# Reference Based Multiple Imputation

## Documentation for package ‘rbmi’ version 1.2.3

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

A C D E F G H I L M P Q R S T V

## -- A --

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 intercurrent event (ICE)
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.data.frame.pool | Pool analysis results obtained from the imputed datasets
assert_variables_exist | Assert that all variables exist within a dataset
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
as_simple_formula | Creates a simple formula object from a string
as_stan_array | As array
as_strata | Create vector of Stratas
as_vcov | Create simulated datasets

## -- C --

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 (JR, CIR)
convert_to_imputation_list_df | Convert list of 'imputation_list_single()' objects to an 'imputation_list_df()' object (i.e. a list of 'imputation_df()' objects's)

## -- D --

delta_template | Create a delta 'data.frame' template
do_not_run | Do not run this function
draws | Fit the base imputation model and get parameter estimates
draws.approxbayes | Fit the base imputation model and get parameter estimates
draws.bayes | Fit the base imputation model and get parameter estimates
draws.bmlmi | Fit the base imputation model and get parameter estimates
draws.condmean | Fit the base imputation model and get parameter estimates
d_lagscale | Calculate delta from a lagged scale coefficient

## -- E --

encap_get_mmrm_sample | Encapsulate get_mmrm_sample
eval_mmrm | Evaluate a call to mmrm
expand | Expand and fill in missing 'data.frame' rows
expand_locf | 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 missing (i.e. which occur after an ICE handled using a reference-based imputation strategy)
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

## -- F --

fill_locf | Expand and fill in missing 'data.frame' rows
fit_mcmc | Fit the base imputation model using a Bayesian approach
fit_mmrm | Fit a MMRM model

## -- G --

generate_data_single | Generate data for a single group
getStrategies | Get imputation strategies
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' object
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
get_visit_distribution_parameters | Derive visit distribution parameters

## -- H --

has_class | Does object have a class ?

## -- I --

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 subjid ID
imputation_single | Create a valid 'imputation_single' object
impute | Create imputed datasets
impute.condmean | Create imputed datasets
impute.random | 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

## -- L --

locf | Last Observation Carried Forward
longDataConstructor | R6 Class for Storing / Accessing & Sampling Longitudinal Data
lsmeans | Least Square Means
ls_design | Calculate design vector for the lsmeans
ls_design_equal | Calculate design vector for the lsmeans
ls_design_proportional | Calculate design vector for the lsmeans

## -- M --

method | Set the multiple imputation methodology
method_approxbayes | Set the multiple imputation methodology
method_bayes | Set the multiple imputation methodology
method_bmlmi | Set the multiple imputation methodology
method_condmean | Set the multiple imputation methodology

## -- P --

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
pool_internal.bmlmi | Internal Pool Methods
pool_internal.bootstrap | Internal Pool Methods
pool_internal.jackknife | Internal Pool Methods
pool_internal.rubin | 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
print.pool | Pool analysis results obtained from the imputed datasets
progressLogger | R6 Class for printing current sampling progress
pval_percentile | P-value of percentile bootstrap

## -- Q --

QR_decomp | QR decomposition

## -- R --

random_effects_expr | Construct random effects formula
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

## -- S --

sample_ids | Sample Patient Ids
sample_list | Create and validate a 'sample_list' object
sample_mvnorm | Sample random values from the multivariate normal distribution
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 'imputation_df()"s by ID
Stack | R6 Class for a FIFO stack
strategies | Strategies
strategy_CIR | Strategies
strategy_CR | Strategies
strategy_JR | Strategies
strategy_LMCF | Strategies
strategy_MAR | Strategies
string_pad | string_pad
str_contains | Does a string contain a substring

## -- T --

transpose_imputations | Transpose imputations
transpose_results | Transpose results object
transpose_samples | Transpose samples

## -- V --

validate | Generic validation method
validate.analysis | Validate 'analysis' objects
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_analyse_pars | Validate analysis results
validate_dataice | Validate a longdata object
validate_datalong | Validate a longdata object
validate_datalong_complete | Validate a longdata object
validate_datalong_notMissing | Validate a longdata object
validate_datalong_types | Validate a longdata object
validate_datalong_unifromStrata | Validate a longdata object
validate_datalong_varExists | Validate a longdata object
validate_strategies | Validate user specified strategies