Description Usage Arguments Value Note See Also Examples
View source: R/sperrorest_resampling.R
partition_tiles
divides the study area into a specified number of
rectangular tiles. Optionally small partitions can be merged with adjacent
tiles to achieve a minimum number or percentage of samples in each tile.
1 2 3 4 5 
data 

coords 
vector of length 2 defining the variables in 
dsplit 
optional vector of length 2: equidistance of splits in
(possibly rotated) x direction ( 
nsplit 
optional vector of length 2: number of splits in
(possibly rotated) x direction ( 
rotation 
indicates whether and how the rectangular grid should
be rotated; random rotation is only between 
user_rotation 
if 
offset 
indicates whether and how the rectangular grid should be shifted by an offset. 
user_offset 
if 
reassign 
logical (default 
min_frac 
numeric >=0, <1: minimum relative size of partition as
percentage of sample; argument passed to get_small_tiles.
Will be ignored if 
min_n 
integer >=0: minimum number of samples per partition;
argument passed to get_small_tiles.
Will be ignored if 
iterate 
argument to be passed to tile_neighbors 
return_factor 
if 
repetition 
numeric vector: crossvalidation repetitions
to be generated. Note that this is not the number of repetitions,
but the indices of these repetitions. E.g., use 
seed1 

A represampling object.
Contains length(repetition)
resampling objects as
repetitions. The exact number of folds / testset tiles within each
resampling objects depends on the spatial configuration of
the data set and possible cleaning steps (see min_frac
, min_n
).
Default parameter settings may change in future releases.
This function, especially the rotation and shifting part of it and the
algorithm for cleaning up small tiles is still a bit experimental.
Use with caution.
For nonzero offsets (offset!='none')
), the number of tiles may
actually be greater than nsplit[1]*nsplit[2]
because of fractional
tiles lurking into the study region. reassign=TRUE
with suitable
thresholds is therefore recommended for nonzero (including random) offsets.
sperrorest, as.resampling.factor, get_small_tiles, tile_neighbors
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  data(ecuador)
parti < partition_tiles(ecuador, nsplit = c(4, 3), reassign = FALSE)
# plot(parti,ecuador)
summary(parti) # tile A4 has only 55 samples
# same partitioning, but now merge tiles with less than 100 samples to
# adjacent tiles:
parti2 < partition_tiles(ecuador, nsplit = c(4,3), reassign = TRUE,
min_n = 100)
# plot(parti2,ecuador)
summary(parti2)
# tile B4 (in 'parti') was smaller than A3, therefore A4 was merged with B4,
# not with A3
# now with random rotation and offset, and tiles of 2000 m length:
parti3 < partition_tiles(ecuador, dsplit = 2000, offset = 'random',
rotation = 'random', reassign = TRUE, min_n = 100)
# plot(parti3, ecuador)
summary(parti3)

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