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.
nmr_pca_build_model(
nmr_dataset,
ncomp = NULL,
center = TRUE,
scale = FALSE,
...
)
# S3 method for nmr_dataset_1D
nmr_pca_build_model(
nmr_dataset,
ncomp = NULL,
center = TRUE,
scale = FALSE,
...
)a nmr_dataset_1D object
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))
(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.
(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
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
Other PCA related functions:
nmr_pca_outliers_filter(),
nmr_pca_outliers_plot(),
nmr_pca_outliers_robust(),
nmr_pca_outliers(),
nmr_pca_plots
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)