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 * omega * (t + phi)) + alpha + beta * t + K * state)
sim.seasonalNoise(A = 1, alpha = 1, beta = 0, phi = 0,
length, frequency = 1, state = NULL, K = 0)A |
amplitude (range of sinus), default = 1. |
alpha |
parameter to move along the y-axis (negative values not allowed) with alpha > = A, default = 1. |
beta |
regression coefficient, default = 0. |
phi |
factor to create seasonal moves (moves the curve along the x-axis), default = 0. |
length |
number of weeks to model. |
frequency |
factor to determine the oscillation-frequency, default = 1. |
state |
if a state chain is entered the outbreaks will be additional weighted by K. |
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.
M. Höhle, A. Riebler, C. Lang
season <- sim.seasonalNoise(length = 300) plot(season$seasonalBackground,type = "l") # use a negative timetrend beta season <- sim.seasonalNoise(beta = -0.003, length = 300) plot(season$seasonalBackground,type = "l")
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