Extract the Model Data of the dynamite Model

Returns the input data to the Stan model. Mostly useful for debugging.

Usage

get_data(x, ...)

# S3 method for class 'dynamiteformula'
get_data(x, data, time, group = NULL, ...)

# S3 method for class 'dynamitefit'
get_data(x, ...)

Arguments

x

[dynamiteformula or dynamitefit]
The model formula or an existing dynamitefit object. See dynamiteformula() and dynamite().

...

Ignored.

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.

Value

A list containing the input data to Stan.

See also

Model outputs as.data.frame.dynamitefit(), as.data.table.dynamitefit(), as_draws_df.dynamitefit(), coef.dynamitefit(), confint.dynamitefit(), dynamite(), get_code(), get_parameter_dims(), get_parameter_names(), get_parameter_types(), ndraws.dynamitefit(), nobs.dynamitefit()

Examples

data.table::setDTthreads(1) # For CRAN
d <- data.frame(y = rnorm(10), x = 1:10, time = 1:10, id = 1)
str(get_data(obs(y ~ x, family = "gaussian"),
  data = d, time = "time", group = "id"
))
#> List of 24
#>  $ K_fixed_y        : int 1
#>  $ K_varying_y      : int 0
#>  $ K_random_y       : int 0
#>  $ K_y              : int 1
#>  $ J_fixed_y        : int [1(1d)] 1
#>   ..- attr(*, "dimnames")=List of 1
#>   .. ..$ : chr "x"
#>  $ J_varying_y      : int[0 (1d)] 
#>  $ J_y              : int [1(1d)] 1
#>   ..- attr(*, "dimnames")=List of 1
#>   .. ..$ : chr "x"
#>  $ J_random_y       : int[0 (1d)] 
#>  $ L_fixed_y        : int [1(1d)] 1
#>  $ L_varying_y      : int[0 (1d)] 
#>  $ obs_y            : int [1, 1:10] 1 1 1 1 1 1 1 1 1 1
#>  $ n_obs_y          : int [1:10] 1 1 1 1 1 1 1 1 1 1
#>  $ t_obs_y          : int [1:10(1d)] 1 2 3 4 5 6 7 8 9 10
#>  $ T_obs_y          : int 10
#>  $ y_y              : num [1, 1:10] 0.6224 0.5068 -0.4262 1.6364 0.0428 ...
#>  $ beta_prior_pars_y: num [1, 1:2] 0 0.66
#>  $ N                : int 1
#>  $ K                : int 1
#>  $ X                : num [1:10, 1, 1] 1 2 3 4 5 6 7 8 9 10
#>  $ M                : int 0
#>  $ P                : num 0
#>  $ T                : int 10
#>  $ X_m              : num [1(1d)] 1
#>  $ grainsize        : num 10