R: A function that controls some few aspects of the memory-based...

mbl_control {resemble}

R Documentation

A function that controls some few aspects of the memory-based learning process in the `mbl` function

Description

This function is used to further control some aspects of the memory-based learning process in the mbl function.

Usage

mbl_control(return_dissimilarity = FALSE,
            validation_type = c("NNv", "local_cv"),
            tune_locally = TRUE,
            number = 10,
            p = 0.75,
            range_prediction_limits = TRUE,
            progress = TRUE,
            allow_parallel = TRUE)

Arguments

`return_dissimilarity`	a logical indicating if the dissimilarity matrix between `Xr` and `Xu` must be returned.
`validation_type`	a character vector which indicates the (internal) validation method(s) to be used for assessing the global performance of the local models. Possible options are: `"NNv"` and `"local_cv"`. Alternatively `"none"` can be used when cross-validation is not required (see details below).
`tune_locally`	a logical. It only applies when `validation_type = "local_cv"` and "pls" or "wapls" fitting algorithms are used. If `TRUE`, the the parameters of the local pls-based models (i.e. pls factors for the "pls" method and minimum and maximum pls factors for the "wapls" method). Default is #' `TRUE`.
`number`	an integer indicating the number of sampling iterations at each local segment when `"local_cv"` is selected in the `validation_type` argument. Default is 10.
`p`	a numeric value indicating the percentage of observations to be retained at each sampling iteration at each local segment when `"local_cv"` is selected in the `validation_type` argument. Default is 0.75 %.
`range_prediction_limits`	a logical. It indicates whether the prediction limits at each local regression are determined by the range of the response variable within each neighborhood. When the predicted value is outside this range, it will be automatically replaced with the value of the nearest range value. If `FALSE`, no prediction limits are imposed. Default is `TRUE`.
`progress`	a logical indicating whether or not to print a progress bar for each observation to be predicted. Default is `TRUE`. Note: In case parallel processing is used, these progress bars will not be printed.
`allow_parallel`	a logical indicating if parallel execution is allowed. If `TRUE`, this parallelism is applied to the loop in `mbl` in which each iteration takes care of a single observation in `Xu`. The parallelization of this for loop is implemented using the foreach function of the `foreach` package. Default is `TRUE`.

Details

The validation methods available for assessing the predictive performance of the memory-based learning method used are described as follows:

Leave-nearest-neighbor-out cross-validation ("NNv"): From the group of neighbors of each observation to be predicted, the nearest observation (i.e. the most similar observation) is excluded and then a local model is fitted using the remaining neighbors. This model is then used to predict the value of the target response variable of the nearest observation. These predicted values are finally cross validated with the actual values (See Ramirez-Lopez et al. (2013a) for additional details). This method is faster than "local_cv".
Local leave-group-out cross-validation ("local_cv"): The group of neighbors of each observation to be predicted is partitioned into different equal size subsets. Each partition is selected based on a stratified random sampling which takes into account the values of the response variable of the corresponding set of neighbors. The selected local subset is used as local validation subset and the remaining observations are used for fitting a model. This model is used to predict the target response variable values of the local validation subset and the local root mean square error is computed. This process is repeated \(m\) times and the final local error is computed as the average of the local root mean square error of all the \(m\) iterations. In the mbl function \(m\) is controlled by the number argument and the size of the subsets is controlled by the p argument which indicates the percentage of observations to be selected from the subset of nearest neighbours. The global error of the predictions is computed as the average of the local root mean square errors.
No validation ("none"): No validation is carried out. If "none" is seleceted along with "NNv" and/or "local_cv", then it will be ignored and the respective validation(s) will be carried out.

Value

a list mirroring the specified parameters

Author(s)

Leonardo Ramirez-Lopez and Antoine Stevens

References

Ramirez-Lopez, L., Behrens, T., Schmidt, K., Stevens, A., Dematte, J.A.M., Scholten, T. 2013a. The spectrum-based learner: A new local approach for modeling soil vis-NIR spectra of complex data sets. Geoderma 195-196, 268-279.

Ramirez-Lopez, L., Behrens, T., Schmidt, K., Viscarra Rossel, R., Dematte, J. A. M., Scholten, T. 2013b. Distance and similarity-search metrics for use with soil vis-NIR spectra. Geoderma 199, 43-53.

Examples

# A control list with the default parameters
mbl_control()

[Package resemble version 2.2.3 Index]

A function that controls some few aspects of the memory-based learning process in the mbl function