[2]*t+psi[2])+a(t)

Usage

gen.sigplusnoise.wge(n,b0,b1=0,coef,freq,psi,phi=0,vara=1,plot=TRUE,sn=0)

Arguments

n	length of realization to be generated
b0	y intercept of the linear component
b1	slope of the linear component
coef	a 2-component vector specifying the coefficients (if only one cosine term is desired define coef[2]=0)
freq	a 2-component vector specifying the frequency components (0 to .5)
psi	a 2-component vector specifying the phase shift (0 to 2pi)
phi	a vector of coefficients of the coefficients of the AR noise
vara	vara is the variance of the noise. NOTE: a(t) is a vector of N(0,WNV) noise generated within the function (default=1)
plot	if TRUE then plot the data generated (default=TRUE)
sn	determines the seed used in the simulation (default=0 indicating new realization each time). sn=positve integer, then the same realization is generated each time

Value

x	realization generated

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

x=gen.sigplusnoise.wge(n=100,coef=c(3,1),freq=c(.1,.4),psi=c(0,0),vara=2)

tswge documentation built on Feb. 16, 2023, 6:51 p.m.