fitted: Obtain fitted values of spatial fusion model

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

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 class '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.

Details

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

Value

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

Author(s)

Craig Wang

See Also

fusion, fusion.dinla, fusion.dstan.

Examples

## example based on simulated data

if (requireNamespace("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 <-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),
                inla.args = list(num.threads = "2:1"))

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

spatialfusion documentation built on June 22, 2025, 5:07 p.m.