multBayesQR | R Documentation |
This function estimates a multiple-output Bayesian quantile regression model
multBayesQR(
response,
formulaPred,
directionPoint,
tau = 0.5,
dataFile,
itNum = 2000,
burnin,
thin = 1,
chains = NULL,
betaValue = NULL,
sigmaValue = 1,
vSampleInit = NULL,
priorVar = 100,
hyperSigma = c(0.1, 0.1),
refresh = 100,
bayesx = TRUE,
sigmaSampling = TRUE,
quiet = T,
tobit = FALSE,
numCores = 1,
recordLat = FALSE,
outfile = NULL,
check_bayesx = FALSE,
path_bayesx = NULL,
adaptive_dir = FALSE,
...
)
response |
Names of response variables |
formulaPred |
a formula object, with . on the left side of a ~ operator, and the predictor terms, separated by + operators, on the right side. |
directionPoint |
Either a vector with the same number of dimensions of
response variable, indicating a direction, or a integer indicating the
number of directions equally spaced in the unit circle one should
estimate. When the response has more than 2 dimensions, this should
determine the number of points considered in each dimension, in order to
define the number of directions taken into account in estimation. The
number of directions is then equal to |
tau |
Quantiles of interest. Default is th median, |
dataFile |
A data.frame from which to find the variables defined in the formula. |
itNum |
Number of iterations. |
burnin |
Size of the initial to be discarded. |
thin |
Thinning parameter. Default value is 1. |
chains |
The number of chains to be run for each parameter. Only
available when using |
betaValue |
Initial values for the parameter beta for the continuous part. |
sigmaValue |
Initial value for the scale parameter. |
vSampleInit |
Initial value for the latent variables. |
priorVar |
Value that multiplies a identity matrix in the elicition process of the prior variance of the regression parameters. |
hyperSigma |
Vector of size containing the hyperparameters of the inverse gamma distribution for the sigma parameter of the asymmetric Laplace distribution. Default is c(0.1, 0.1), which gives a noninformative prior for sigma. |
refresh |
Interval between printing a message during the iteration process. Default is set to 100. |
bayesx |
If TRUE, the default, it uses bayesX software to estimate the quantile regression oarameters, which can be faster. If FALSE, it uses a Rcpp implementation of the MCMC sampler. |
sigmaSampling |
If TRUE, the default, it will sample from the posterior distribution of the scale parameter. If FALSE, all values will be fixed to 1. |
quiet |
If TRUE, the default, it does not print messages to check if the MCMC is actually updating. If FALSE, it will use the value of refresh to print messages to control the iteration process. |
tobit |
If TRUE, it will input the censored value for all observations with y = 0, according to the model. If FALSE, the default, it will estimate the parameter without this inputation process. |
numCores |
The number of cores that could be used for estimating parallel models when more than one direction is considered. |
recordLat |
If TRUE, it will keep the Markov chain samples for the latent variable. Default is FALSE. |
outfile |
argument to be passed to |
check_bayesx |
To check whether all calls to BayesX generated valid
chain values for all models. In case there are NA values, it calls BayesX
just for those models with problems. This is only considered when
|
path_bayesx |
When |
adaptive_dir |
If |
... |
arguments passed to |
A list with the chains of all parameters of interest.
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