gen.params.mjmcmc: Generate a parameter list for MJMCMC (Mode Jumping MCMC)

Description

Generate a parameter list for MJMCMC (Mode Jumping MCMC)

Usage

gen.params.mjmcmc(data)

Value

A list of parameters to use when running the mjmcmc function.

The list contains the following elements:

burn_in: The burn-in period for the MJMCMC algorithm, which is set to 100 iterations by default.

mh

A list containing parameters for the regular Metropolis-Hastings (MH) kernel:

neigh.size: The size of the neighborhood for MH proposals with fixed proposal size, default set to 1.

neigh.min

The minimum neighborhood size for random proposal size, default set to 1.

neigh.max

The maximum neighborhood size for random proposal size, default set to 2.

large

A list containing parameters for the large jump kernel:

neigh.size: The size of the neighborhood for large jump proposals with fixed neighborhood size, default set to the smaller of 0.35 $\times p$ and 35, where $p$ is the number of covariates.

neigh.min

The minimum neighborhood size for large jumps with random size of the neighborhood, default set to the smaller of 0.25 $\times p$ and 25.

neigh.max

The maximum neighborhood size for large jumps with random size of the neighborhood, default set to the smaller of 0.45 $\times p$ and 45.

random

A list containing a parameter for the randomization kernel:

prob: The small probability of changing the component around the mode, default set to 0.01.

sa

A list containing parameters for the simulated annealing kernel:

probs: A numeric vector of length 6 specifying the probabilities for different types of proposals in the simulated annealing algorithm.

neigh.size

The size of the neighborhood for the simulated annealing proposals, default set to 1.

neigh.min

The minimum neighborhood size, default set to 1.

neigh.max

The maximum neighborhood size, default set to 2.

t.init

The initial temperature for simulated annealing, default set to 10.

t.min

The minimum temperature for simulated annealing, default set to 0.0001.

dt

The temperature decrement factor, default set to 3.

M

The number of iterations in the simulated annealing process, default set to 12.

greedy

A list containing parameters for the greedy algorithm:

probs: A numeric vector of length 6 specifying the probabilities for different types of proposals in the greedy algorithm.

neigh.size

The size of the neighborhood for greedy algorithm proposals, set to 1.

neigh.min

The minimum neighborhood size for greedy proposals, set to 1.

neigh.max

The maximum neighborhood size for greedy proposals, set to 2.

steps

The number of steps for the greedy algorithm, set to 20.

tries

The number of tries for the greedy algorithm, set to 3.

loglik

A list to store log-likelihood values, which is by default empty.

Note that the $loglik item is an empty list, which is passed to the log likelihood function of the model, intended to store parameters that the estimator function should use.

Arguments

data: The dataset that will be used in the algorithm

Examples

Run this code

gen.params.mjmcmc(matrix(rnorm(600), 100))

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