bayesQR
From factorQR v0.14
by Lane Burgette
Bayesian quantile regression
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
, whereY
is the response and variables on the righthand 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 burnin, with a default of
0
.  thin
 Number of iterations to skip between stored values, with a default of
0
.  C0
 Prior shape for $\tau$, which is the inverse scale of the response. Defaults to
1
.  D0
 Prior scale for $\tau$.
 B0
 Prior precision (i.e., inverse variance) for $\beta$ regression parameters. Default is a diagonal matrix with nonzero values of
0.01
. May be left at NULL, or changed to a nonnegative scalar, a vector with length equal to the number of covariates, or a symmetric, positive semidefinite matrix with dimension equal to the number of covariates.  betaZero
 Starting value for $\beta$.
 verbose
 If
TRUE
, prints progress updates in Gibbs sampler.
Value

Returns an item of the class
 param
 Matrix of sampled parameter values.
 call
 The matched call.
 betLen
 The number of $\beta$ 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.
bayesQR
composed of the following components:
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