R: Train a sits classification model using multinomial...

sits_mlr {sits} | R Documentation

## Train a sits classification model using multinomial log-linear

### Description

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.

### Usage

    sits_mlr(
      data = NULL,
      formula = sits_formula_linear(),
      n_weights = 20000,
      maxit = 2000,
      ...
    )

### Arguments

`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.

### Value

Model fitted to input data (to be passed to `sits_classify`)

### Author(s)

Alexandre Ywata de Carvalho, alexandre.ywata@ipea.gov.br

Rolf Simoes, rolf.simoes@inpe.br

Gilberto Camara, gilberto.camara@inpe.br

### Examples

    ## 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)

[Package sits version 0.15.0 Index]