getZmat: getZmat function

In lgcp: Log-Gaussian Cox Process

getZmat function

Description

A function to construct a design matrix for use with the Bayesian MCMC routines in lgcp. See the vignette "Bayesian_lgcp" for further details on how to use this function.

Usage

getZmat(
  formula,
  data,
  regionalcovariates = NULL,
  pixelcovariates = NULL,
  cellwidth,
  ext = 2,
  inclusion = "touching",
  overl = NULL
)

Arguments

formula	a formula object of the form X ~ var1 + var2 etc. The name of the dependent variable must be "X". Only accepts 'simple' formulae, such as the example given.
data	the data to be analysed (using, for example lgcpPredictSpatialPlusPars). Either an object of class ppp, or an object of class SpatialPolygonsDataFrame
regionalcovariates	an optional SpatialPolygonsDataFrame object containing covariate information, if applicable
pixelcovariates	an optional SpatialPixelsDataFrame object containing covariate information, if applicable
cellwidth	the width of computational cells
ext	integer multiple by which grid should be extended, default is 2. Generally this will not need to be altered, but if the spatial correlation decays slowly, increasing 'ext' may be necessary.
inclusion	criterion for cells being included into observation window. Either 'touching' or 'centroid'. The former, the default, includes all cells that touch the observation window, the latter includes all cells whose centroids are inside the observation window.
overl	an object of class "lgcppolyol", created by the function getpolyol. Such an object contains the FFT grid and a polygon/polygon overlay and speeds up computation massively.

Details

For example, a spatial LGCP model for the would have the form:

X(s) ~ Poisson[R(s)]

R(s) = C_A lambda(s) exp[Z(s)beta+Y(s)]

The function getZmat helps create the matrix Z. The returned object is passed onto an MCMC function, for example lgcpPredictSpatialPlusPars or lgcpPredictAggregateSpatialPlusPars. This function can also be used to help construct Z for use with lgcpPredictSpatioTemporalPlusPars and lgcpPredictMultitypeSpatialPlusPars, but these functions require a list of such objects: see the vignette "Bayesian_lgcp" for examples.

Value

a design matrix for passing on to the Bayesian MCMC functions

See Also

chooseCellwidth, getpolyol, guessinterp, addTemporalCovariates, lgcpPrior, lgcpInits, CovFunction lgcpPredictSpatialPlusPars, lgcpPredictAggregateSpatialPlusPars, lgcpPredictSpatioTemporalPlusPars, lgcpPredictMultitypeSpatialPlusPars

lgcp documentation built on Oct. 3, 2023, 5:08 p.m.