R/bm_SampleFactorLevels.R
bm_SampleFactorLevels.RdThis internal biomod2 function allows the user to sample all levels of all
the factorial variables contained in a data.frame or SpatRaster
object.
bm_SampleFactorLevels(expl.var, mask.out = NULL, mask.in = NULL)a data.frame or SpatRaster
object containing the explanatory variables (in columns or layers)
a data.frame or SpatRaster
object containing the area that has already been sampled (factor
levels within this mask will not be sampled)
a data.frame or SpatRaster
object containing areas where factor levels are to be sampled in priority.
Note that if after having explored these masks, some factor levels
remain unsampled, they will be sampled in the reference input object expl.var.
A vector of numeric values corresponding to either row (data.frame) or
cell (SpatRaster) numbers, each refering to a single level of a
single factorial variable.
In case no factorial variable is found in the input object, NULL is returned.
The expl.var, mask.out and mask.in parameters must be coherent in terms of
dimensions :
same number of rows for data.frame objects
same resolution, projection system and number of cells for SpatRaster objects
If mask.in contains several columns (data.frame) or layers
(SpatRaster), then their order matters :
they will be considered successively to sample missing factor levels.
Values in data.frame will be understood as :
FALSE : out of mask
TRUE : in mask
Values in SpatRaster will be understood as :
NA : out of mask
not NA : in mask
bm_PseudoAbsences, bm_CrossValidation
Other Secundary functions:
bm_BinaryTransformation(),
bm_CrossValidation(),
bm_FindOptimStat(),
bm_MakeFormula(),
bm_ModelingOptions(),
bm_PlotEvalBoxplot(),
bm_PlotEvalMean(),
bm_PlotRangeSize(),
bm_PlotResponseCurves(),
bm_PlotVarImpBoxplot(),
bm_PseudoAbsences(),
bm_RunModelsLoop(),
bm_SRE(),
bm_SampleBinaryVector(),
bm_Tuning(),
bm_VariablesImportance()
library(terra)
## Create raster data
ras.1 <- ras.2 <- mask.out <- rast(nrows = 10, ncols = 10)
ras.1[] <- as.factor(rep(c(1, 2, 3, 4, 5), each = 20))
ras.1 <- as.factor(ras.1)
ras.2[] <- rnorm(100)
stk <- c(ras.1, ras.2)
names(stk) <- c("varFact", "varNorm")
## define a mask for already sampled points
mask.out[1:40] <- 1
## define a list of masks where we want to sample in priority
mask.in <- list(ras.1, ras.1)
mask.in[[1]][1:80] <- NA ## only level 5 should be sampled in this mask
mask.in[[1]][21:80] <- NA ## only levels 1 and 5 should be sampled in this mask
## Sample all factor levels
samp1 <- bm_SampleFactorLevels(expl.var = stk, mask.out = mask.out)
samp2 <- bm_SampleFactorLevels(expl.var = stk, mask.in = mask.in)
samp3 <- bm_SampleFactorLevels(expl.var = stk, mask.out = mask.out, mask.in = mask.in)