fusion: Fit a spatial fusion model

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

Fit a spatial fusion model

Description

Fit a spatial fusion model based on the unifying framework proposed by Wang and Furrer (2021). One or more latent Gaussian process(es) is assumed to be associated with the spatial response variables.

Usage

fusion(data, n.latent = 1, bans = 0, pp.offset,
       verbose = FALSE, ...)

Arguments

data	an object of class either dstan or dinla. Output of fusionData().
n.latent	integer. Number of latent processes to be modeled.
bans	either 0 or a matrix of 0s and 1s with dimension J times n.latent, where J is the total number of response variables. If matrix, 1 indicates banning an association between the latent process and response variable. If 0, no association is banned.
pp.offset	numeric, vector of numeric or matrix of numeric. Offset term for point pattern data.
verbose	logical. If TRUE, prints progress and debugging information.
...	additional arguments depending on the class of data

Details

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

Value

The returned value is a named list of class fusionModel consisting of model output and data structure used. If the model is fitted with INLA, the mesh used is also included.

Author(s)

Craig Wang

References

Wang, C., Furrer, R. and for the SNC Study Group (2021). Combining heterogeneous spatial datasets with process-based spatial fusion models: A unifying framework, Computational Statistics & Data Analysis

See Also

fusion.dinla, fusion.dstan, fusionData for preparing data, fitted.fusionModel for extracting fitted values, predict.fusionModel for prediction.

Examples

## example based on simulated data
## Not run: 
dat <- fusionSimulate(n.point = 20, n.area = 10,
          psill = 1, phi = 1, nugget = 0, tau.sq = 0.5,
          point.beta = list(rbind(1,5)),
          area.beta = list(rbind(-1, 0.5)),
          distributions = c("normal","poisson"),
          design.mat = matrix(c(1,1)))

geo_data <- data.frame(x = dat$mrf[dat$sample.ind, "x"],
                y = dat$mrf[dat$sample.ind, "y"],
                cov.point = dat$data$X_point[,2],
                outcome = dat$data$Y_point[[1]])
lattice_data <- sp::SpatialPolygonsDataFrame(dat$poly,
                    data.frame(outcome = dat$data$Y_area[[1]],
                    cov.area = dat$data$X_area[,2]))

dat_stan <- fusionData(geo.data = geo_data, geo.formula = outcome ~ cov.point,
                lattice.data = lattice_data, lattice.formula = outcome ~ cov.area,
                distributions = c("normal","poisson"),
                method = "Stan")

## S3 method for class 'dstan'
mod_stan <- fusion(data = dat_stan, n.latent = 1, bans = 0,
                prior.phi = list(distr = "normal", pars = c(1, 10)))
summary(mod_stan)

## End(Not run)
## Not run: 
if (require("INLA", quietly = TRUE)) {
dat_inla <- fusionData(geo.data = geo_data, geo.formula = outcome ~ cov.point,
                lattice.data = lattice_data, lattice.formula = outcome ~ cov.area,
                pp.data = dat$data$lgcp.coords[[1]], distributions = c("normal","poisson"),
                method = "INLA")

## S3 method for class 'dinla'
mod_inla <- fusion(data = dat_inla, n.latent = 1, bans = 0,
                prior.range = c(1, 0.5), prior.sigma = c(1, 0.5),
                mesh.locs = dat_inla$locs_point, mesh.max.edge = c(0.5, 1))
summary(mod_inla)
}

## End(Not run)

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