nmr_pca_build_model {AlpsNMR} | R Documentation

## 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`

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()`

### 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)

[Package AlpsNMR version 3.2.3 Index]