R: Train a sits classification model using linear discriminant...

sits_lda {sits} | R Documentation

## Train a sits classification model using linear discriminant analysis

### Description

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 method performs a linear discriminant analysis (lda) to obtain a predictive model. This function is a front-end to the "lda" method in the "MASS" package. Please refer to the documentation in that package for more details.

### Usage

    sits_lda(data = NULL, formula = sits_formula_logref(), ...)

### Arguments

`data` | Time series with the training samples.
`formula` | A symbolic description of the model to be fit. (default: sits_formula_logref).
`...` | Other parameters to be passed to MASS::lda 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

    ## 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_lda())

    # 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 0.12.0 Index]