CTS_4_sources: CTS Engine for 4 Sources

In OceanTracer: Marine Source Unmixing Toolkit

CTS Engine for 4 Sources

Description

Solves determined systems for 3 tracers and 4 sources to assess solution dispersion and discriminant capacity.

Usage

CTS_4_sources(source, mixture, iter = 1000, seed = 123456, verbose = TRUE)

triplets(source, mixture, iter = 1000, seed = 123456, verbose = TRUE)

CTS_seeds_triplets(source, mixture, iter = 1000, seed = 123456, verbose = TRUE)

Arguments

source	Data frame of sediment sources containing 4 source rows, mean values, standard deviations, and sample size ('n').
mixture	Data frame of mixture samples.
iter	Number of Monte Carlo iterations. Default is 1000.
seed	Integer seed for reproducibility. Default is 123456.
verbose	Logical. Whether to show a progress bar in the console. Default is TRUE.

Value

A data.frame ordered by max_dw (lowest dispersion first) containing:

id	Triplet of tracer names used for the system.
w1, w2, w3, w4	Calculated proportion contributions for Sources 1 through 4.
dw1, dw2, dw3, dw4	Dispersion uncertainties for Sources 1 through 4 (half 84% - 16% quantile range).
cons	Proportion of Monte Carlo iterations yielding physically valid proportions.
max_dw	Maximum dispersion uncertainty across all four sources.

Examples

# Prepare small dummy datasets for 4 sources and 3 tracers
src <- data.frame(
  ID = c("Src1", "Src2", "Src3", "Src4"),
  Tracer1 = c(10.5, 20.2, 5.0, 15.1),
  Tracer2 = c(2.1, 8.5, 12.3, 4.2),
  Tracer3 = c(0.5, 1.2, 3.4, 0.9),
  Tracer1_SD = c(1.1, 1.8, 0.5, 0.8),
  Tracer2_SD = c(0.3, 0.9, 1.1, 0.4),
  Tracer3_SD = c(0.1, 0.2, 0.3, 0.1),
  n = c(10, 10, 10, 10)
)
mix <- data.frame(ID = "Mix1", Tracer1 = 12.0, Tracer2 = 6.0, Tracer3 = 1.5)

# Run engine
result <- CTS_4_sources(source = src, mixture = mix, iter = 100, verbose = FALSE)
head(result)

OceanTracer documentation built on Oct. 15, 2026, 5:09 p.m.