| sits_qda {sits} | R Documentation |
This function receives a tibble with a set of attributes X for each observation Y. These attributes are the values of the time series for each band. The function performs a quadratic discriminant analysis (qda) to obtain a predictive model. This function is a front-end to the "qda" method in the "MASS" package. Please refer to the documentation in that package for more details.
sits_qda(data = NULL, formula = sits_formula_logref(), ...)
data |
Time series with the training samples. |
formula |
Symbolic description of the model to be fit. (default: sits_formula_logref). |
... |
Other parameters to be passed to MASS::qda 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 Mato Grosso region (provided by EMBRAPA) # Select the NDVI band samples_mt_ndvi <- sits_select(samples_modis_4bands, bands = "NDVI") # Train a QDA model qda_model <- sits_train(samples_mt_ndvi, sits_qda()) # Classify a point point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI") class <- sits_classify(point_ndvi, qda_model)