pqrBayes-package {pqrBayes} | R Documentation |
Bayesian penalized quantile regression for linear, binary LASSO, group LASSO and varying coefficient models
Description
In this package, we implement Bayesian penalized quantile regression for linear regression (i.e., LASSO), binary LASSO, group LASSO and quantile varying coefficient (VC) models. Point-mass spike-and-slab priors have been incorporated in the Bayesian hierarchical models to facilitate Bayesian shrinkage estimation with exact sparsity in these models. The four default methods are Bayesian regularized quantile regression with spike-and-slab priors under the linear (i.e., LASSO), binary LASSO, group LASSO and VC model, correspondingly. In addition to default methods, users can also choose methods without robustness and/or spike–and–slab priors.
Details
The user friendly, integrated interface pqrBayes() allows users to flexibly choose fitting models by specifying the following parameters:
robust: | whether to fit a robust sparse quantile regression model (LASSO, binary LASSO, |
group LASSO or Varying Coefficient models) or their non-robust counterparts. | |
sparse: | whether to use the spike-and-slab priors to impose exact sparsity. |
model: | whether to fit a linear model (i.e., LASSO), binary LASSO, group LASSO |
or a varying coefficient model. |
The function pqrBayes() returns a pqrBayes object that stores the posterior estimates of regression coefficients.
References
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