Computes nearest-available-value imputation for missing values in space

## Usage

``````bru_fill_missing(
data,
where,
values,
layer = NULL,
selector = NULL,
batch_size = deprecated()
)``````

## Arguments

data

A SpatialPointsDataFrame, SpatialPixelsDataFrame, SpatialGridDataFrame, SpatRaster, Raster, or sf object containing data to use for filling

where

A, matrix, data.frame, or SpatialPoints or SpatialPointsDataFrame, or sf object, containing the locations of the evaluated values

values

A vector of values to be filled in where `is.na(values)` is `TRUE`

layer, selector

Specifies what data column or columns from which to extract data, see `component()` for details.

batch_size

due to improved algorithm. Size of nearest-neighbour calculation blocks, to limit the memory and computational complexity.

## Value

An infilled vector of values

## Examples

``````if (FALSE) { # \dontrun{
if (bru_safe_inla()) {
points <-
sp::SpatialPointsDataFrame(
matrix(1:6, 3, 2),
data = data.frame(val = c(NA, NA, NA))
)
input_coord <- expand.grid(x = 0:7, y = 0:7)
input <-
sp::SpatialPixelsDataFrame(
input_coord,
data = data.frame(val = as.vector(input_coord\$y))
)
points\$val <- bru_fill_missing(input, points, points\$val)
print(points)

# To fill in missing values in a grid:
print(input\$val[c(3, 30)])
input\$val[c(3, 30)] <- NA # Introduce missing values
input\$val <- bru_fill_missing(input, input, input\$val)
print(input\$val[c(3, 30)])
}
} # }
``````