run_adap_metropolis_MCMC: run_adap_metropolis_MCMC
In khaors/pumpingtest: R Package for the analysis and evaluation of pumping tests

Description Usage Arguments Details Value Author(s) References See Also

View source: R/adaptative_mcmc_utilities.R

Function to sample for a complex probabibility density function using MCMC with the adaptative Metropolis algorithm proposed by Roberts and Rosenthal(2009).

1
2
3

run_adap_metropolis_MCMC(startvalue, iterations = 10000,
  iter_update_par = 100, ptest, model, prior.pdf, prior.parameters,
  proposal.sigma, cov.corr = FALSE)

`startvalue`	A numeric vector with the fit parameters of the pumping test.
`iterations`	An integer with the number of iterations to run the chain.
`iter_update_par`	An integet specifying the number of iterations to update the covariance matrix.
`ptest`	A pumping_test object.
`model`	A character string with the name of the model used in the parameter estimation.
`prior.pdf`	A character vector with the distributions of the fit parameters ( 'unif' and 'norm' are currently supported).
`prior.parameters`	A matrix with the parameters of the distributions (min and max for uniform distributions, mean and sd for normal distributions)
`proposal.sigma`	A numeric vector with the standard deviations of the proposal distribution.
`cov.corr`	A logical flag indicating if the covariance matrix must be corrected for positive definiteness.

This function implements the adaptative MCMC proposed by Roberts and Rosenthal (2009), in which the proposal distribution Q_{n}(x, \cdot) is given by:

Q_{n}(x, \cdot) = ≤ft\{ \begin{aligned} &(1-θ)N(x, (2.38)^{2}Σ_{n}/d) + θ N(x,(0.1)^{2}I_{d}/d), &Σ_{n}\text{ is positive definite} \\ &N(x,(0.1)^{2}I_{d}), &Σ_{n}\text{ is not positive definitive}\\ \end{aligned} \right.

where