mcmc_langmuirLM4 {adsoRptionMCMC}R Documentation

MCMC Analysis for Langmuir Isotherm Linear (Form 4) Model

Description

Performs Bayesian parameter estimation using Markov Chain Monte Carlo (MCMC) to estimate the parameters of the Langmuir isotherm using its fourth linear form: Qe / Ce = b * Qmax - b * Qe This method provides a probabilistic interpretation of the model parameters and accounts for their uncertainties. It supports multiple MCMC chains and computes convergence diagnostics (Gelman-Rubin).

Arguments

Ce

Numeric vector of equilibrium concentrations.

Qe

Numeric vector of adsorbed amounts.

burnin

Integer specifying the number of burn-in iterations (default is 1000).

mcmc

Integer specifying the total number of MCMC iterations (default is 5000).

thin

Integer specifying the thinning interval (default is 10).

verbose

Integer controlling the frequency of progress updates (default is 100).

plot

Logical; if TRUE, trace and density plots of the MCMC chains are shown (default is FALSE).

n_chains

Number of independent MCMC chains (default = 2).

seed

Optional integer for reproducibility.

Value

A list with:

mcmc_results

Combined posterior samples (mcmc.list).

Qmax_mean

Posterior mean of Qmax.

b_mean

Posterior mean of b.

intercept_mean

Posterior mean of intercept (b * Qmax).

intercept_sd

Posterior standard deviation of intercept.

intercept_ci

95% credible interval for intercept.

slope_mean

Posterior mean of slope (-b).

slope_sd

Posterior standard deviation of slope.

slope_ci

95% credible interval for slope.

gelman_diag

Gelman-Rubin convergence diagnostics.

mcmc_summary

Summary of the first MCMC chain.

Author(s)

Paul Angelo C. Manlapaz

References

Gilks, W. R., Richardson, S., & Spiegelhalter, D. J. (1995). Markov Chain Monte Carlo in Practice. Chapman and Hall/CRC.

Examples

Ce <- c(0.01353, 0.04648, 0.13239, 0.27714, 0.41600, 0.63607, 0.80435, 1.10327, 1.58223)
Qe <- c(0.03409, 0.06025, 0.10622, 0.12842, 0.15299, 0.15379, 0.15735, 0.15735, 0.16607)
mcmc_langmuirLM4(Ce, Qe, burnin = 1000, mcmc = 10000, thin = 10,
                 verbose = 100, plot = TRUE, n_chains = 2, seed = 123)

[Package adsoRptionMCMC version 0.1.0 Index]