Bayesian quantile regression
bayesQR is an MCMC sampler to fit a Bayesian quantile regression model. This does not assume a factor structure.
- A formula of the form
formula = Y ~ X1 + X2, where
Yis the response and variables on the right-hand side are covariates.
- An optional data frame, list, or environment containing the variables in the model.
- Response quantile to model. Defaults to
- Number of MCMC iterations, with a default of
- Iterations of burn-in, with a default of
- Number of iterations to skip between stored values, with a default of
- Prior shape for $\tau$, which is the inverse scale of the response. Defaults to
- Prior scale for $\tau$.
- Prior precision (i.e., inverse variance) for $\beta$ regression parameters. Default is a diagonal matrix with non-zero values of
0.01. May be left at NULL, or changed to a non-negative scalar, a vector with length equal to the number of covariates, or a symmetric, positive semi-definite matrix with dimension equal to the number of covariates.
- Starting value for $\beta$.
TRUE, prints progress updates in Gibbs sampler.
Returns an item of the class
- Matrix of sampled parameter values.
- The matched call.
- The number of $\beta$ components.
- The number of observations.
- The number of Gibbs iterations before samples were stored.
- The number of Gibbs iterations between stored values.
- The total number of Gibbs iterations.
bayesQRcomposed of the following components:
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