nmr_pca_build_model: Build a PCA on for an nmr_dataset

In sipss/AlpsNMR: Automated spectraL Processing System for NMR

Build a PCA on for an nmr_dataset

Description

This function builds a PCA model with all the NMR spectra. Regions with zero values (excluded regions) or near-zero variance regions are automatically excluded from the analysis.

Usage

nmr_pca_build_model(
  nmr_dataset,
  ncomp = NULL,
  center = TRUE,
  scale = FALSE,
  ...
)

## S3 method for class 'nmr_dataset_1D'
nmr_pca_build_model(
  nmr_dataset,
  ncomp = NULL,
  center = TRUE,
  scale = FALSE,
  ...
)

Arguments

nmr_dataset	a nmr_dataset_1D object
ncomp	Integer, if data is complete ncomp decides the number of components and associated eigenvalues to display from the pcasvd algorithm and if the data has missing values, ncomp gives the number of components to keep to perform the reconstitution of the data using the NIPALS algorithm. If NULL, function sets ncomp = min(nrow(X), ncol(X))
center	(Default=TRUE) Logical, whether the variables should be shifted to be zero centered. Only set to FALSE if data have already been centered. Alternatively, a vector of length equal the number of columns of X can be supplied. The value is passed to scale. If the data contain missing values, columns should be centered for reliable results.
scale	(Default=FALSE) Logical indicating whether the variables should be scaled to have unit variance before the analysis takes place. The default is FALSE for consistency with prcomp function, but in general scaling is advisable. Alternatively, a vector of length equal the number of columns of X can be supplied. The value is passed to scale.
...	Additional arguments passed on to mixOmics::pca

Value

A PCA model as given by mixOmics::pca with two additional attributes:

• nmr_data_axis containing the full ppm axis

• nmr_included with the data points included in the model These attributes are used internally by AlpsNMR to create loading plots

See Also

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

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)

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)

sipss/AlpsNMR documentation built on June 29, 2023, 6:51 a.m.