Hierarchical Shrinkage Stan Models for Biomarker Selection
Bayesian and LOO-adjusted R-squared
Next variable to enter the current submodel
Compute the fit of a submodel
Hierarchical shrinkage Stan models for biomarker selection
Hierarchical shrinkage models
K-fold cross-validation
Compute projections of full predictors on to subspace of predictors
Pointwise log-likelihood
Predictive information criteria for Bayesian models
Number of posterior samples
Plot of relative explanatory power of predictors
Posterior uncertainty intervals
Posterior distribution of the linear predictor
Posterior measures of performance
Posterior predictive distribution
Posterior summary
Print a summary for the fitted model
Forward selection minimizing KL-divergence in projection
Sampler statistics
Summary for the fitted model
Linear and logistic regression models penalized with hierarchical shrinkage priors for selection of biomarkers (or more general variable selection), which can be fitted using Stan (Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>). It implements the horseshoe and regularized horseshoe priors (Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>), as well as the projection predictive selection approach to recover a sparse set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020) <doi:10.1214/20-EJS1711>).