nmr_pca_outliers: Compute PCA residuals and score distance for each sample

In sipss/AlpsNMR: Automated spectraL Processing System for NMR

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()

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)

sipss/AlpsNMR documentation built on Aug. 13, 2024, 5:11 p.m.