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Map Posterior Predictions from a Fitted spifa Model

Source: R/plot-predict.R
plot_predict.Rd

Maps a posterior summary of each latent factor (stat: the posterior mean by default, or any other function of the draws) at the locations pred was predicted at. Draws points if those locations are points (e.g. the observed households), or a filled polygon map if they are polygons (e.g. a prediction grid).

Usage

plot_predict(
  pred,
  grid = NULL,
  select = NULL,
  stat = mean,
  boundary = NULL,
  facet_scales = "fixed",
  ncol = NULL,
  ...
)

Arguments

pred

A draws_array as returned by predict.spifa for a spatial model.

grid

An sf/sfc object of the locations pred was predicted at (points or polygons), in the same row order as pred's locations. Defaults to attr(pred, "newdata"), attached automatically by predict.spifa; only needed explicitly if that attribute is missing.

select

Factors to plot: an integer vector of factor indices, or NULL (default) for all.

stat

A function of one numeric vector (the draws at one location/factor) returning a single number – e.g. mean (the default), sd, median, or function(v) mean(v > 1) for an exceedance probability.

boundary

Optional sf/sfc polygon(s) drawn as an outline (e.g. the prediction area) on top of the map; has no effect on the computed/plotted values.

facet_scales

The scales argument of facet_wrap.

ncol

Number of columns in the facet grid; default lets facet_wrap choose.

...

Currently unused.

Value

A ggplot object with a clean map theme already applied (no axis titles, light dashed reference grid, small bottom legend). A fill (polygons) or colour (points) scale can be added as usual; a theme/theme_*() added afterwards overrides it as normal.

Details

pred is computed once via predict.spifa and can be reused across multiple plot_predict() calls – e.g. different stat/select values – without calling predict() again, which is normally the expensive step.

Author

Erick A. Chacón-Montalván

Examples

# \donttest{
library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE
data(ipixuna)

nitems <- ncol(ipixuna$items)
nfactors <- 3
A <- matrix(1, nitems, nfactors)
A[c(4, 8), 1] <- 0
A[c(2, 4, 5, 6, 7, 8, 10), 2] <- 0
A[c(5, 6), 3] <- 0

samples <- spifa(items ~ 1, data = ipixuna, nfactors = nfactors, niter = 5,
  constraints = list(discrimination = A))

# at the observed household locations (points)
pred <- predict(samples)
plot_predict(pred)


# on a grid (polygons); predict() takes the cell centroids for the
# spatial kernel but keeps the polygons for plot_predict() to map
grid <- st_sf(geometry = st_make_grid(ipixuna, n = c(3, 2)))
pred_grid <- predict(samples, newdata = grid)
plot_predict(pred_grid)

plot_predict(pred_grid, stat = sd)

plot_predict(pred_grid, stat = function (v) mean(v > 0)) # exceedance

# }