nmr_pca_build_model {AlpsNMR} | R Documentation |
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 class 'nmr_dataset_1D' nmr_pca_build_model( nmr_dataset, ncomp = NULL, center = TRUE, scale = FALSE, ... )
nmr_dataset |
a nmr_dataset_1D object |
ncomp |
Integer, if data is complete |
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 |
scale |
(Default=FALSE) Logical indicating whether the variables should be
scaled to have unit variance before the analysis takes place. The default is
|
... |
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
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_outliers_filter()
,
nmr_pca_outliers_plot()
,
nmr_pca_outliers_robust()
,
nmr_pca_outliers()
,
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()
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)