Man pages for osofr/gridisl
Discrete Super Learner with Grid-Search for Longitudinal Data
| add_CVfolds_ind | Define a column of fold indicators for V-fold... |
| add_holdout_ind | Define and fit growth models evaluated on holdout... |
| as.int | Convert specific columns in 'vars' to numeric |
| as.num | Convert specific columns in 'vars' to integers |
| assign_model_name_id | Generate model names / IDs |
| char_to_factor | Convert all character columns to factors |
| cpp | Subset of growth data from the collaborative perinatal... |
| create_fit_object | Create a model fit list |
| create_fit_params | Create a list with main model parameters |
| defModel | Interface for defining models |
| drop_NA_y | Drop all observation rows with missing outcomes |
| eval_MSE | Evaluate MSE based on holdout/validation predictions |
| factor_to_dummy | Convert factors to binary indicators, for factors with > 2... |
| fit_model | Generic modeling function for longitudinal data. |
| fit.ModelStack | Fit Discrete SuperLearner |
| get_out_of_sample_predictions | Get the combined out of sample predictions from V... |
| get_train_data | Get training data used by the modeling object |
| get_validation_data | Get validation data used by the modeling object |
| get_yvalues | Get the y values (outcomes) used in the training data |
| glmModelClass | R6 class for storing the design matrix and the binary outcome... |
| GriDiSLOptions | Querying/setting a single 'gridisl' option |
| importData | Import data, define nodes (columns) and define input data R6... |
| logical_to_int | Convert logical covariates to integers |
| make_kfold_from_column | Convert a column of validation folds into origami format |
| make_model_report | Generate report(s) with modeling stats using pandoc. |
| make_PredictionStack | Combine models into ensemble |
| openFileInOS | Open file |
| pander.H2OBinomialMetrics | Pander method for H2OBinomialMetrics S4 class |
| pander.H2OGrid | Pander method for H2OGrid S4 class |
| pander.H2ORegressionMetrics | Pander method for H2ORegressionMetrics S4 class |
| plotMSEs | Plot the top (smallest) validation MSEs for an ensemble of... |
| predict_generic | Generic SuperLearner prediction function |
| predict_SL.PredictionSL | Predict for convex (continuous) SuperLearner fit |
| predict_SL.PredictionStack | Predict for discrete SuperLearner fit |
| prepare_data | Wrapper for several data processing functions. |
| print.brokenstickmodel | S3 methods for printing model fit summary for... |
| print.GLMmodel | S3 methods for printing model fit summary for glmfit class... |
| print_GriDiSL_opts | Print Current Option Settings for 'gridisl' |
| print.H2Oensemblemodel | S3 methods for printing model fit summary for H2Omodel class... |
| print.ModelStack | S3 methods for printing a collection of learners |
| print.PredictionModel | S3 methods for printing model fit summary for PredictionModel... |
| print.PredictionStack | S3 methods for printing model fit summary for PredictionModel... |
| print_tables | S3 methods for printing model fit summaries as pander tables |
| save_best_model | Save the best performing h2o model |
| set_all_GriDiSL_options | Setting 'gridisl' Options |
| summary.brokenstickmodel | S3 methods for getting model fit summary for glmfit class... |
| summary.GLMmodel | S3 methods for fit summary for glmfit class |
| summary.H2Oensemblemodel | S3 methods for getting model fit summary for H2Oensemblemodel... |
| summary.H2ORegressionModel | S3 methods for fit summary for h2o |
| summary.xgb.Booster | S3 methods for fit summary from xgboost |
| xgb.grid | Hyper-parameter grid search for xgboost |
| XGBoostClass | R6 class model fitting with xgboost R package |
