Build Inputs for Prediction at New GP Coordinates

Creates the augmented distance matrices needed for Gaussian-process conditioning and indicator matrices mapping prediction observations to unique new spatial and temporal levels. The first observed level in each dimension is the baseline omitted by ZINB_GP() and is therefore excluded from the augmented distance matrix.

Usage

make_prediction_inputs(
  coords = NULL,
  time_coords = NULL,
  coords_new = NULL,
  time_coords_new = NULL
)

Arguments

coords

Observed spatial coordinate matrix, including the baseline as its first row. Use NULL for a temporal-only model.

time_coords

Observed temporal coordinate matrix, including the baseline as its first row. A numeric vector is treated as a one-column matrix. Use NULL for a spatial-only model.

coords_new

Spatial coordinates for the prediction observations, with one row per row of the new fixed-effect design matrix. Repeated rows share one predicted spatial random effect.

time_coords_new

Temporal coordinates for the prediction observations, with one row per row of the new fixed-effect design matrix. A numeric vector is treated as a one-column matrix. Repeated rows share one predicted temporal random effect.

Value

A list containing Ds_new, Dt_new, Vs_new, and Vt_new. Each non-NULL distance matrix covers the observed nonbaseline levels followed by the unique new levels. Each design matrix has one row per prediction observation and one column per unique new level.

Examples

observed_space <- rbind(c(0, 0), c(1000, 0), c(0, 1000))
observed_time <- c(0, 100)
new_space <- rbind(c(500, 500), c(500, 500), c(750, 250))
new_time <- c(150, 200, 150)
prediction_inputs <- make_prediction_inputs(
  coords = observed_space,
  time_coords = observed_time,
  coords_new = new_space,
  time_coords_new = new_time
)
prediction_inputs$Vs_new
#>      [,1] [,2]
#> [1,]    1    0
#> [2,]    1    0
#> [3,]    0    1
prediction_inputs$Vt_new
#>      [,1] [,2]
#> [1,]    1    0
#> [2,]    0    1
#> [3,]    1    0