Man pages for rbmi
Reference Based Multiple Imputation
| add_class | Add a class |
| adjust_trajectories | Adjust trajectories due to the intercurrent event (ICE) |
| adjust_trajectories_single | Adjust trajectory of a subject's outcome due to the... |
| analyse | Analyse Multiple Imputed Datasets |
| ancova | Analysis of Covariance |
| ancova_single | Implements an Analysis of Covariance (ANCOVA) |
| antidepressant_data | Antidepressant trial data |
| apply_delta | Applies delta adjustment |
| as_analysis | Construct an 'analysis' object |
| as_ascii_table | as_ascii_table |
| as_class | Set Class |
| as_cropped_char | as_cropped_char |
| as_dataframe | Convert object to dataframe |
| as_draws | Creates a 'draws' object |
| as_imputation | Create an imputation object |
| as_indices | Convert indicator to index |
| as_mmrm_df | Creates a "MMRM" ready dataset |
| as_mmrm_formula | Create MMRM formula |
| as_model_df | Expand 'data.frame' into a design matrix |
| assert_variables_exist | Assert that all variables exist within a dataset |
| as_simple_formula | Creates a simple formula object from a string |
| as_stan_array | As array |
| as_strata | Create vector of Stratas |
| char2fct | Convert character variables to factor |
| check_ESS | Diagnostics of the MCMC based on ESS |
| check_hmc_diagn | Diagnostics of the MCMC based on HMC-related measures. |
| check_mcmc | Diagnostics of the MCMC |
| compute_sigma | Compute covariance matrix for some reference-based methods... |
| convert_to_imputation_list_df | Convert list of 'imputation_list_single()' objects to an... |
| delta_template | Create a delta 'data.frame' template |
| d_lagscale | Calculate delta from a lagged scale coefficient |
| do_not_run | Do not run this function |
| draws | Fit the base imputation model and get parameter estimates |
| encap_get_mmrm_sample | Encapsulate get_mmrm_sample |
| eval_mmrm | Evaluate a call to mmrm |
| expand | Expand and fill in missing 'data.frame' rows |
| extract_covariates | Extract Variables from string vector |
| extract_data_nmar_as_na | Set to NA outcome values that would be MNAR if they were... |
| extract_draws | Extract draws from a 'stanfit' object |
| extract_imputed_df | Extract imputed dataset |
| extract_imputed_dfs | Extract imputed datasets |
| extract_params | Extract parameters from a MMRM model |
| fit_mcmc | Fit the base imputation model using a Bayesian approach |
| fit_mmrm | Fit a MMRM model |
| generate_data_single | Generate data for a single group |
| get_bootstrap_stack | Creates a stack object populated with bootstrapped samples |
| get_cluster | Create cluster |
| get_conditional_parameters | Derive conditional multivariate normal parameters |
| get_delta_template | Get delta utility variables |
| get_draws_mle | Fit the base imputation model on bootstrap samples |
| get_ESS | Extract the Effective Sample Size (ESS) from a 'stanfit'... |
| get_ests_bmlmi | Von Hippel and Bartlett pooling of BMLMI method |
| get_example_data | Simulate a realistic example dataset |
| get_jackknife_stack | Creates a stack object populated with jackknife samples |
| get_mmrm_sample | Fit MMRM and returns parameter estimates |
| get_pattern_groups | Determine patients missingness group |
| get_pattern_groups_unique | Get Pattern Summary |
| get_pool_components | Expected Pool Components |
| getStrategies | Get imputation strategies |
| get_visit_distribution_parameters | Derive visit distribution parameters |
| has_class | Does object have a class ? |
| ife | if else |
| imputation_df | Create a valid 'imputation_df' object |
| imputation_list_df | List of imputations_df |
| imputation_list_single | A collection of 'imputation_singles()' grouped by a single... |
| imputation_single | Create a valid 'imputation_single' object |
| impute | Create imputed datasets |
| impute_data_individual | Impute data for a single subject |
| impute_internal | Create imputed datasets |
| impute_outcome | Sample outcome value |
| invert | invert |
| invert_indexes | Invert and derive indexes |
| is_absent | Is value absent |
| is_char_fact | Is character or factor |
| is_char_one | Is single character |
| is_in_rbmi_development | Is package in development mode? |
| is_num_char_fact | Is character, factor or numeric |
| locf | Last Observation Carried Forward |
| longDataConstructor | R6 Class for Storing / Accessing & Sampling Longitudinal Data |
| ls_design | Calculate design vector for the lsmeans |
| lsmeans | Least Square Means |
| method | Set the multiple imputation methodology |
| parametric_ci | Calculate parametric confidence intervals |
| pool | Pool analysis results obtained from the imputed datasets |
| pool_bootstrap_normal | Bootstrap Pooling via normal approximation |
| pool_bootstrap_percentile | Bootstrap Pooling via Percentiles |
| pool_internal | Internal Pool Methods |
| prepare_stan_data | Prepare input data to run the Stan model |
| print.analysis | Print 'analysis' object |
| print.draws | Print 'draws' object |
| print.imputation | Print 'imputation' object |
| progressLogger | R6 Class for printing current sampling progress |
| pval_percentile | P-value of percentile bootstrap |
| QR_decomp | QR decomposition |
| random_effects_expr | Construct random effects formula |
| rbmi-package | rbmi: Reference Based Multiple Imputation |
| record | Capture all Output |
| recursive_reduce | recursive_reduce |
| remove_if_all_missing | Remove subjects from dataset if they have no observed values |
| rubin_df | Barnard and Rubin degrees of freedom adjustment |
| rubin_rules | Combine estimates using Rubin's rules |
| sample_ids | Sample Patient Ids |
| sample_list | Create and validate a 'sample_list' object |
| sample_mvnorm | Sample random values from the multivariate normal... |
| sample_single | Create object of 'sample_single' class |
| scalerConstructor | R6 Class for scaling (and un-scaling) design matrices |
| set_simul_pars | Set simulation parameters of a study group. |
| set_vars | Set key variables |
| simulate_data | Generate data |
| simulate_dropout | Simulate drop-out |
| simulate_ice | Simulate intercurrent event |
| simulate_test_data | Create simulated datasets |
| sort_by | Sort 'data.frame' |
| split_dim | Transform array into list of arrays |
| split_imputations | Split a flat list of 'imputation_single()' into multiple... |
| Stack | R6 Class for a FIFO stack |
| strategies | Strategies |
| str_contains | Does a string contain a substring |
| string_pad | string_pad |
| transpose_imputations | Transpose imputations |
| transpose_results | Transpose results object |
| transpose_samples | Transpose samples |
| validate | Generic validation method |
| validate_analyse_pars | Validate analysis results |
| validate.analysis | Validate 'analysis' objects |
| validate_datalong | Validate a longdata object |
| validate.draws | Validate 'draws' object |
| validate.is_mar | Validate 'is_mar' for a given subject |
| validate.ivars | Validate inputs for 'vars' |
| validate.references | Validate user supplied references |
| validate.sample_list | Validate 'sample_list' object |
| validate.sample_single | Validate 'sample_single' object |
| validate.simul_pars | Validate a 'simul_pars' object |
| validate.stan_data | Validate a 'stan_data' object |
| validate_strategies | Validate user specified strategies |
