| sits_rfor {sits} | R Documentation |
Use Random Forest algorithm to classify data. This function is a front-end to the "randomForest" package. Please refer to the documentation in that package for more details.
sits_rfor(data = NULL, num_trees = 200, nodesize = 1, ...)
data |
time series with the training samples |
num_trees |
number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times (default: 200). |
nodesize |
minimum size of terminal nodes (default 1 for classification) |
... |
other parameters to be passed to 'randomForest::randomForest' function |
model fitted to input data
(to be passed to sits_classify)
Alexandre Ywata de Carvalho, alexandre.ywata@ipea.gov.br
Rolf Simoes, rolf.simoes@inpe.br
Gilberto Camara, gilberto.camara@inpe.br
# Retrieve the set of samples for the Mato Grosso region samples_MT_ndvi <- sits_select(samples_modis_4bands, bands = "NDVI") # Build a random forest model rfor_model <- sits_train(samples_MT_ndvi, sits_rfor(num_trees = 200)) # get a point with a 16 year time series point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI") # classify the point class.tb <- sits_classify(point_ndvi, rfor_model)