rdCV_PLS_RF_ML {AlpsNMR} | R Documentation

## Deprecated function Feature selection and validation in MULTILEVEL analysis

### Description

Statistical analysis and feature selection in a repeated double cross-validation frame based on the partial least squares (PLS) or random forest (RF) analyses using an algorithm for multivariate modelling with minimally biased variable selection (MUVR) from the `MUVR` package. The function `rdCV_PLS_RF_ML` allows the multilevel comparison, especially useful in crossover or longitudinal studies (2 timepoints) considering the same individual (it requires 2 samples of the same observation).

### Usage

    rdCV_PLS_RF_ML(nmr_peak_table)

### Arguments

`nmr_peak_table` | an AlpsNMR integration object (2 classes)

### Value

a MUVR model containing selection parameters, validation and fitness

### References

Shi,L. et al. (2018) Variable selection and validation in multivariate modelling. Bioinformatics.

### See Also

Other nmr_dataset_1D functions: `[.nmr_dataset_1D()`, `computes_peak_width_ppm()`, `file_lister()`, `files_to_rDolphin()`, `format.nmr_dataset_1D()`, `is.nmr_dataset_1D()`, `load_and_save_functions`, `new_nmr_dataset_1D()`, `nmr_align_find_ref()`, `nmr_baseline_removal()`, `nmr_baseline_threshold()`, `nmr_exclude_region()`, `nmr_integrate_regions()`, `nmr_interpolate_1D()`, `nmr_meta_add()`, `nmr_meta_export()`, `nmr_meta_get_column()`, `nmr_meta_get()`, `nmr_normalize()`, `nmr_pca_build_model()`, `nmr_pca_outliers_filter()`, `nmr_pca_outliers_plot()`, `nmr_pca_outliers_robust()`, `nmr_pca_outliers()`, `nmr_ppm_resolution()`, `plot.nmr_dataset_1D()`, `plot_webgl()`, `print.nmr_dataset_1D()`, `rdCV_PLS_RF()`, `save_files_to_rDolphin()`, `to_ChemoSpec()`, `validate_nmr_dataset_peak_table()`, `validate_nmr_dataset()`

### Examples

    message("Deprecated. MUVR is not compatible with Bioconductor,
    use bp_kfold_VIP_analysis method instead")

[Package AlpsNMR version 3.2.3 Index]