find.kh

average run length in acceptance setting, aka. in control state. Specifies the number of observations before false alarm.

ARLa

average run length in rejection state, aka. out of control state. Specifies the number of observations before an increase is detected (i.e. detection delay)

ARLr

sided

Which method to use in the function <code><a href="/link/optim?package=surveillance&version=1.4-1" rd-options="" data-mini-rdoc="surveillance::optim">optim</a></code>. Standard choice is BFGS, but in some situation Nelder-Mead can be advantageous.

method

gives extra information about the root finding process

verbose

Given a specification of the average run length in the (a)cceptance
        and (r)ejected setting determine the k and h values in a standard
        normal setting.

models

A package implementing statistical methods for the
        modeling and change-point detection in time series of counts,
        proportions and categorical data, as well as for the modeling
        of continuous-time epidemic phenomena, e.g. discrete-space
        setups such as the spatially enriched
        Susceptible-Exposed-Infectious-Recovered (SEIR) models for
        surveillance data, or continuous-space point process data such
        as the occurrence of disease or earthquakes. Main focus is on
        outbreak detection in count data time series originating from
        public health surveillance of infectious diseases, but
        applications could just as well originate from environmetrics,
        reliability engineering, econometrics or social sciences.
        Currently the package contains implementations of typical
        outbreak detection procedures such as Stroup et. al (1989),
        Farrington et al (1996), Rossi et al (1999), Rogerson and
        Yamada (2001), a Bayesian approach, negative binomial CUSUM
        methods and a detector based on generalized likelihood ratios.
        Furthermore, inference methods for the retrospective infectious
        disease model in Held et al (2005), Held et al (2006), Paul et
        al (2008) and Paul and Held (2011) are provided. A novel CUSUM
        approach combining logistic and multinomial logistic modelling
        is also included.  Continuous self-exciting spatio-temporal
        point processes are modeled through additive-multiplicative
        conditional intensities as described in H�hle (2009)
        ("twinSIR", discrete space) and Meyer et al (2012) ("twinstim",
        continuous space).  The package contains several real-world
        datasets, the ability to simulate outbreak data, visualize the
        results of the monitoring in temporal, spatial or
        spatio-temporal fashion.

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find.kh: Determine the k and h values in a standard normal setting

Description

Usage

Arguments

Value

Details

Examples