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The mean surface of a median map already made.

Source: R/smearing.R
smear_map.Rd

The conditional mean is exp(z) S(z) - 1 of the median z in transform space, so a mean map by any of smear()'s factors comes from the median map alone, cell by cell, without running the network again.

Usage

smear_map(
  median,
  cal,
  method = c("global", "level", "total"),
  lower_limit = 0,
  filename = ""
)

Arguments

median

The ensemble median, in native units: a SpatRaster, or the path of one – dsm_predict()'s ensemble_median band.

cal

A smearing_cal, from smearing_factor() or smearing_from_run().

method

As in smear().

lower_limit

As in smear().

filename

Where to write the map; "" keeps it in memory or in terra's temporary files.

Value

A SpatRaster: the conditional mean, cell by cell.

Examples

set.seed(1)
f <- runif(500, 2, 5)                    # predictions, in log1p space
z <- f + rnorm(500, 0, 0.4)              # what was observed, in log1p space
cal <- smearing_factor(z, f)
median_map <- terra::rast(nrows = 10, ncols = 10, vals = expm1(runif(100, 2, 5)))
smear_map(median_map, cal)
#> class       : SpatRaster
#> size        : 10, 10, 1  (nrow, ncol, nlyr)
#> resolution  : 36, 18  (x, y)
#> extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (CRS84) (OGC:CRS84)
#> source(s)   : memory
#> name        :      lyr.1
#> min value   :   7.048745
#> max value   : 151.717409