R/pca_helpers.R
nmr_pca_outliers.Rd
Compute PCA residuals and score distance for each sample
nmr_pca_outliers(
nmr_dataset,
pca_model,
ncomp = NULL,
quantile_critical = 0.975
)
An nmr_dataset_1D object
A pca model returned by nmr_pca_build_model
Number of components to use. Use NULL
for 90% of the variance
critical quantile
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.
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()
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)