bayesQR: Bayesian quantile regression

In factorQR: Bayesian quantile regression factor models

Description

bayesQR is an MCMC sampler to fit a Bayesian quantile regression model. This does not assume a factor structure.

Arguments

formula	A formula of the form formula = Y ~ X1 + X2, where Y is the response and variables on the right-hand side are covariates.
dataSet	An optional data frame, list, or environment containing the variables in the model.
pQuant	Response quantile to model. Defaults to pQuant=0.5.
nSamp	Number of MCMC iterations, with a default of 5000.
burn	Iterations of burn-in, with a default of 0.
thin	Number of iterations to skip between stored values, with a default of 0.
C0	Prior shape for τ, which is the inverse scale of the response. Defaults to 1.
D0	Prior scale for τ.
B0	Prior precision (i.e., inverse variance) for β 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.
betaZero	Starting value for β.
verbose	If TRUE, prints progress updates in Gibbs sampler.

Value

Returns an item of the class bayesQR composed of the following components:

param	Matrix of sampled parameter values.
call	The matched call.
betLen	The number of β components.
nObs	The number of observations.
burn	The number of Gibbs iterations before samples were stored.
thin	The number of Gibbs iterations between stored values.
nSamp	The total number of Gibbs iterations.

Author(s)

Lane F. Burgette, Department of Statistical Science, Duke University. [e-mail]

factorQR documentation built on May 2, 2019, 3:38 p.m.