dynamice: Estimate a Bayesian Dynamic Multivariate Panel Model With...

In dynamite: Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data

Estimate a Bayesian Dynamic Multivariate Panel Model With Multiple Imputation

Description

Applies multiple imputation using mice::mice() to the supplied data and fits a dynamic multivariate panel model to each imputed data set using dynamite(). Posterior samples from each imputation run are combined. When using wide format imputation, the long format data is automatically converted to a wide format before imputation to preserve the longitudinal structure, and then converted back to long format for estimation.

Usage

dynamice(
  dformula,
  data,
  time,
  group = NULL,
  priors = NULL,
  backend = "rstan",
  verbose = TRUE,
  verbose_stan = FALSE,
  stanc_options = list("O0"),
  threads_per_chain = 1L,
  grainsize = NULL,
  custom_stan_model = NULL,
  debug = NULL,
  mice_args = list(),
  impute_format = "wide",
  keep_imputed = FALSE,
  stan_csv_dir = tempdir(),
  ...
)

Arguments

dformula	[dynamiteformula] The model formula. See dynamiteformula() and 'Details'.
data	[data.frame, tibble::tibble, or data.table::data.table] The data that contains the variables in the model in long format. Supported column types are integer, logical, double, and factor. Columns of type character will be converted to factors. Unused factor levels will be dropped. The data can contain missing values which will simply be ignored in the estimation in a case-wise fashion (per time-point and per channel). Input data is converted to channel specific matrix representations via stats::model.matrix.lm().
time	[character(1)] A column name of data that denotes the time index of observations. If this variable is a factor, the integer representation of its levels are used internally for defining the time indexing.
group	[character(1)] A column name of data that denotes the unique groups or NULL corresponding to a scenario without any groups. If group is NULL, a new column .group is created with constant value 1L is created indicating that all observations belong to the same group. In case of name conflicts with data, see the group_var element of the return object to get the column name of the new variable.
priors	[data.frame] An optional data frame with prior definitions. See get_priors() and 'Details'.
backend	[character(1)] Defines the backend interface to Stan, should be either "rstan" (the default) or "cmdstanr". Note that cmdstanr needs to be installed separately as it is not on CRAN. It also needs the actual CmdStan software. See https://mc-stan.org/cmdstanr/ for details.
verbose	[logical(1)] All warnings and messages are suppressed if set to FALSE. Defaults to TRUE. Setting this to FALSE will also disable checks for perfect collinearity in the model matrix.
verbose_stan	[logical(1)] This is the verbose argument for rstan::sampling(). Defaults to FALSE.
stanc_options	[list()] This is the stanc_options argument passed to the compile method of a CmdStanModel object via cmdstan_model() when backend = "cmdstanr". Defaults to list("O0"). To enable level one compiler optimizations, use list("O1"). See https://mc-stan.org/cmdstanr/reference/cmdstan_model.html for details.
threads_per_chain	[integer(1)] A Positive integer defining the number of parallel threads to use within each chain. Default is 1. See rstan::rstan_options() and https://mc-stan.org/cmdstanr/reference/model-method-sample.html for details.
grainsize	[integer(1)] A positive integer defining the suggested size of the partial sums when using within-chain parallelization. Default is number of time points divided by threads_per_chain. Setting this to 1 leads the workload division entirely to the internal scheduler. The performance of the within-chain parallelization can be sensitive to the choice of grainsize, see Stan manual on reduce-sum for details.
custom_stan_model	[character(1)] An optional character string that either contains a customized Stan model code or a path to a .stan file that contains the code. Using this will override the generated model code. For expert users only.
debug	[list()] A named list of form name = TRUE indicating additional objects in the environment of the dynamite function which are added to the return object. Additionally, values no_compile = TRUE and no_sampling = TRUE can be used to skip the compilation of the Stan code and sampling steps respectively. This can be useful for debugging when combined with model_code = TRUE, which adds the Stan model code to the return object.
mice_args	[list()] Arguments passed to mice::mice() excluding data.
impute_format	[character(1)] Format of the data that will be passed to the imputation method. Should be either "wide" (the default) or "long" corresponding to wide format and long format imputation.
keep_imputed	[logical(1)] Should the imputed datasets be kept in the return object? The default is FALSE. If TRUE, the imputations will be included in the imputed field in the return object that is otherwise NULL.
stan_csv_dir	[character(1)] A directory path to output the Stan .csv files when backend is "cmdstanr". The files are saved here via ⁠$save_output_files()⁠ to avoid garbage collection between sampling runs with different imputed datasets.
...	For dynamite(), additional arguments to rstan::sampling() or the ⁠$sample()⁠ method of the CmdStanModel object (see https://mc-stan.org/cmdstanr/reference/model-method-sample.html), such as chains and cores (chains and parallel_chains in cmdstanr). For summary(), additional arguments to as.data.frame.dynamitefit(). For print(), further arguments to the print method for tibbles (see tibble::formatting). Not used for formula().

See Also

Model fitting dynamite(), get_priors(), update.dynamitefit()

dynamite documentation built on April 12, 2025, 1:18 a.m.