FBMS (version 1.1)

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,
  impute = FALSE,
  loglik.pi = gaussian.loglik,
  method = "mjmcmc",
  verbose = TRUE,
  ...
)

Value

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

Arguments

formula

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

family

The distribution family of the response variable. Currently supports "gaussian", "binomial" and "custom". 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.

impute

TRUE means imputation combined with adding a dummy column with indicators of imputed values, FALSE (default) means only full data is used.

loglik.pi

Custom function to compute the logarithm of the posterior mode based on logarithm of marginal likelihood and logarithm of prior functions (needs specification only used if family = "custom")

method

Which fitting algorithm should be used, currently implemented options include "gmjmcmc", "gmjmcmc.parallel", "mjmcmc" and "mjmcmc.parallel" with "mjmcmc" being the default and 'mjmcmc' means that only linear models will be estimated

verbose

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

...

Additional parameters to be passed to the underlying method.

See Also

mjmcmc, gmjmcmc, gmjmcmc.parallel

Examples

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