summary | R Documentation |
Generate summary statistics for posterior parameter estimates from a spatial fusion model.
## S3 method for class 'fusionModel' summary(object, digits = 3, ...)
object |
object of class |
digits |
integer. The number of significant digits. |
... |
additional arguments not used. |
The returned value is a matrix containing the parameter estimates and their summary statistics. 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.
Craig Wang
## 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)) summary(mod_inla) } ## End(Not run)
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