plot: Generate diagnostics plot for a fusion model

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

Generate diagnostics plot for a fusion model

Description

Plot model diagnostics for fusionModel objects. By default, it shows posterior versus prior distributions of fixed effect coefficients and latent parameters. The names of fixed effect coefficients are covariate names followed by internal parameter names in parentheses. 'beta_p' denotes the coefficients for point data and 'beta_a' denotes the coefficients for lattice data.

Usage

## S3 method for class 'fusionModel'
plot(x, posterior = TRUE, interactive = TRUE, ...)

Arguments

x	object of class fusionModel. Output of fusion().
posterior	logical. If TRUE, then shows posterior versus prior distributions of fixed effect coefficients and latent parameters.
interactive	logical. If TRUE, then print messages in the terminal to proceed to next plots.
...	additional arguments not used

Details

When posterior = FALSE, then traceplot of posterior samples for the fixed effect coefficients and latent parameters are shown for Stan approach and the mesh overlayed with spatial data is shown for INLA approach.

Author(s)

Craig Wang

Examples

## example based on simulated data
## Not run: 
if (require("INLA", quietly = TRUE)) {
dat <- fusionSimulate(n.point = 20, n.area = 10, n.grid = 2,
               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(1,1,1)))

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_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")

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))

plot(mod_inla, interactive = FALSE)
}

## End(Not run)

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