Extracts the names and dimensions of all parameters used in the
dynamite
model. See also get_parameter_types()
and
get_parameter_names()
. The returned dimensions match those of
the stanfit
element of the dynamitefit
object. When applied to
dynamiteformula
objects, the model is compiled and sampled for 1 iteration
to get the parameter dimensions.
get_parameter_dims(x, ...)# S3 method for dynamiteformula
get_parameter_dims(x, data, time, group = NULL, ...)
# S3 method for dynamitefit
get_parameter_dims(x, ...)
A named list with all parameter dimensions of the input model.
[dynamiteformula
or dynamitefit
]
The model formula or an
existing dynamitefit
object. See dynamiteformula()
and dynamite()
.
Ignored.
[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.
Model outputs
as.data.frame.dynamitefit()
,
as.data.table.dynamitefit()
,
as_draws_df.dynamitefit()
,
coef.dynamitefit()
,
confint.dynamitefit()
,
dynamite()
,
get_code()
,
get_data()
,
get_parameter_names()
,
get_parameter_types()
,
ndraws.dynamitefit()
,
nobs.dynamitefit()
data.table::setDTthreads(1) # For CRAN
get_parameter_dims(multichannel_example_fit)
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