map function

Map Relevant Quantities from a Small Area Model

The map() function enables to plot maps containing relevant model outputs by accounting for their geographical dimension. The shapefile of the area must be provided via a SpatialPolygonsDataFrame or sf object.

map(
  x,
  spatial_df,
  spatial_id_domains,
  match_names = NULL,
  color_palette = c("snow2", "deepskyblue4"),
  quantity = c("HB_est", "Direct_est", "SD"),
  time = NULL,
  style = "quantile",
  ...
)

Arguments

• x: An object of class summary_fitsae or benchmark_fitsae.
• spatial_df: A object of class SpatialPolygonsDataFrame (spatial polygons object) from sp package or sf from the sf package, accounting for the geographical dimension of the domains.
• spatial_id_domains: A character string indicating the name of spatial_df variable containing area denominations, in order to correctly match the areas.
• match_names: An encoding two-columns data.frame: the first with the original data coding (domains) and the second one with corresponding spatial_df object labels. This argument has to be specified only if spatial_df object labels do not match the ones provided through the original dataset.
• color_palette: A vector with two color strings denoting the extreme bounds of colors range to be used.
• quantity: A string indicating the quantity to be mapped. When a summary_fitsae is given as input, it can be selected among "HB_est" (model-based estimates), "SD"(posterior standard deviations) and "Direct_est"(direct estimates). While when a benchmark_fitsae class object is given as input, this argument turns automatically to "Bench_est", displaying the benchmarked estimates.
• time: A string indicating the year of interest for the quantities to be treated, in case of temporal or spatio-temporal objects.
• style: Method to process the color scale, see tmap documentation.
• ...: Arguments passed to tm_fill (e.g. n, breaks).

Returns

Atmap object.

Examples

## Not run:

library(tipsae)

# loading toy dataset
data("emilia_cs")

# fitting a model
fit_beta <- fit_sae(formula_fixed = hcr ~ x, data = emilia_cs, domains = "id",
                    type_disp = "var", disp_direct = "vars", domain_size = "n",
                    # MCMC setting to obtain a fast example. Remove next line for reliable results.
                    chains = 1, iter = 150, seed = 0)

# check model diagnostics
summ_beta <- summary(fit_beta)

# load shapefile of concerned areas
data("emilia_shp")

# plot the map using model diagnostics and areas shapefile
map(x = summ_beta,
   spatial_df = emilia_shp,
   spatial_id_domains = "NAME_DISTRICT")
## End(Not run)

See Also

summary.fitsae to produce the input object and SpatialPolygonsDataFrame to manage the shapefile.