mcmc_temkinLM {adsoRptionMCMC} | R Documentation |
MCMC Analysis for Temkin Isotherm Linear Model
Description
Performs Bayesian parameter estimation using Markov Chain Monte Carlo (MCMC) to estimate the parameters of the Temkin isotherm based on its linearized form: Qe = aT + bT * log(Ce) 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. |
Temp |
Numeric value of temperature in Kelvin. |
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 containing:
- mcmc_results
An object of class
mcmc.list
containing posterior samples from all MCMC chains.- aT_mean
Posterior mean estimate of Temkin constant (aT).
- bT_mean
Posterior mean estimate of Temkin constant (bT).
- aT_raw_mean
Posterior mean of the intercept (aT) from the linear model.
- bT_raw_mean
Posterior mean of the slope (b_T) from the linear model.
- aT_sd
Posterior standard deviation of (aT).
- bT_sd
Posterior standard deviation of (bT).
- aT_ci
95% credible interval for (aT).
- bT_ci
95% credible interval for (bT ).
- gelman_diag
Gelman-Rubin convergence diagnostic.
- mcmc_summary
Summary statistics from the first 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_temkinLM(Ce, Qe, 298, burnin = 1000, mcmc = 5000, thin = 10,
verbose = 100, plot = TRUE, n_chains = 2, seed = 123)