simHReg: Simulate harmonic regression models
In ptest: Periodicity Tests in Short Time Series

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

Simulates a harmonic regression. Possible types of models are normal, t(5), Laplace, cubic and AR1.

1 2	simHReg(n, f, A, B, model = c("Gaussian", "t5", "Laplace", "cubic", "AR1"), phi = 0, sig = 1)

`n`	Length of series.
`f`	Frequency.
`A`	Cosine amplitude.
`B`	Sine amplitude.
`model`	The model used for generating the error term. See details.
`phi`	Only used if AR1 error distribution is selected.
`sig`	The standard error of the series.

Generate a harmonic series y with length n, where y_t = A*cos(2*pi*f*t)+B*sin(2*pi*f*t)+sig*e_t,\ t=1,...,n, and e comes from one of the following specified distributions with mean 0 and standard error 1:

Gaussian: A standard normal distribution (i.i.d.).

t5: A t distribution with 5 degrees of freedom (i.i.d., standardized to mean 0 and variance 1).

Laplace: A Laplace (double exponential) distribution (i.i.d., standardized to mean 0 and variance 1).

cubic: A standard normal distribution for e, but y=y^3 this time.

AR1: An AR(1) series with autocorrelation paramater phi (standardized to mean 0 and variance 1).

Vector of length n, simulated harmonic series.

A.I. McLeod and Yuanhao Lai

McLeod, A.I., Yu, Hao and Krougly, Z. (2007), Algorithms for Linear Time Series Analysis: With R Package, Journal of Statistical Software 23, 5 1-26.

fitHReg, ptestReg

#Simulate the harmonic regression model with standard Gaussian error terms
z <- simHReg(10, f=2/10, 1, 2, model="Gaussian",sig=1) #Fourier Frequency
plot(1:10,z,type="b")

#Simulate the AR(1) errors
z <- simHReg(10, f=0/10, 0,0, model="AR1",phi=0.2,sig=1) 
acf(z)