sits (version 0.13.0)

sits_qda: Train a classification model using quadratic 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 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.

Usage

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

Arguments

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.

Value

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

Examples

# NOT RUN {
# 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)

# }