Man pages for glmnetr
Nested Cross Validation for the Relaxed Lasso and Other Machine Learning Models
| aicreg | Identify model based upon AIC criteria from a stepreg()... |
| ann_tab_cv | Fit an Artificial Neural Network model on "tabular" provided... |
| ann_tab_cv_best | Fit multiple Artificial Neural Network models on "tabular"... |
| best.preds | Get the best models for the steps of a stepreg() fit |
| boot.factor.foldid | Generate foldid's by 0/1 factor for bootstrap like samples... |
| calceloss | calculate cross-entry for multinomial outcomes |
| calplot | Construct calibration plots for a nested.glmnetr output... |
| cox.sat.dev | Calculate the CoxPH saturated log-likelihood |
| cv.glmnetr | Get a cross validation informed relaxed lasso model fit. |
| cv.stepreg | Cross validation informed stepwise regression model fit. |
| devrat_ | Calculate deviance ratios for CV based |
| diff_time | Output to console the elapsed and split times |
| diff_time1 | Get elapsed time in c(hour, minute, secs) |
| factor.foldid | Generate foldid's by factor levels |
| get.foldid | Get foldid's with branching for cox, binomial and gaussian... |
| get.id.foldid | Get foldid's when id variable is used to identify groups of... |
| glmnetr | Fit relaxed part of lasso model |
| glmnetr.cis | A redirect to nested.cis() |
| glmnetr.compcv | A redirect to nested.compare |
| glmnetr_seed | Get seeds to store, facilitating replicable results |
| glmnetr.simdata | Generate example data |
| nested.cis | Calculate performance measure "nominal" CI's and p's |
| nested.compare | Compare cross validation fit performances from a... |
| nested.compare_0_5_1 | Compare cross validation fit performances from a... |
| nested.glmnetr | Using (nested) cross validation, describe and compare some... |
| orf_tune | Fit a Random Forest model on data provided in matrix and... |
| plot.cv.glmnetr | Plot cross-validation deviances, or model coefficients. |
| plot.glmnetr | Plot the relaxed lasso coefficients. |
| plot.nested.glmnetr | Plot results from a nested.glmnetr() output |
| plot_perf_glmnetr | Plot nested cross validation performance summaries |
| predict_ann_tab | Get predicteds for an Artificial Neural Network model fit in... |
| predict.cv.glmnetr | Give predicteds based upon a cv.glmnetr() output object. |
| predict.cv.stepreg | Beta's or predicteds based upon a cv.stepreg() output object. |
| predict.glmnetr | Get predicteds or coefficients using a glmnetr output object |
| predict.nested.glmnetr | Give predicteds based upon the cv.glmnet output object... |
| print.nested.glmnetr | A redirect to the summary() function for nested.glmnetr()... |
| print.orf_tune | Print output from orf_tune() function |
| print.rf_tune | Print output from rf_tune() function |
| rederive_orf | Rederive Oblique Random Forest models not kept in... |
| rederive_rf | Rederive Random Forest models not kept in nested.glmnetr()... |
| rederive_xgb | Rederive XGB models not kept in nested.glmnetr() output |
| rf_tune | Fit a Random Forest model on data provided in matrix and... |
| roundperf | round elements of a summary.glmnetr() output |
| stepreg | Fit the steps of a stepwise regression. |
| summary.cv.glmnetr | Output summary of a cv.glmnetr() output object. |
| summary.cv.stepreg | Summarize results from a cv.stepreg() output object. |
| summary.nested.glmnetr | Summarize a nested.glmnetr() output object |
| summary.orf_tune | Summarize output from rf_tune() function |
| summary.rf_tune | Summarize output from rf_tune() function |
| summary.stepreg | Briefly summarize steps in a stepreg() output object, i.e. a... |
| xgb.simple | Get a simple XGBoost model fit (no tuning) |
| xgb.tuned | Get a tuned XGBoost model fit |
