Man pages for glmaag
Adaptive LASSO and Network Regularized Generalized Linear Models
| coef.cv_glmaag | Coefficients |
| coef.glmaag | Coefficients for glmaag |
| coef.ss_glmaag | Coefficients for ss_glmaag |
| cv_glmaag | Cross validation for glmaag |
| evaluate | Evaluate prediction |
| evaluate_plot | Prediction visualization |
| getcut | Get optimal cut points for binary or right censored phenotype |
| getS | Estimate standardized Laplacian matrix |
| glmaag | Fit glmaag model |
| L0 | sample network 0 |
| L1 | sample network 1 |
| laps | Standardized Laplacian matrix |
| plot.cv_glmaag | Cross validation plot |
| plot.glmaag | Paths for glmaag object |
| plot.ss_glmaag | Instability plot |
| predict.cv_glmaag | Predict |
| predict.glmaag | Prediction for glmaag |
| predict.ss_glmaag | Prediction via stability selection |
| print.cv_glmaag | the results of the cross validation model |
| print.ss_glmaag | the results of the stability selection model |
| runtheExample | Shiny app |
| sampledata | Simulated data |
| ss_glmaag | Stability selection for glmaag |
| tune_network | tune two network |
