Man pages for Kaukol/IBGS
Variable selection in ultrahigh dimensions using Gibbs sampler
| ac.ratio | The acceptance ratio of jumping from the current model to the... |
| AICc | The corrected AIC function |
| binary.table | Binary table for imbalanced data |
| BlockGibbsSampler | The iterated block Gibbs sampler algorithm |
| BlockGibbsSampler.step1 | The first step in the block Gibbs sampler search algorithm |
| BlockGibbsSampler.step2 | The second step in the block Gibbs sampler search algorithm... |
| BlockGibbsSampler.step3 | Title |
| burn.seq | the function to burn first samples out |
| CV | Leave one out Cross-Validation in GLM |
| divide | Set up train and test sets |
| exBIC | extended BIC function |
| GibbsSampler | The Metropolized restricted Gibbs sampler |
| GibbsSamplerStep | Gibbs sampler search algorithm |
| imbalanced.metric | Imbalanced metrics |
| plots.Gibbs | plot the marginal probability for top predictors |
| plots.ichart | I-chart for the generated sequence |
| plots.mf | Model frequency figure for model selection |
| plots.vr | plot the marginal probability for top predictors |
| predicts.Gibbs | The prediction of the best model and model averaging over top... |
| remove.highly.cor | Remove highly correlation predictors |
| result.GibbsSampler | a summary of partial results |
| r.index | re-order the predictor sequence |
| sigmoid | sigmoid function to calculate the acceptance probability |
| v.sic | The function for model selection criterion value |
| weight | Calculate the weights based on the model selection criterion... |
