I run the example with clusterR:
no_cores <- parallel::detectCores() -1
raster::beginCluster(no_cores)
?????? res <- raster::clusterR(inp, raster::stackApply, args =
list(indices=c(2,2,3,3,1,1),fun = mean))
raster::endCluster()
And the result is:
> res
class?????????? : RasterBrick
dimensions : 180, 360, 64800, 3?? (nrow, ncol, ncell, nlayers)
resolution : 1, 1?? (x, y)
extent???????? : -180, 180, -90, 90?? (xmin, xmax, ymin, ymax)
crs?????????????? : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
source???????? : memory
names?????????? : layer.1, layer.2, layer.3
min values :???????? 1.5,???????? 3.5,???????? 5.5
max values :???????? 1.5,???????? 3.5,???????? 5.5??
layer.1, layer.2, layer.3 (?)
So what corrensponds to what?
If I run:
res2 <- stackApply(inp,c(2,2,3,3,1,1),mean)
The result is:
> res2
class : RasterBrick
dimensions : 180, 360, 64800, 3 (nrow, ncol, ncell, nlayers)
resolution : 1, 1 (x, y)
extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
source : memory
names : index_2, index_3, index_1
min values : 1.5, 3.5, 5.5
max values : 1.5, 3.5, 5.5
There is no consistency with the names of the output and obscure correspondence
with the indices in the case of clusterR
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