spatialfusion (version 0.6-6)

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"), ...)

Value

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

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.

Author

Craig Wang

Details

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

See Also

fusion, fusion.dinla, fusion.dstan.

Examples

## example based on simulated data
if (FALSE) {
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")
}
}