| sits_mlr {sits} | R Documentation |
Use multinomial log-linear (mlr) fitting model to classify data. 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. This function is a front-end to the "multinom" method in the "nnet" package. Please refer to the documentation in that package for more details.
sits_mlr( data = NULL, formula = sits_formula_linear(), n_weights = 20000, maxit = 2000, ... )
data |
Time series with the training samples. |
formula |
Symbolic description of the model to be fit. (default: sits_formula_logref). |
n_weights |
Maximum number of weights (should be proportional to size of input data). |
maxit |
Maximum number of iterations (default 300). |
... |
Other parameters to be passed to nnet::multinom. |
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
## Not run:
# Retrieve the set of samples for Mato Grosso region (provided by EMBRAPA)
samples_2bands <- sits_select(samples_modis_4bands, bands = c("NDVI", "EVI"))
# Build a machine learning model
ml_model <- sits_train(samples_2bands, sits_mlr())
# get a point and classify the point with the ml_model
point.tb <- sits_select(point_mt_6bands, bands = c("NDVI", "EVI"))
class.tb <- sits_classify(point.tb, ml_model)
plot(class.tb, bands = c("NDVI", "EVI"))
## End(Not run)