Obtain posterior draws of an index of metacognitive bias

Computes \(\textrm{meta-}\Delta\), an index of metacognitive bias. \(\textrm{meta-}\Delta\) is the distance between meta_c and the average of the the confidence criteria meta_c2_0 and meta_c2_1. For metacognitive_bias_draws and add_metacognitive_bias_draws, parameters are returned in a tidy tibble with one row per posterior draw and per response. For metacognitive_bias_rvars and add_metacognitive_bias_rvars, parameters are returned as posterior::rvars, with one row per row in newdata and per response.

Usage

metacognitive_bias_draws(
  object,
  newdata,
  ...,
  .response = "response",
  by_response = TRUE
)

add_metacognitive_bias_draws(newdata, object, ...)

metacognitive_bias_rvars(
  object,
  newdata,
  ...,
  .response = "response",
  by_response = TRUE
)

add_metacognitive_bias_rvars(newdata, object, ...)

Arguments

object

The brms model with the metad family

newdata

A data frame from which to generate posterior predictions

...

Additional parameters passed to tidybayes::epred_draws or tidybayes::epred_rvars

.response

The name of "response" column

by_response

If TRUE, compute metacognitive bias separately for the two type 1 responses. If FALSE, compute an un-weighted average of the two measures.

Value

a tibble containing posterior draws of \(\textrm{meta-}\Delta\) with the following columns:

• .row: the row of newdata

• .chain, .iteration, .draw: for metacognitive_bias_draws and add_metacognitive_bias_draws, identifiers for the posterior sample

• {.response}: the type 1 response for perceived stimulus presence

• metacognitive_bias: the distance between meta_c and the average of the confidence criteria meta_c2_{response}.

See also

tidybayes::linpred_draws(), tidybayes::linpred_rvars()

Examples

# \donttest{
newdata <- tidyr::tibble(.row = 1)

# compute metacognitive bias
# equivalent to `add_metacognitive_bias_draws(newdata, example_model())`
metacognitive_bias_draws(example_model(), newdata)
#> # A tibble: 2,000 × 6
#> # Groups:   .row, response [2]
#>     .row response .chain .iteration .draw metacognitive_bias
#>    <int>    <int>  <int>      <int> <int>              <dbl>
#>  1     1        0     NA         NA     1              1.09 
#>  2     1        0     NA         NA     2              0.983
#>  3     1        0     NA         NA     3              1.05 
#>  4     1        0     NA         NA     4              1.05 
#>  5     1        0     NA         NA     5              1.04 
#>  6     1        0     NA         NA     6              1.03 
#>  7     1        0     NA         NA     7              1.02 
#>  8     1        0     NA         NA     8              1.05 
#>  9     1        0     NA         NA     9              1.06 
#> 10     1        0     NA         NA    10              1.07 
#> # ℹ 1,990 more rows

# use `posterior::rvar` for increased efficiency
# equivalent to `add_metacognitive_bias_rvars(newdata, example_model())`
metacognitive_bias_rvars(example_model(), newdata)
#> # A tibble: 2 × 3
#> # Groups:   .row, response [2]
#>    .row response metacognitive_bias
#>   <dbl>    <int>         <rvar[1d]>
#> 1     1        0          1 ± 0.044
#> 2     1        1          1 ± 0.043

# average over the two type 1 responses
metacognitive_bias_rvars(example_model(), newdata, by_response = FALSE)
#> # A tibble: 1 × 2
#> # Groups:   .row [1]
#>    .row metacognitive_bias
#>   <dbl>         <rvar[1d]>
#> 1     1          1 ± 0.028
# }