scale.gpc.poly

an object of class <code>"gpc.poly"</code>.

numeric vector of length 2 (x,y), which will be subtracted
    from the respective coordinates of <code>x</code>.

center

numeric vector of length 2 (x,y), which serves as the
    divisor for the respective coordinates of <code>x</code>.

scale

This is a re-implementation of the corresponding method from package
<pkg>gpclib</pkg> to also allow centering.

manip

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 Farrington et al (1996),
        Noufaily et al (2012) or the negative binomial LR-CUSUM method
        described in Hoehle and Paul (2008). 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 data sets, the ability
        to simulate outbreak data, visualize the results of the
        monitoring in temporal, spatial or spatio-temporal fashion.
	Note: Using the 'boda' algorithm requires the the INLA
        package, which should be installed automatically through the
        specified Additional_repositories, if uninstalled dependencies
        are also requested. As this might not work under Mac OS X it
        might be necessary to manually install the INLA package as
        specified at http://www.r-inla.org/download.

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scale.gpc.poly: Centering and Scaling a `"gpc.poly"` Polygon

Description

Usage

Arguments

Value