Reference Based Multiple Imputation

Documentation for package ‘rbmi’ version 1.2.6

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-- 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