fusionData: Prepare data structure for spatial fusion modelling

In spatialfusion: Multivariate Analysis of Spatial Data Using a Unifying Spatial Fusion Framework

Prepare data structure for spatial fusion modelling

Description

Takes various datasets and formulas from different spatial data types and process them to prepare for spatial fusion modeling using either Stan or INLA.

Usage

fusionData(geo.data, geo.formula,
           lattice.data, lattice.formula,
           pp.data, distributions, domain = NULL,
           method = c("Stan", "INLA"),
           proj4string = CRS(as.character(NA)),
           stan.control = NULL)

Arguments

geo.data	an object of class data.frame or SpatialPointsDataFrame. If data.frame, it must have column names "x" and "y" as coordinates of observations.
geo.formula	an object of class formula. A symbolic description of the model to be fitted for geostatistical data. For multivariate geostatistical data, use syntax cbind(y1, y2) followed by ~.
lattice.data	an object of class SpatialPolygonsDataFrame. Contains lattice data.
lattice.formula	an object of class formula. A symbolic description of the model to be fitted for lattice data. For multivariate lattice data, use syntax cbind(y1, y2) followed by ~.
pp.data	an object of class data.frame, SpatialPoints or SpatialPointsDataFrame, or a list of them. If data.frame, it must have column names "x" and "y" as coordinates.
distributions	a vector of strings. Specifying the distributions of each geostatistical and lattice response variable, currently “Gaussian” or “normal”, “Poisson” (count) and “Bernoulli” (binary) are supported. Note: no distribution is required to be specified for point pattern data.
domain	an object of class SpatialPolygons. The spatial domain considered for computing gridded point pattern data. If NULL, a bounding box that contains all spatial units is used.
method	character. Either 'Stan' or 'INLA', the method to be used for fitting the spatial fusion model later.
proj4string	projection string of class CRS-class.
stan.control	a named list of parameters to control the Stan implementation of spatial fusion models. Default to NULL such that all the default values are used. n.neighbor (positive integer) Number of nearest neighbors to consider. Default to 5. n.sampling (positive integer) Number of sampling points for each area. Default to 5. n.grid (positive integer) Number of grid used to divide the spatial domain in each of x- and y-direction to count the number of cases/events in each grid. Default to 10.

Details

It is not possible to add covariate for point pattern data in the spatial fusion framework. However, an offset term can be supplied to pp.offset in the modelling stage with fusion. Any covariate information can be taken into account by firstly fit a fixed effect model and enter the fitted values into the offset term.

Value

The returned value is an object of either class dstan or dinla, depending on the chosen method. They are both lists that contain:

distributions	distribution specified each response variable.
n_point	sample size for geostatistical data.
n_area	sample size for lattice data.
n_grid	Set to 1 for INLA, set to the number of grids for Stan.
p_point	number of coefficients for geostatistical model component (only if there is geostatistical data).
n_point_var, n_area_var, n_pp_var	number of response variables for each data type.
Y_point	response variable for geostatistical data (only if there is geostatistical data).
X_point	covariates for geostatistical data (only if there is geostatistical data).
p_area	number of coefficients for lattice model component (only if there is lattice data).
Y_area	response variable for lattice data (only if there is lattice data).
X_area	covariates for lattice data (only if there is lattice data).
geo.formula, lattice.formula	formulas used for geostatistical and lattice data.

dstan additionally contains:

n_neighbor	number of nearest neighbors to consider for NNGP modelling.
n_sample	total number of sampling points.
nearid, nearind_sample	vectors containing neighborhood indices
C_nei, C_site_nei, sC_nei, sC_site_nei	various distance matrices
A1	aggregation matrix that maps sampling points to areal averages (only if there is lattice data).
Y_pp	the number of cases/events in each grid for point pattern data (only if there is point pattern data).
area	the area of each grid (only if there is point pattern data).
grd_lrg	the grid generated for point pattern data modeling (only if there is point pattern data).
locs	all the locations where the latent components are modelled.

dinla additionally contains:

domain	spatial domain as a SpatialPolygons-class
locs_point	locations of geostatistical data.
locs_pp	locations of point pattern data.
poly	lattice data as a SpatialPolygonsDataFrame-class.

Author(s)

Craig Wang

See Also

fusion.dinla, fusion.dstan

Examples

## example based on simulated built-in data

dat <- fusionData(dataGeo, lungfunction ~ covariate,
           dataLattice, mortality ~ covariate,
           dataPP, distribution = c("normal","poisson"),
           domain = dataDomain,
           method = "INLA")
## Not run: 
if (require("INLA", quietly = TRUE)) {
## fit a spatial fusion model on the prepared data
## pp.offset = 400 was chosen based on simulation parameters
mod <- fusion(data = dat, n.latent = 1, bans = 0, pp.offset = 400,
           prior.range = c(0.1, 0.5), prior.sigma = c(1, 0.5),
           mesh.locs = dat$locs_point, mesh.max.edge = c(0.5, 1))

## parameter estimates
summary(mod)
}

## End(Not run)

spatialfusion documentation built on Aug. 23, 2022, 1:05 a.m.