frequentist_unconstrained_lvp: Source apportionment via unconstrained least-squares with...

In OceanTracer: Marine Source Unmixing Toolkit

Source apportionment via unconstrained least-squares with linear variability propagation (LVP)

Description

Estimates the fractional contribution of each sediment source to a mixture by solving an overdetermined mass-balance system with least-squares minimisation. Unlike classical Monte Carlo propagation, uncertainty is handled analytically through Linear Variability Propagation (LVP): the Jacobian of the least-squares solution propagates source covariances directly into the apportionment uncertainty, avoiding assumptions of independent normally distributed errors.

Usage

frequentist_unconstrained_lvp(
  data,
  variability = "SEM",
  iter = 1000,
  means = FALSE,
  seed = 123456L,
  verbose = TRUE
)

Arguments

data	Data frame in the standard OceanTracer format (sources + mixture).
variability	Character. "SEM" for Standard Error of the Mean, "SD" for Standard Deviation.
iter	Integer or character. Number of iterations (e.g., 1000, or character keywords like "short", "medium", "long"). Default is 1000.
means	Logical. TRUE when the input is in averaged format (Mean_/SD_ columns); FALSE for raw observations.
seed	Integer. Random seed for reproducibility. Default is 123456L.
verbose	Logical. Whether to show progress bars and summary messages in the console. Default is TRUE.

Details

LVP is recommended when source variability is high or when apportionment are near the physical boundaries (0 or 1), where Monte Carlo resampling tends to underestimate uncertainty.

This function is the computational back-end called by unmixing when lvp = TRUE. For most users, calling unmixing directly is recommended.

Value

A data frame with one row per iteration containing the mixture ID, goodness-of-fit (GOF), and one column per source with the estimated fractional contribution.

References

Latorre, B., Lizaga, I., Gaspar, L., & Navas, A. (2025). Evaluating the impact of high source variability and extreme contributing sources on sediment fingerprinting models. Water Resources Management. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11269-025-04169-8")}

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