Extract Samples From a dynamitefit Object as a Data Frame

Provides a data.frame representation of the posterior samples of the model parameters.

Usage

# S3 method for dynamitefit
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  parameters = NULL,
  responses = NULL,
  types = NULL,
  summary = FALSE,
  probs = c(0.05, 0.95),
  include_fixed = TRUE,
  ...
)

Arguments

x

[dynamitefit]
The model fit object.

row.names

Ignored.

optional

Ignored.

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.

summary

[logical(1)]
If TRUE, returns posterior mean, standard deviation, and posterior quantiles (as defined by the probs argument) for all parameters. If FALSE (default), returns the posterior samples instead.

probs

[numeric()]
Quantiles of interest. Default is c(0.05, 0.95).

include_fixed

[logical(1)]
If TRUE (default), time-varying parameters for 1:fixed time points are included in the output as NA values. If FALSE, fixed time points are omitted completely from the output.

...

Ignored.

Value

A tibble containing either samples or summary statistics of the model parameters in a long format. For a wide format, see as_draws().

Details

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

Potential values for the types argument are:

• alpha
  Intercept terms (time-invariant or time-varying).

• beta
  Time-invariant regression coefficients.

• delta
  Time-varying regression coefficients.

• nu
  Group-level random effects.

• lambda
  Factor loadings.

• psi
  Latent factors.

• tau
  Standard deviations of the spline coefficients of delta.

• tau_alpha
  Standard deviations of the spline coefficients of time-varying alpha.

• sigma_nu
  Standard deviations of the random effects nu.

• corr_nu
  Pairwise within-group correlations of random effects nu. Samples of the full correlation matrix can be extracted manually as rstan::extract(fit$stanfit, pars = "corr_matrix_nu") if necessary.

• sigma_lambda
  Standard deviations of the latent factor loadings lambda.

• tau_psi
  Standard deviations of the the spline coefficients of psi.

• corr_psi
  Pairwise correlations of the latent factors. Samples of the full correlation matrix can be extracted manually as rstan::extract(fit$stanfit, pars = "corr_matrix_psi") if necessary.

• sigma
  Standard deviations of gaussian responses.

• corr
  Pairwise correlations of multivariate gaussian responses.

• phi
  Describes various distributional parameters, such as:

  • Dispersion parameter of the Negative Binomial distribution.

  • Shape parameter of the Gamma distribution.

  • Precision parameter of the Beta distribution.

  • Degrees of freedom of the Student t-distribution.

• omega
  Spline coefficients of the regression coefficients delta.

• omega_alpha
  Spline coefficients of time-varying alpha.

• omega_psi
  Spline coefficients of the latent factors psi.

See also

Model outputs as.data.table.dynamitefit(), as_draws_df.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.data.frame(
  gaussian_example_fit,
  responses = "y",
  types = "beta"
)
#> # A tibble: 200 × 10
#>    parameter value  time category group response type  .draw .iteration .chain
#>    <chr>     <dbl> <int> <chr>    <int> <chr>    <chr> <int>      <int>  <int>
#>  1 beta_y_z   1.97    NA NA          NA y        beta      1          1      1
#>  2 beta_y_z   1.96    NA NA          NA y        beta      2          2      1
#>  3 beta_y_z   1.95    NA NA          NA y        beta      3          3      1
#>  4 beta_y_z   1.96    NA NA          NA y        beta      4          4      1
#>  5 beta_y_z   1.97    NA NA          NA y        beta      5          5      1
#>  6 beta_y_z   1.96    NA NA          NA y        beta      6          6      1
#>  7 beta_y_z   1.97    NA NA          NA y        beta      7          7      1
#>  8 beta_y_z   1.97    NA NA          NA y        beta      8          8      1
#>  9 beta_y_z   1.95    NA NA          NA y        beta      9          9      1
#> 10 beta_y_z   1.97    NA NA          NA y        beta     10         10      1
#> # ℹ 190 more rows

# Basic summaries can be obtained automatically with summary = TRUE
as.data.frame(
  gaussian_example_fit,
  responses = "y",
  types = "beta",
  summary = TRUE
)
#> # A tibble: 1 × 10
#>   parameter  mean     sd    q5   q95  time group category response type 
#>   <chr>     <dbl>  <dbl> <dbl> <dbl> <int> <int> <chr>    <chr>    <chr>
#> 1 beta_y_z   1.97 0.0122  1.95  1.99    NA    NA NA       y        beta 

# Time-varying coefficients "delta"
as.data.frame(
  gaussian_example_fit,
  responses = "y",
  types = "delta",
  summary = TRUE
)
#> # A tibble: 60 × 10
#>    parameter   mean      sd     q5    q95  time group category response type 
#>    <chr>      <dbl>   <dbl>  <dbl>  <dbl> <int> <int> <chr>    <chr>    <chr>
#>  1 delta_y_x NA     NA      NA     NA         1    NA NA       y        delta
#>  2 delta_y_x -0.161  0.0310 -0.206 -0.109     2    NA NA       y        delta
#>  3 delta_y_x -0.906  0.0308 -0.960 -0.852     3    NA NA       y        delta
#>  4 delta_y_x -0.883  0.0250 -0.921 -0.841     4    NA NA       y        delta
#>  5 delta_y_x -0.915  0.0218 -0.951 -0.881     5    NA NA       y        delta
#>  6 delta_y_x -1.06   0.0214 -1.10  -1.03      6    NA NA       y        delta
#>  7 delta_y_x -1.14   0.0224 -1.18  -1.10      7    NA NA       y        delta
#>  8 delta_y_x -0.998  0.0208 -1.03  -0.966     8    NA NA       y        delta
#>  9 delta_y_x -0.905  0.0244 -0.945 -0.867     9    NA NA       y        delta
#> 10 delta_y_x -1.01   0.0221 -1.05  -0.975    10    NA NA       y        delta
#> # ℹ 50 more rows

# Obtain summaries for a specific parameters
as.data.frame(
  gaussian_example_fit,
  parameters = c("tau_y_x", "sigma_y"),
  summary = TRUE
)
#> # A tibble: 2 × 10
#>   parameter  mean      sd    q5   q95  time group category response type 
#>   <chr>     <dbl>   <dbl> <dbl> <dbl> <int> <int> <chr>    <chr>    <chr>
#> 1 tau_y_x   0.368 0.0746  0.257 0.508    NA    NA NA       y        tau  
#> 2 sigma_y   0.198 0.00397 0.192 0.205    NA    NA NA       y        sigma