fusion.dstan: Fit a spatial fusion model using Stan

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

Fit a spatial fusion model using Stan

Description

Fit a spatial fusion model using Stan 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

## S3 method for class 'dstan'
fusion(data, n.latent = 1, bans = 0, pp.offset,
    verbose = FALSE, prior.pointbeta, prior.areabeta,
    prior.tausq, prior.phi, prior.z,
    nsamples = 2000, nburnin = 1000, thinning = 1,
    nchain = 2, ncore = 2, adapt.delta = 0.95, ...)

Arguments

data	an object of class dstan. 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.
prior.pointbeta	a list with prior information for the coeffients of geostatistical model component. The default prior is list(distr = "normal",pars=c(0, 10)), i.e. a normal distribution with mean 0 and standard devation 10.
prior.areabeta	a list with prior information for the coeffients of lattice model component. The default prior is list(distr = "normal",pars=c(0, 10)), i.e. a normal distribution with mean 0 and standard devation 10.
prior.tausq	a list with prior information for the coeffients of geostatistical model component. The default prior is list(distr = "inv_gamma",pars=c(2, 1)), i.e. a inverse gamma distribution with shape 2 and rate 1.
prior.phi	a list with prior information for the spatial range parameter. NO default prior is available. We recomend using a moderately informative normal prior.
prior.z	a list with prior information for the design matrix, which also controls the partial sill. The default prior is list(distr = "normal",pars=c(1, 1)), i.e. a normal distribution with mean 1 and standard devation 1.
nsamples	a positive integer specifying the number of samples for each chain (including burn-in samples). Default 2000.
nburnin	a positive integer specifying the number of burn-in samples. Default 1000.
thinning	a positive integer specifying the thinning parameter. Default 1.
nchain	a positive integer specifying the number of chains. Default 2.
ncore	a positive integer specifying the number of cores to use when executing the chains in parallel. Default 2.
adapt.delta	a numeric value between 0 and 1 specifying the target acceptance rate. Default 0.95.
...	additional arguments passed to sampling function in rstan

Details

In the model parameterization, beta are fixed-effect coefficients, phi is the range parameter, Z_ij is the ith row and j column of the design matrix for latent processes and tau_sq is the variance parameter of a normal distribution.

NOTE: Only exponential covariance model for the latent processes is implemented. However, it can be easily extended by modifying the model code from the output.

Value

The returned value is a list consists of

model	an object of S4 class stanfit representing the fitted Stan model
data	the data structure used to fit the model

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)
# To kill parallel process except one (for stopping a stan call)
# system("killall R")

## End(Not run)

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