modal_regression {GUD}

Bayesian Modal Regression

Description

Bayesian Modal Regression

Usage

modal_regression(formula, data, model, ...)

Arguments

formula

a formula.

data

a dataframe.

model

a description of the error distribution. Can be one of "FG", "DTP" and "TPSC".

...

Arguments passed to rstan::sampling (e.g. iter, chains).

Details

The Bayesian modal regression model based on the FG, DTP or TPSC distribution is defined as:

Y_{i} = \mathbf{X}_{i} \boldsymbol{\beta} + e_{i},

where e_{i} follows the FG, DTP or TPSC distribution.

More details of the Bayesian modal regression model can be found at at Liu, Huang, and Bai (2024) https://arxiv.org/pdf/2211.10776.

Value

A draw object from the posterior package.

References

Liu Q, Huang X, Bai R (2024). “Bayesian Modal Regression Based on Mixture Distributions.” Computational Statistics & Data Analysis, 108012. doi:10.1016/j.csda.2024.108012.

Examples

# Save current user's options.
old <- options()
# (Optional - Running Multiple Chains in Parallel)
options(mc.cores = 2)

if (require(MASS)) { # Need Boston housing data from MASS package.
  # Fit the modal regression based on the FG distribution to the Boston housing data.
  FG_model <- modal_regression(formula = medv ~ .,
                               data = Boston,
                               model = "FG",
                               chains = 2,
                               iter = 2000)
  print(summary(FG_model), n = 17)

  # Fit the modal regression based on the TPSC-Student-t distribution to the Boston housing data.
  TPSC_model <- modal_regression(formula = medv ~ .,
                                 data = Boston,
                                 model = "TPSC",
                                 chains = 2,
                                 iter = 2000)
  print(summary(TPSC_model), n = 17)

  # Fit the modal regression based on the DTP-Student-t distribution to the Boston housing data.
  DTP_model <- modal_regression(formula = medv ~ .,
                                data = Boston,
                                model = "DTP",
                                chains = 2,
                                iter = 2000)
  print(summary(DTP_model), n = 17)
}

# reset (all) initial options
options(old)