Convert dynamite Output to draws_df Format

Converts the output from a dynamite() call to a draws_df format of the posterior package, enabling the use of diagnostics and plotting methods of posterior and bayesplot packages. Note that this function returns variables in a wide format, whereas as.data.frame() uses the long format.

Usage

# S3 method for dynamitefit
as_draws_df(x, parameters = NULL, responses = NULL, types = NULL, ...)

# S3 method for dynamitefit
as_draws(x, parameters = NULL, responses = NULL, types = NULL, ...)

Arguments

x

[dynamitefit]
The model fit object.

parameters

[character()]
Parameter(s) for which the samples should be extracted. Possible options can be found with function get_parameter_names(). Default is all parameters of specific type for all responses.

responses

[character()]
Response(s) for which the samples should be extracted. Possible options are elements of unique(x$priors$response), and the default is this entire vector. Ignored if the argument parameters is supplied.

types

[character()]
Type(s) of the parameters for which the samples should be extracted. See details of possible values. Default is all values listed in details except spline coefficients omega, omega_alpha, and omega_psi. See also get_parameter_types(). Ignored if the argument parameters is supplied.

...

Ignored.

Value

A draws_df object.

A draws_df object.

Details

You can use the arguments parameters, responses and types to extract only a subset of the model parameters (i.e., only certain types of parameters related to a certain response variable).

See potential values for the types argument in as.data.frame.dynamitefit() and get_parameter_names() for potential values for parameters argument.

See also

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

Examples

data.table::setDTthreads(1) # For CRAN
as_draws(gaussian_example_fit, types = c("sigma", "beta"))
#> # A draws_df: 100 iterations, 2 chains, and 2 variables
#>    beta_y_z sigma_y
#> 1         2    0.19
#> 2         2    0.19
#> 3         2    0.19
#> 4         2    0.19
#> 5         2    0.20
#> 6         2    0.20
#> 7         2    0.20
#> 8         2    0.20
#> 9         2    0.20
#> 10        2    0.20
#> # ... with 190 more draws
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}

# Compute MCMC diagnostics using the posterior package
posterior::summarise_draws(as_draws(gaussian_example_fit))
#> # A tibble: 143 × 10
#>    variable      mean median     sd    mad      q5   q95  rhat ess_bulk ess_tail
#>    <chr>        <dbl>  <dbl>  <dbl>  <dbl>   <dbl> <dbl> <dbl>    <dbl>    <dbl>
#>  1 alpha_y[2]  0.0579 0.0594 0.0301 0.0318 0.00740 0.102 1.01      144.     120.
#>  2 alpha_y[3]  0.0973 0.0955 0.0452 0.0478 0.0268  0.170 1.01      225.     191.
#>  3 alpha_y[4]  0.169  0.168  0.0407 0.0416 0.106   0.234 1.02      202.     188.
#>  4 alpha_y[5]  0.264  0.263  0.0410 0.0429 0.202   0.329 0.998     285.     218.
#>  5 alpha_y[6]  0.303  0.300  0.0392 0.0391 0.245   0.374 1.01      278.     154.
#>  6 alpha_y[7]  0.332  0.335  0.0384 0.0397 0.265   0.390 1.01      223.     114.
#>  7 alpha_y[8]  0.422  0.423  0.0348 0.0302 0.365   0.482 1.00      247.     163.
#>  8 alpha_y[9]  0.459  0.456  0.0382 0.0381 0.390   0.520 0.997     207.     220.
#>  9 alpha_y[10] 0.414  0.414  0.0433 0.0456 0.350   0.494 1.02      126.     165.
#> 10 alpha_y[11] 0.405  0.407  0.0412 0.0433 0.340   0.479 1.00      196.     231.
#> # ℹ 133 more rows