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Perform Principal Component Analysis

Source: R/6_pca_results.R
pca_results.Rd

pca_results performs Principal Component Analysis on the pc_data data frame containing statistics from wavelet analysis

Usage

pca_results(
  pc_data,
  standardise = TRUE,
  No_pcs = NULL,
  PCV = NULL,
  plot_eigenvalues = TRUE,
  output = FALSE,
  output_folder = NULL,
  verbose = FALSE,
  interactive_mode = TRUE
)

Arguments

pc_data

Data frame containing the output of the pca_data() function.

standardise

TRUE or FALSE. Whether or not to standardise the data. Default TRUE.

No_pcs

Numerical. Number of principal components to retain. Null by default

PCV

Numerical. Percentage of cumulative variance to retain. Null by default

plot_eigenvalues

TRUE or FALSE. Plot PC eigenvalues and general loadings. Default TRUE.

output

Logical. If TRUE, output is saved to output_folder. Defaults to FALSE.

output_folder

Output folder path. If output = TRUE, output_folder must be provided. Defaults to NULL.

verbose

Logical. If TRUE, progress messages are shown. Defaults to FALSE.

interactive_mode

TRUE or FALSE. Used for testing the package. Default FALSE.

Value

A PCA object from 'FactoMineR' package containing the output of the Principal Component Analysis.

Examples

# Set file path
filepath <- system.file("extdata", package = "FishDiveR")

# Load pc_data
pc_data <- readRDS(file.path(filepath, "data/4_PCA/pc_data.rds"))


# Run a minimal, fast pca_results example
pc_results <- pca_results(
  pc_data = pc_data,
  standardise = TRUE,
  No_pcs = 1,
  PCV = NULL,
  plot_eigenvalues = FALSE,
  output = TRUE,
  output_folder = tempdir(),
  verbose = TRUE,
  interactive_mode = FALSE
)
#> Non-numerical columns being excluded: tag_ID, date_only
#> 7 principal components of 9 have eigenvalues >= 1
#> Output file: /tmp/RtmpTKWEE7/data/4_PCA/eigenvalues_cum_var.csv
#> Keeping 1 PC's contributing to a cumulative 47.9402033916319% of variance
#> Output file: /tmp/RtmpTKWEE7/data/4_PCA/pc_results.rds contains the selected number of principal components.

# \donttest{
# Full example using the complete dataset
# Run pca_results function
pc_results <- pca_results(
  pc_data = pc_data,
  standardise = TRUE,
  No_pcs = 3,
  PCV = NULL,
  plot_eigenvalues = TRUE,
  output = TRUE,
  output_folder = tempdir(),
  verbose = TRUE,
  interactive_mode = FALSE
)
#> Non-numerical columns being excluded: tag_ID, date_only
#> 7 principal components of 9 have eigenvalues >= 1
#> Output file: /tmp/RtmpTKWEE7/data/4_PCA/eigenvalues_cum_var.csv
#> Keeping 3 PC's contributing to a cumulative 91.6918728473569% of variance
#> Output folder: /tmp/RtmpTKWEE7/data/4_PCA
#> Output file: /tmp/RtmpTKWEE7/data/4_PCA/pc_results.rds contains the selected number of principal components.
# }