gibbs_logistic: Fit logistic regression model

In ktw5691/psychtm: Text Mining Methods for Psychological Research

Description

gibbs_logistic() is used to fit a Bayesian logistic regression model using Gibbs sampling.

Usage

gibbs_logistic(
  formula,
  data,
  m = 100,
  burn = 0,
  thin = 1,
  mu0 = NULL,
  sigma0 = NULL,
  eta_start = NULL,
  proposal_sd = NULL,
  verbose = FALSE,
  display_progress = FALSE
)

Arguments

formula	An object of class formula: a symbolic description of the model to be fitted.
data	An optional data frame containing the variables in the model.
m	The number of iterations to run the Gibbs sampler (default: 100).
burn	The number of iterations to discard as the burn-in period (default: 0).
thin	The period of iterations to keep after the burn-in period (default: 1).
mu0	An optional p x 1 mean vector for the prior on the regression coefficients. See 'Details'.
sigma0	A p x p variance-covariance matrix for the prior on the regression coefficients. See 'Details'.
eta_start	A p x 1 vector of starting values for the regression coefficients.
proposal_sd	The proposal standard deviations for drawing the regression coefficients, N(0, proposal_sd(j)), j = 1, …, p (default: 2.38 for all coefficients).
verbose	Should parameter draws be output during sampling? (default: FALSE).
display_progress	Show progress bar? (default: FALSE). Do not use with verbose = TRUE.

Details

For mu0, by default, we use a vector of p 0s for p regression coefficients.

For sigma0, by default, we use a p x p diagonal matrix with diagonal elements (variances) of 6.25.

Value

An object of class Logistic.

See Also

Other Gibbs sampler: gibbs_mlr(), gibbs_sldax()

Examples

data(mtcars)
m1 <- gibbs_logistic(vs ~ hp, data = mtcars)

ktw5691/psychtm documentation built on Nov. 3, 2021, 9:10 a.m.