sits_factory_function function

Create a closure for calling functions with and without data

This function implements the factory method pattern. Its creates a generic interface to closures in R so that the functions in the sits package can be called in two different ways: 1. Called directly, passing input data and parameters. 2. Called as second-order values (parameters of another function). In the second case, the call will pass no data values and only pass the parameters for execution

The factory pattern is used in many situations in the sits package, to allow different alternatives for filtering, pattern creation, training, and cross-validation

Please see the chapter "Technical Annex" in the sits book for details.

sits_factory_function(data, fun)

Arguments

• data: Input data.
• fun: Function that performs calculation on the input data.

Returns

A closure that encapsulates the function applied to data.

Examples

# example code
if (sits_run_examples()) {
    # Include a new machine learning function (multiple linear regression)
    # function that returns mlr model based on a sits sample tibble

    sits_mlr <- function(samples = NULL, formula = sits_formula_linear(),
                         n_weights = 20000, maxit = 2000) {
        train_fun <- function(samples) {
            # Data normalization
            ml_stats <- sits_stats(samples)
            train_samples <- sits_predictors(samples)
            train_samples <- sits_pred_normalize(
                pred = train_samples,
                stats = ml_stats
            )
            formula <- formula(train_samples[, -1])
            # call method and return the trained model
            result_mlr <- nnet::multinom(
                formula = formula,
                data = train_samples,
                maxit = maxit,
                MaxNWts = n_weights,
                trace = FALSE,
                na.action = stats::na.fail
            )

            # construct model predict closure function and returns
            predict_fun <- function(values) {
                # retrieve the prediction (values and probs)
                prediction <- tibble::as_tibble(
                    stats::predict(result_mlr,
                        newdata = values,
                        type = "probs"
                    )
                )
                return(prediction)
            }
            class(predict_fun) <- c("sits_model", class(predict_fun))
            return(predict_fun)
        }
        result <- sits_factory_function(samples, train_fun)
        return(result)
    }
    # create an mlr model using a set of samples
    mlr_model <- sits_train(samples_modis_ndvi, sits_mlr)
    # classify a point
    point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")
    point_class <- sits_classify(point_ndvi, mlr_model, multicores = 1)
    plot(point_class)
}

Author(s)

Rolf Simoes, rolf.simoes@inpe.br

Gilberto Camara, gilberto.camara@inpe.br