Generation of Background Noise for Simulated Timeseries
Generation of a cyclic model of a Poisson distribution as background data for a simulated timevector.
The mean of the Poisson distribution is modelled as: $$\mu = \exp(A \sin( frequency \cdot \omega \cdot (t + \phi)) + \alpha + \beta * t + K * state)$$
sim.seasonalNoise(A = 1, alpha = 1, beta = 0, phi = 0, length, frequency = 1, state = NULL, K = 0)
amplitude (range of sinus), default = 1.
parameter to move along the y-axis (negative values not allowed) with alpha > = A, default = 1.
regression coefficient, default = 0.
factor to create seasonal moves (moves the curve along the x-axis), default = 0.
number of weeks to model.
factor to determine the oscillation-frequency, default = 1.
if a state chain is entered the outbreaks will be additional weighted by K.
additional weigth for an outbreak which influences the distribution parameter mu, default = 0.
an object of class
seasonNoise which includes the modelled
timevector, the parameter
mu and all input parameters.