sim.seasonalNoise: Generation of Background Noise for Simulated Timeseries

In surveillance: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

Generation of Background Noise for Simulated Timeseries

Description

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)

Usage

sim.seasonalNoise(A = 1, alpha = 1, beta = 0, phi = 0,
                  length, frequency = 1, state = NULL, K = 0)

Arguments

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 weight for an outbreak which influences the distribution parameter mu, default = 0.

Value

an object of class seasonNoise which includes the modelled timevector, the parameter mu and all input parameters.

Author(s)

M. Höhle, A. Riebler, C. Lang

See Also

sim.pointSource

Examples

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")

surveillance documentation built on Nov. 4, 2024, 3 a.m.