Implementation of the CUSUM algorithm retrieved from the surveillance package and adapted so that after a signal was triggered the cusum is set to 0 Parameters are inherited from surveillance algo.cusum

algo.cusum_with_reset(
  disProgObj,
  control = list(range = range, k = 1.04, h = 2.26, m = NULL, trans = "standard", alpha =
    NULL)
)

Arguments

disProgObj

object of class disProg (including the observed and the state chain)

control

control object:

range

determines the desired time points which should be evaluated

k

is the reference value

h

the decision boundary

m

how to determine the expected number of cases -- the following arguments are possible

numeric: a vector of values having the same length as range. If a single numeric value is specified then this value is replicated length(range) times.
NULL: A single value is estimated by taking the mean of all observations previous to the first range value.
"glm": A GLM of the form $$\log(m_t) = \alpha + \beta t + \sum_{s=1}^S (\gamma_s \sin(\omega_s t) + \delta_s \cos(\omega_s t)),$$ where $\omega_s = \frac{2\pi}{52}s$ are the Fourier frequencies is fitted. Then this model is used to predict the range values.

trans

one of the following transformations (warning: Anscombe and NegBin transformations are experimental)

rossi: standardized variables z3 as proposed by Rossi
standard: standardized variables z1 (based on asymptotic normality) - This is the default.
anscombe: anscombe residuals -- experimental
anscombe2nd: anscombe residuals as in Pierce and Schafer (1986) based on 2nd order approximation of E(X) -- experimental
pearsonNegBin: compute Pearson residuals for NegBin -- experimental
anscombeNegBin: anscombe residuals for NegBin -- experimental
none: no transformation

alpha

parameter of the negative binomial distribution, s.t. the variance is $m+\alpha *m^2$