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Ternary Plot of Holobiont Redox Resilience Allocation

Source: R/plot_RRI_ternary.R
plot_RRI_ternary.Rd

Creates a ternary diagram visualising the compositional allocation of holobiont redox buffering capacity across plant physiology (Physio), soil redox chemistry (Soil), and microbial resilience (Micro). Points are filled according to per-sample RRI values.

Usage

plot_RRI_ternary(
  ternary_df,
  point_size = 5,
  point_alpha = 0.9,
  palette = "plasma",
  show_subtitle = TRUE,
  show_centroid = TRUE,
  centroid_shape = 23,
  centroid_size = 1.4,
  tolerance = 1e-06,
  renormalize = FALSE,
  centroid_method = c("auto", "simplex_mean", "aitchison_mean")
)

Arguments

ternary_df

A data frame containing compositional columns Physio, Soil, Micro, and RRI.

point_size

Numeric; size of ternary points.

point_alpha

Numeric between 0 and 1 controlling point transparency.

palette

Character; viridis palette option.

show_subtitle

Logical; display system-level RRI mean in subtitle.

show_centroid

Logical; add compositional centroid marker.

centroid_shape

Numeric; ggplot2 shape for centroid marker.

centroid_size

Numeric multiplier for centroid size.

tolerance

Numeric; tolerance used for compositional closure checks.

renormalize

Logical; if TRUE, renormalises rows that do not sum to 1.

centroid_method

Character; one of "auto", "simplex_mean", or "aitchison_mean".

Value

A ggtern object.

Details

If clr-transformed coordinates are attached as an attribute ("clr"), the centroid can be computed using the Aitchison mean, ensuring geometric coherence in compositional space. Otherwise, a simplex arithmetic mean is used.

Examples

# ---- Simulate holobiont dataset (snapshot) ----
sim <- simulate_redox_holobiont(
  n_plot = 20,
  n_depth = 30,
  n_plant = 8,
  n_time = 10,
  p_micro = 30,
  seed = 1234
)

# ---- Compute RedoxRRI ----
res <- rri_pipeline_st(
  ROS_flux = sim$ROS_flux,
  Eh_stability = sim$Eh_stability,
  micro_data = sim$micro_data,
  id = sim$id,
  reducer = "per_domain",
  scaling = "pnorm"
)

# ---- Extract compositional scores ----
ternary_df <- res$row_scores_comp

# ---- Plot ternary allocation ----
p <- plot_RRI_ternary(
  ternary_df,
  point_size = 3,
  show_centroid = TRUE
)
#> Warning: Ignoring unknown aesthetics: z