Takes a probability cube and estimate the local variance of the logit of the probability, to support the choice of parameters for Bayesian smoothing.
Usage
sits_variance(cube, ...)
# S3 method for class 'probs_cube'
sits_variance(
cube,
...,
window_size = 9L,
neigh_fraction = 0.5,
memsize = 4L,
multicores = 2L,
output_dir,
version = "v1",
progress = TRUE
)
# S3 method for class 'raster_cube'
sits_variance(cube, ...)
# S3 method for class 'derived_cube'
sits_variance(cube, ...)
# Default S3 method
sits_variance(cube, ...)Arguments
- cube
Probability data cube (class "probs_cube")
- ...
Parameters for specific functions
- window_size
Size of the neighborhood (odd integer)
- neigh_fraction
Fraction of neighbors with highest probability for Bayesian inference (numeric from 0.0 to 1.0)
- memsize
Maximum overall memory (in GB) to run the smoothing (integer, min = 1, max = 16384)
- multicores
Number of cores to run the smoothing function (integer, min = 1, max = 2048)
- output_dir
Output directory for image files (character vector of length 1)
- version
Version of resulting image (character vector of length 1)
- progress
Check progress bar?
Examples
if (sits_run_examples()) {
# create a random forest model
rfor_model <- sits_train(samples_modis_ndvi, sits_rfor())
# create a data cube from local files
data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
cube <- sits_cube(
source = "BDC",
collection = "MOD13Q1-6.1",
data_dir = data_dir
)
# classify a data cube
probs_cube <- sits_classify(
data = cube, ml_model = rfor_model, output_dir = tempdir()
)
# plot the probability cube
plot(probs_cube)
# smooth the probability cube using Bayesian statistics
var_cube <- sits_variance(probs_cube, output_dir = tempdir())
# plot the variance cube
plot(var_cube)
}