spatialfusion (version 0.6-2)

fitted: Obtain fitted values of spatial fusion model

Description

Generate fitted values of the response variables based on a spatial fusion model.

Usage

# S3 method for fusionModel
fitted(object, type = c("link", "summary", "full", "latent"), ...)

Arguments

object

object of class fusionModel. Output of fusion().

type

string. The default "link" gives the median of linear predictors; "summary" gives the mean, standard deviation and quantiles of linear predictors; "full" gives full marginals for INLA or posterior samples for Stan; "latent" gives the median of latent processes with their corresponding locations.

...

additional arguments not used.

Value

The returned value is a list containing the fitted results for each response variable.

Details

For INLA models, no posterior values for point pattern data will be generated.

See Also

fusion, fusion.dinla, fusion.dstan.

Examples

# NOT RUN {
## 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))

fit_inla <- fitted(mod_inla, type = "summary")
}
# }