nmr_pca_outliers {AlpsNMR} | R Documentation

## Compute PCA residuals and score distance for each sample

### Description

Compute PCA residuals and score distance for each sample

### Usage

    nmr_pca_outliers(
      nmr_dataset,
      pca_model,
      ncomp = NULL,
      quantile_critical = 0.975
    )

### Arguments

`nmr_dataset` | An nmr_dataset_1D object
`pca_model` | A pca model returned by nmr_pca_build_model
`ncomp` | Number of components to use. Use `NULL` for 90% of the variance
`quantile_critical` | critical quantile

### Value

A list with:

- outlier_info: A data frame with the NMRExperiment, the Q residuals and T scores

- ncomp: Number of components used to compute Q and T

- Tscore_critical, QResidual_critical: Critical values, given a quantile, for both Q and T.

### See Also

Other PCA related functions: `nmr_pca_build_model()`, `nmr_pca_outliers_filter()`, `nmr_pca_outliers_plot()`, `nmr_pca_outliers_robust()`, `nmr_pca_plots`

Other outlier detection functions: `Pipelines`, `nmr_pca_outliers_filter()`, `nmr_pca_outliers_plot()`, `nmr_pca_outliers_robust()`

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_ppm_resolution()`, `plot.nmr_dataset_1D()`, `plot_webgl()`, `print.nmr_dataset_1D()`, `rdCV_PLS_RF_ML()`, `rdCV_PLS_RF()`, `save_files_to_rDolphin()`, `to_ChemoSpec()`, `validate_nmr_dataset_peak_table()`, `validate_nmr_dataset()`

### Examples

    dir_to_demo_dataset <- system.file("dataset-demo", package = "AlpsNMR")
    dataset <- nmr_read_samples_dir(dir_to_demo_dataset)
    dataset_1D <- nmr_interpolate_1D(dataset, axis = c(min = -0.5, max = 10, by = 2.3E-4))
    model <- nmr_pca_build_model(dataset_1D)
    outliers_info <- nmr_pca_outliers(dataset_1D, model)

[Package AlpsNMR version 3.2.3 Index]