R: Train a SITS classifiction model using random forest...

sits_rfor {sits} | R Documentation

## Train a SITS classifiction model using random forest algorithm

### Description

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.

### Usage

    sits_rfor(data = NULL, num_trees = 2000, nodesize = 1, ...)

### Arguments

`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: 2000).
`nodesize` | minimum size of terminal nodes (default 1 for classification)
`...` | other parameters to be passed to 'randomForest::randomForest' function

### 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

    # 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 = 300))
    # 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)

[Package sits version 0.12.0 Index]