fitted | R Documentation |
Generate fitted values of the response variables based on a spatial fusion model.
## S3 method for class 'fusionModel'
fitted(object, type = c("link", "summary", "full", "latent"), ...)
object |
object of class |
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. |
For INLA models, no posterior values for point pattern data will be generated.
The returned value is a list containing the fitted results for each response variable.
Craig Wang
fusion
, fusion.dinla
, fusion.dstan
.
## 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")
}
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