spatialfusion (version 0.7-2)

plot: 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 fusionModel
plot(x, posterior = TRUE, interactive = TRUE, ...)

Value

No return value, called for side effects

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

Author

Craig Wang

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.

Examples

## example based on simulated data
# \donttest{
if (requireNamespace("INLA", quietly = TRUE)) {
Sys.setenv("OMP_THREAD_LIMIT" = 2)
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 <- cbind(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)
}
# }