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Perform Principal Component Analysis (PCA) on Spectral Data

Source: R/spec_pca.R
spec_pca.Rd

This function computes a Principal Component Analysis (PCA) on spectral data, excluding the wavenumber column from the analysis.

Usage

spec_pca(.data, wn_col = NULL, scale = TRUE, center = TRUE)

Arguments

.data

A data frame containing spectral data, with one column representing wavenumbers and the remaining columns containing spectral intensity values.

wn_col

A string specifying the name of the column that contains the wavenumber values. If NULL, the function attempts to retrieve the default wavenumber column set by `set_spec_wn()`.

scale

A logical value indicating whether the spectral data should be scaled (default is TRUE).

center

A logical value indicating whether the spectral data should be centered (default is TRUE).

Value

A `prcomp` object containing the PCA results, including principal components, standard deviations, and loadings.

Examples

# \donttest{
set_spec_wn("Wavenumber")
#> Successfully set 'Wavenumber' as the default wavenumber column.
pca_result <- spec_pca(CoHAspec)
summary(pca_result)
#> Importance of components:
#>                            PC1     PC2     PC3       PC4
#> Standard deviation     38.3080 18.1977 8.32702 3.398e-14
#> Proportion of Variance  0.7856  0.1773 0.03712 0.000e+00
#> Cumulative Proportion   0.7856  0.9629 1.00000 1.000e+00
# }