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.
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(),
...
)[dynamiteformula]
The model formula.
See dynamiteformula() and 'Details'.
[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().
[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.
[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.
[data.frame]
An optional data frame with prior
definitions. See get_priors() and 'Details'.
[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.
[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.
[logical(1)]
This is the verbose argument for
rstan::sampling(). Defaults to FALSE.
[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.
[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.
[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.
[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.
[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.
[list()]
Arguments passed to mice::mice() excluding data.
[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.
[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.
[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().
Model fitting
dynamite(),
get_priors(),
update.dynamitefit()