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# 'sim.seasonalNoise' generates a cyclic model of a poisson distribution
# as background data for a simulated timevector.
#
# Parameters:
# A - amplitude (range of sinus), default = 1
# alpha - parameter to move along the y-axis
# (negative values not allowed)
# d.h alpha > = A, default = 1,
# beta - regression coefficient, default = 0
# season - 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 given, it is weighted by the parameter K
# and influences mu
# K - weight for outbreaks
sim.seasonalNoise <- function(A = 1, alpha = 1, beta = 0, phi = 0,
length, frequency = 1, state = NULL, K = 0){
t <- 1:length
# constant factor to transform weeks to the appropriate pi-value.
omega <- 2 * pi/ 52
# season moves the sin along the x-axis.
if(is.null(state)){ # no state chain
mu <- exp(A * sin( frequency * omega * (t + phi)) + alpha + beta * t)
}
else{ # encounter the state chain
mu <- exp(A * sin( frequency * omega * (t + phi)) +
alpha + beta * t + K * state)
}
# create the noise as random numbers of the Poisson distribution
# with parameter mu
seasonalBackground <- rpois(length, mu) # get random numbers
result <- list(seasonalBackground = seasonalBackground, t = t,
mu = mu, A = A, alpha = alpha,
beta = beta, phi = phi,
length = length,
frequency = frequency, K = K)
class(result) = "seasonNoise"
return(result)
}
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