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Obtain posterior draws of joint response probabilities

Source: R/epred_draws_metad.R
epred_draws_metad.Rd

Given a data frame and a meta-d' model, adds estimates of joint type 1 and type 2 response probabilities. For epred_draws_metad and add_epred_draws_metad, estimates are returned in a tidy tibble with one row per posterior draw. For epred_rvars_metad and add_epred_rvars_metad, parameters are returned as posterior::rvars, with one row per row in newdata.

Usage

epred_draws_metad(object, newdata, ...)

add_epred_draws_metad(newdata, object, ...)

epred_rvars_metad(object, newdata, ...)

add_epred_rvars_metad(newdata, object, ...)

Arguments

object

The brms model with the metad family

newdata

A data frame from which to generate posterior predictions

...

Additional arguments passed to tidybayes::add_epred_draws or tidybayes::add_epred_rvars

Value

a tibble containing posterior draws of model parameters with the following columns:

  • .row: the row of newdata

  • .chain, .iteration, .draw: for epred_draws_metad, identifiers for the posterior sample

  • stimulus, joint_response, response, confidence: identifiers for the response type

  • .epred: probability of the type 1 and type 2 response given the stimulus, \(P(R, C \;\vert\; S)\)

See also

tidybayes::epred_draws(), tidybayes::epred_rvars()

Examples

newdata <- tidyr::tibble(.row = 1)

# obtain model predictions
# equivalent to `add_epred_draws_metad(newdata, example_model)`
epred_draws_metad(example_model, newdata)
#> # A tibble: 16,000 × 9
#> # Groups:   .row, stimulus, joint_response, response, confidence [16]
#>     .row stimulus joint_response response confidence .epred .chain .iteration
#>    <int>    <int>          <int>    <int>      <dbl>  <dbl>  <int>      <int>
#>  1     1        0              1        0          4  0.191     NA         NA
#>  2     1        0              1        0          4  0.209     NA         NA
#>  3     1        0              1        0          4  0.171     NA         NA
#>  4     1        0              1        0          4  0.174     NA         NA
#>  5     1        0              1        0          4  0.196     NA         NA
#>  6     1        0              1        0          4  0.185     NA         NA
#>  7     1        0              1        0          4  0.183     NA         NA
#>  8     1        0              1        0          4  0.176     NA         NA
#>  9     1        0              1        0          4  0.184     NA         NA
#> 10     1        0              1        0          4  0.172     NA         NA
#> # ℹ 15,990 more rows
#> # ℹ 1 more variable: .draw <int>

# obtain model predictions (`posterior::rvar`)
# equivalent to `add_epred_rvars_metad(newdata, example_model)`
epred_rvars_metad(example_model, newdata)
#> # A tibble: 16 × 6
#> # Groups:   .row, stimulus, joint_response, response, confidence [16]
#>     .row stimulus joint_response response confidence          .epred
#>    <int>    <int>          <int>    <int>      <dbl>      <rvar[1d]>
#>  1     1        0              1        0          4  0.188 ± 0.0165
#>  2     1        0              2        0          3  0.147 ± 0.0142
#>  3     1        0              3        0          2  0.180 ± 0.0150
#>  4     1        0              4        0          1  0.185 ± 0.0150
#>  5     1        0              5        1          1  0.142 ± 0.0142
#>  6     1        0              6        1          2  0.098 ± 0.0095
#>  7     1        0              7        1          3  0.040 ± 0.0053
#>  8     1        0              8        1          4  0.019 ± 0.0039
#>  9     1        1              1        0          4  0.025 ± 0.0044
#> 10     1        1              2        0          3  0.043 ± 0.0058
#> 11     1        1              3        0          2  0.088 ± 0.0091
#> 12     1        1              4        0          1  0.155 ± 0.0142
#> 13     1        1              5        1          1  0.171 ± 0.0135
#> 14     1        1              6        1          2  0.207 ± 0.0150
#> 15     1        1              7        1          3  0.153 ± 0.0140
#> 16     1        1              8        1          4  0.159 ± 0.0160