Package 'Orangutan'

Run multivariate statistical tests

Description

Performs beta-dispersion and PERMANOVA analyses.

Usage

multivariate_tests(df, output_dir, seed_disp = NULL, seed_perm = NULL)

Arguments

df	A cleaned data frame containing morphometric traits.
output_dir	Directory where results will be written.
seed_disp	Optional integer; if provided, sets the random seed immediately before beta-dispersion permutation tests to ensure reproducibility.
seed_perm	Optional integer; if provided, sets the random seed immediately before PERMANOVA permutation tests to ensure reproducibility.

Value

A list containing multivariate test results.

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Run Orangutan

Description

Runs the full Orangutan morphometric analysis pipeline.

Usage

run_orangutan(
  data_path,
  output_dir = file.path(dirname(data_path), paste0("orangutan_outputs_",
    format(Sys.time(), "%Y%m%d_%H%M%S"))),
  apply_allometry = FALSE,
  allometry_var = NULL,
  remove_outliers = FALSE,
  outlier_vars = NULL,
  outlier_tail_pct = 0.05,
  palette_name = "Paired",
  custom_colors = NULL,
  species_to_encircle = character(0),
  seeds = list(betadisper = 123, permanova = 456),
  point_aes = list(point_size = 3.5, jitter_width = 0.1, jitter_alpha = 0.8, jitter_shape
    = 21, jitter_color = "black", jitter_stroke = 0.35),
  mean_aes = list(size = 1.8, shape = 21, fill = "white", color = "black", stroke = 0.6),
  violin_aes = list(alpha = 0.4),
  box_aes = list(alpha = 0.4, width = 0.15),
  label_aes = list(text_size = 6, axis_text_size = 10, title_size = 12, label_offset =
    0.05),
  label_templates = NULL,
  verbose = FALSE
)

Arguments

data_path	Path to input CSV file
output_dir	Output directory for results
apply_allometry	Logical; apply allometric correction
allometry_var	Character; size variable for allometry
remove_outliers	Logical; remove outliers
outlier_vars	Variables used for outlier detection
outlier_tail_pct	Tail proportion for Tukey filtering
palette_name	RColorBrewer palette name
custom_colors	Optional named vector of hex colors for species (e.g., c(A = "#FF0000"))
species_to_encircle	Species to encircle in multivariate plots
seeds	A named list of integer seeds for reproducibility, with elements: betadisper for beta-dispersion permutation tests and permanova for PERMANOVA permutation tests. Defaults to list(betadisper = 123, permanova = 456).
point_aes	List of point aesthetics
mean_aes	List of mean-point aesthetics
violin_aes	List of violin aesthetics
box_aes	List of boxplot aesthetics
label_aes	List of label/text aesthetics
label_templates	Optional plot label templates
verbose	Logical; if TRUE, print progress messages. Defaults to FALSE.

Value

A list containing results from all analyses

Examples

# Create a tiny example dataset in a temporary file
tmp <- tempfile(fileext = ".csv")
toy_data <- data.frame(
  species = c("A", "A", "B", "B", "C", "C"),
  trait1  = c(1, 2, 5, 6, 9, 10),
  trait2  = c(3, 4, 7, 8, 11, 12),
  trait3  = c(2, 3, 6, 7, 10, 11)
)
write.csv(toy_data, tmp, row.names = FALSE)

# Create a temporary output directory
out_dir <- tempdir()

# Set a named list of seeds for reproducibility
seeds <- list(betadisper = 123, permanova = 456)

# Run Orangutan on the toy dataset
res <- run_orangutan(
  data_path = tmp,
  output_dir = out_dir,
  seeds = seeds,
  verbose = FALSE
)

# Inspect returned object
str(res)

# Clean up temporary dataset file
unlink(tmp)

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