fbms {FBMS}

Fit a BGNLM model using Genetically Modified Mode Jumping Markov Chain Monte Carlo (MCMC) sampling. Or Fit a BGLM model using Modified Mode Jumping Markov Chain Monte Carlo (MCMC) sampling.

Description

This function fits a model using the relevant MCMC sampling. The user can specify the formula, family, data, transforms, and other parameters to customize the model.

Usage

fbms(
  formula = NULL,
  family = "gaussian",
  data = NULL,
  transforms = NULL,
  loglik.pi = gaussian.loglik,
  loglik.alpha = gaussian.loglik.alpha,
  P = 10,
  runs = 10,
  cores = 1,
  verbose = FALSE,
  ...
)

Arguments

formula

A formula object specifying the model structure. Default is NULL.

family

The distribution family of the response variable. Currently supports "gaussian" and "binomial". Default is "gaussian".

data

A data frame containing the variables in the model. If NULL, the variables are taken from the environment of the formula. Default is NULL.

transforms

A list of transformations for BGNLM model. Default is NULL.

loglik.pi

The log-likelihood function for estimating the marginal likelihood and posterior modes (only used if family = "custom")

loglik.alpha

The log-likelihood function for the alpha parameter in the model. Default is gaussian.loglik.alpha.

P

The number of GMJMCMC generations. Default is 10.

runs

The number of parallel chains in case of parallel processing. Default is 2.

cores

The number of CPU cores to use for parallel processing. Default is 2.

verbose

If TRUE, print detailed progress information during the fitting process. Default is FALSE.

...

Additional parameters to be passed to the underlying MCMC fitting functions.

Value

An object containing the results of the fitted model and MCMC sampling.

See Also

mjmcmc, gmjmcmc, gmjmcmc.parallel

Examples

# Fit a Gaussian multivariate time series model
fbms_result <- fbms(
 X1 ~ .,
 family = "gaussian",
 data = data.frame(matrix(rnorm(600), 100)),
 P = 10,
 runs = 1,
 cores = 1
)
summary(fbms_result)
plot(fbms_result)