predict.ah4

fitted <code><a href="/link/hhh4?package=surveillance&version=1.7-0" rd-options="" data-mini-rdoc="surveillance::hhh4">hhh4</a></code> model object of class <code>"ah4"</code>.

object

subset of time points for which to return the
  predictions. Defaults to the subset used for fitting the model, and
  must be a subset of <code>1:nrow(object$stsObj)</code>.

newSubset

the type of prediction required. The default
  (<code>"response"</code> or, equivalently, <code>"mean"</code>) is on the 
  scale of the response variable (mean = endemic plus epidemic components).
  The alternatives are: <code>"endemic"</code>, <code>"ep</code>

type

Get fitted (component) means from a <code><a href="/link/hhh4?package=surveillance&version=1.7-0" rd-options="" data-mini-rdoc="surveillance::hhh4">hhh4</a></code> model
  (of class <code>"ah4"</code>).

models

methods

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.

predict.ah4: Predictions from a `hhh4` Model

Description

Usage

Arguments

Value