| plot.variance_cube | R Documentation |
Plots a variance cube, useful to understand how local smoothing will work.
## S3 method for class 'variance_cube'
plot(
x,
...,
tile = x[["tile"]][[1L]],
roi = NULL,
labels = NULL,
palette = "YlGnBu",
rev = FALSE,
type = "map",
quantile = 0.75,
scale = 1,
max_cog_size = 1024L,
legend_position = "inside",
legend_title = "logvar"
)
x |
Object of class "variance_cube". |
... |
Further specifications for plot. |
tile |
Tile to be plotted. |
roi |
Spatial extent to plot (see notes) |
labels |
Labels to plot. |
palette |
RColorBrewer or "cols4all" palette |
rev |
Reverse order of colors in palette? |
type |
Type of plot ("map" or "hist") |
quantile |
Minimum quantile to plot |
scale |
Scale to plot map (0.4 to 1.0) |
max_cog_size |
Maximum size of COG overviews (lines or columns) |
legend_position |
Where to place the legend (default = "inside") |
legend_title |
Title of legend (default = "probs") |
A plot containing local variances associated to the logit probability for each pixel and each class.
To see which color palettes are supported, please run cols4all::c4a_gui().
To define a roi use one of:
A path to a shapefile with polygons;
A sfc or sf object from sf package;
A SpatExtent object from terra package;
A named vector ("lon_min",
"lat_min", "lon_max", "lat_max") in WGS84;
A named vector ("xmin", "xmax",
"ymin", "ymax") with XY coordinates.
Gilberto Camara, gilberto.camara@inpe.br
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()
)
# obtain a variance cube
var_cube <- sits_variance(probs_cube, output_dir = tempdir())
# plot the variance cube
plot(var_cube)
}
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