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Obtain posterior draws of the area under the type 2 receiver operating characteristic (ROC) curve.

Source: R/auroc2_draws.R
auroc2_draws.Rd

Given a data frame and a meta-d' model, adds estimates of AUROC2 (optionally for each type 1 response). For auroc2_draws and add_auroc2_draws, estimates are returned in a tidy tibble with one row per posterior draw. For auroc2_rvars and add_auroc2_rvars, parameters are returned as posterior::rvars, with one row per row in newdata.

Usage

auroc2_draws(
  object,
  newdata,
  ...,
  .response = "response",
  .confidence = "confidence",
  by_response = TRUE
)

add_auroc2_draws(newdata, object, ...)

auroc2_rvars(
  object,
  newdata,
  ...,
  .response = "response",
  .confidence = "confidence",
  by_response = TRUE
)

add_auroc2_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

.response

The name of "response" column

.confidence

The name of "confidence" column

by_response

If TRUE (default), compute separate ROCs for each type 1 response. Otherwise, average ROCs across both type 1 responses.

Value

a tibble containing posterior draws of the pseudo type 1 ROC with the following columns:

  • .row: the row of newdata

  • .chain, .iteration, .draw: for auroc2_draws and add_auroc2_draws, identifiers for the posterior sample

  • {.response}: the type 1 response for perceived stimulus presence (\(R \in \{0, 1\}\))

  • {.confidence}: the type 2 confidence response (\(C \in [1, K]\))

  • p_fa2: the cumulative probability of an incorrect response (\(P(C\ge c \;\vert\; R\ne S)\))

  • p_hit2: the cumulative probability of a correct response (\(P(C\ge c \;\vert\; R = S)\))

See also

auroc2(), tidybayes::epred_draws(), tidybayes::epred_rvars()

Examples

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

# compute type 2 ROC curve
# equivalent to `add_auroc2_draws(newdata, example_model())`
auroc2_draws(example_model(), newdata)
#> # A tibble: 2,000 × 4
#> # Groups:   .row, response [2]
#>     .row response .draw auroc2
#>    <int>    <int> <int>  <dbl>
#>  1     1        0     1  0.659
#>  2     1        0     2  0.640
#>  3     1        0     3  0.656
#>  4     1        0     4  0.690
#>  5     1        0     5  0.659
#>  6     1        0     6  0.658
#>  7     1        0     7  0.671
#>  8     1        0     8  0.657
#>  9     1        0     9  0.683
#> 10     1        0    10  0.651
#> # ℹ 1,990 more rows

# use posterior::rvar for additional efficiency
# equivalent to `add_auroc2_rvars(newdata, example_model())`
auroc2_rvars(example_model(), newdata)
#> # A tibble: 2 × 3
#> # Groups:   .row [1]
#>    .row response        auroc2
#>   <int>    <int>    <rvar[1d]>
#> 1     1        0  0.67 ± 0.017
#> 2     1        1  0.66 ± 0.017
# }