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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,
  types = NULL,
  parameters = NULL,
  responses = NULL,
  times = NULL,
  groups = NULL,
  summary = FALSE,
  probs = c(0.05, 0.95),
  include_fixed = TRUE,
  ...
)

Arguments

x

[dynamitefit]
The model fit object.

row.names

Ignored.

optional

Ignored.

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. This argument is mutually exclusive with parameters.

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. This argument is mutually exclusive with types.

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. omega_alpha, and omega_psi. See also get_parameter_types().

times

[double()]
Time point(s) to keep. If NULL (the default), all time points are kept.

groups

[character()] Group name(s) to keep. If NULL (the default), all groups are kept.

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.

  • cutpoint
    Cutpoints for ordinal regression.

  • delta
    Time-varying regression coefficients.

  • nu
    Group-level random effects.

  • lambda
    Factor loadings.

  • kappa
    Contribution of the latent factor loadings in the total variation.

  • 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.

  • corr_psi
    Pairwise correlations of the noise terms 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. Note that in case of nonzero_lambda = FALSE, mean of these are used to flip the sign of psi to avoid multimodality due to sign-switching, but omega_psi variables are not modified.

  • zeta
    Total variation of latent factors, i.e., the sum of sigma_lambda and tau_psi.

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   1.96    NA NA          NA y        beta      1          1      1
#>  2 beta_y_z   1.97    NA NA          NA y        beta      2          2      1
#>  3 beta_y_z   1.96    NA NA          NA y        beta      3          3      1
#>  4 beta_y_z   1.95    NA NA          NA y        beta      4          4      1
#>  5 beta_y_z   1.96    NA NA          NA y        beta      5          5      1
#>  6 beta_y_z   1.97    NA NA          NA y        beta      6          6      1
#>  7 beta_y_z   1.96    NA NA          NA y        beta      7          7      1
#>  8 beta_y_z   1.96    NA NA          NA y        beta      8          8      1
#>  9 beta_y_z   1.97    NA NA          NA y        beta      9          9      1
#> 10 beta_y_z   1.95    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 0.0112  1.95  1.98    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  0.0299 -0.214 -0.110     2    NA NA       y        delta
#>  3 delta_y_x -0.909  0.0351 -0.966 -0.852     3    NA NA       y        delta
#>  4 delta_y_x -0.882  0.0253 -0.924 -0.839     4    NA NA       y        delta
#>  5 delta_y_x -0.916  0.0217 -0.959 -0.884     5    NA NA       y        delta
#>  6 delta_y_x -1.06   0.0202 -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 -0.997  0.0226 -1.04  -0.962     8    NA NA       y        delta
#>  9 delta_y_x -0.902  0.0248 -0.942 -0.864     9    NA NA       y        delta
#> 10 delta_y_x -1.01   0.0223 -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   0.364 0.0770  0.253 0.497    NA    NA NA       y        tau  
#> 2 sigma_y   0.198 0.00404 0.192 0.205    NA    NA NA       y        sigma