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Provides a data.table representation of the posterior samples of the model parameters. See as.data.frame.dynamitefit() for details.

Usage

# S3 method for dynamitefit
as.data.table(
  x,
  keep.rownames = FALSE,
  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.

keep.rownames

[logical(1)]
Not used.

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 data.table containing either samples or summary statistics of the model parameters.

Examples

data.table::setDTthreads(1) # For CRAN
as.data.table(
  gaussian_example_fit,
  responses = "y",
  types = "beta",
  summary = FALSE
)
#>      parameter    value  time category group response   type .draw .iteration
#>         <char>    <num> <int>   <char> <int>   <char> <char> <int>      <int>
#>   1:  beta_y_z 1.958289    NA     <NA>    NA        y   beta     1          1
#>   2:  beta_y_z 1.972815    NA     <NA>    NA        y   beta     2          2
#>   3:  beta_y_z 1.956837    NA     <NA>    NA        y   beta     3          3
#>   4:  beta_y_z 1.949148    NA     <NA>    NA        y   beta     4          4
#>   5:  beta_y_z 1.962168    NA     <NA>    NA        y   beta     5          5
#>  ---                                                                         
#> 196:  beta_y_z 1.960357    NA     <NA>    NA        y   beta   196         96
#> 197:  beta_y_z 1.967019    NA     <NA>    NA        y   beta   197         97
#> 198:  beta_y_z 1.968887    NA     <NA>    NA        y   beta   198         98
#> 199:  beta_y_z 1.960676    NA     <NA>    NA        y   beta   199         99
#> 200:  beta_y_z 1.961960    NA     <NA>    NA        y   beta   200        100
#>      .chain
#>       <int>
#>   1:      1
#>   2:      1
#>   3:      1
#>   4:      1
#>   5:      1
#>  ---       
#> 196:      2
#> 197:      2
#> 198:      2
#> 199:      2
#> 200:      2