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Obtain posterior draws of an index of metacognitive bias

Source: R/metacognitive_bias_draws.R
bias_draws.Rd

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, ..., by_response = TRUE)

add_metacognitive_bias_draws(newdata, object, ...)

metacognitive_bias_rvars(object, newdata, ..., 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

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

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              0.949
#>  2     1        0     NA         NA     2              0.938
#>  3     1        0     NA         NA     3              0.974
#>  4     1        0     NA         NA     4              1.08 
#>  5     1        0     NA         NA     5              1.03 
#>  6     1        0     NA         NA     6              1.04 
#>  7     1        0     NA         NA     7              0.948
#>  8     1        0     NA         NA     8              0.941
#>  9     1        0     NA         NA     9              0.969
#> 10     1        0     NA         NA    10              1.01 
#> # ℹ 1,990 more rows
# \donttest{
# 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       0.98 ± 0.042
#> 2     1        1       1.01 ± 0.047

# 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       0.99 ± 0.029
# }