Package index • rbmi

All functions

QR_decomp(): QR decomposition

STAN_BLOCKS: List of Stan Blocks

Stack: R6 Class for a FIFO stack

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_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

assert_variables_exist(): Assert that all variables exist within a dataset

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)

control_bayes(): Control the computational details of the imputation methods

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_lagscale(): Calculate delta from a lagged scale coefficient

delta_template(): Create a delta data.frame template

draws(): Fit the base imputation model and get parameter estimates

eval_mmrm(): Evaluate a call to mmrm

expand() fill_locf() 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

fit_mcmc(): Fit the base imputation model using a Bayesian approach

fit_mmrm(): Fit a MMRM model

format_method_descriptions(): Format method descriptions

generate_data_single(): Generate data for a single group

getStrategies(): Get imputation strategies

get_ESS(): Extract the Effective Sample Size (ESS) from a stanfit object

get_bootstrap_stack(): Creates a stack object populated with bootstrapped samples

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_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

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 subjid ID

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_equal() ls_design_counterfactual() ls_design_proportional(): Calculate design vector for the lsmeans

lsmeans(): Least Square Means

make_rbmi_cluster(): Create a rbmi ready cluster

mcse_jackknife() jackknife_se() mcse_combine_all_pars(): Internal MCSE Computations

method_bayes() method_approxbayes() method_condmean() method_bmlmi(): Set the multiple imputation methodology

par_lapply(): Parallelise Lapply

parametric_ci(): Calculate parametric confidence intervals

pool() as.data.frame(<pool>) print(<pool>) mcse() as.data.frame(<mcse>) print(<mcse>): 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

random_effects_expr(): Construct random effects formula

set_options(): rbmi settings

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 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() as_vcov(): 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

str_contains(): Does a string contain a substring

strategy_MAR() strategy_JR() strategy_CR() strategy_CIR() strategy_LMCF(): Strategies

string_pad(): string_pad

transpose_imputations(): Transpose imputations

transpose_results(): Transpose results object

transpose_samples(): Transpose samples

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_datalong() validate_datalong_varExists() validate_datalong_types() validate_datalong_notMissing() validate_datalong_complete() validate_datalong_unifromStrata() validate_dataice(): Validate a longdata object

validate_strategies(): Validate user specified strategies