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Obtain posterior draws of the response-specific type 2 receiver operating characteristic (ROC) curves.

Source: R/roc2_draws.R
roc2_draws.Rd

Given a data frame and a meta-d' model, adds estimates of the cumulative probability over confidence for each type 1 response. For roc2_draws and add_roc2_draws, estimates are returned in a tidy tibble with one row per posterior draw and per joint response. For roc2_rvars and add_roc2_rvars, parameters are returned as posterior::rvars, with one row per row in newdata and per joint response.

Usage

roc2_draws(
  object,
  newdata,
  ...,
  .response = "response",
  .confidence = "confidence",
  bounds = FALSE,
  by_response = TRUE
)

add_roc2_draws(newdata, object, ...)

roc2_rvars(
  object,
  newdata,
  ...,
  .response = "response",
  .confidence = "confidence",
  bounds = FALSE,
  by_response = TRUE
)

add_roc2_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

bounds

If TRUE, include the endpoints of the ROC at \((0, 0)\) and \((1, 1)\). Otherwise, the endpoints are excluded.

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 roc2_draws and add_roc2_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

roc2(), tidybayes::epred_draws(), tidybayes::epred_rvars()

Examples

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

# compute type 2 ROC curve
# equivalent to `add_roc2_draws(newdata, example_model())`
roc2_draws(example_model(), newdata)
#> # A tibble: 6,000 × 8
#> # Groups:   .row, response, confidence [6]
#>     .row response confidence .chain .iteration .draw p_hit2  p_fa2
#>    <int>    <int>      <int>  <int>      <int> <int>  <dbl>  <dbl>
#>  1     1        0          3     NA         NA     1  0.180 0.0459
#>  2     1        0          3     NA         NA     2  0.210 0.0706
#>  3     1        0          3     NA         NA     3  0.190 0.0517
#>  4     1        0          3     NA         NA     4  0.202 0.0380
#>  5     1        0          3     NA         NA     5  0.194 0.0515
#>  6     1        0          3     NA         NA     6  0.215 0.0612
#>  7     1        0          3     NA         NA     7  0.197 0.0462
#>  8     1        0          3     NA         NA     8  0.187 0.0498
#>  9     1        0          3     NA         NA     9  0.222 0.0494
#> 10     1        0          3     NA         NA    10  0.193 0.0563
#> # ℹ 5,990 more rows

# use posterior::rvar for additional efficiency
# equivalent to `add_roc2_rvars(newdata, example_model())`
roc2_rvars(example_model(), newdata)
#> # A tibble: 6 × 5
#> # Groups:   .row, response, confidence [6]
#>    .row response confidence        p_hit2          p_fa2
#>   <int>    <int>      <int>    <rvar[1d]>     <rvar[1d]>
#> 1     1        0          1  0.75 ± 0.020  0.526 ± 0.032
#> 2     1        0          2  0.47 ± 0.024  0.216 ± 0.024
#> 3     1        0          3  0.21 ± 0.021  0.053 ± 0.011
#> 4     1        1          1  0.74 ± 0.019  0.515 ± 0.030
#> 5     1        1          2  0.45 ± 0.024  0.197 ± 0.023
#> 6     1        1          3  0.22 ± 0.021  0.058 ± 0.011

# include the ROC bounds
# equivalent to `roc2_draws(newdata, example_model(), bounds = TRUE)`
roc2_draws(example_model(), newdata, bounds = TRUE)
#> # A tibble: 10,000 × 8
#> # Groups:   .row, response, confidence [10]
#>     .row response confidence .chain .iteration .draw p_hit2  p_fa2
#>    <int>    <int>      <int>  <int>      <int> <int>  <dbl>  <dbl>
#>  1     1        0          3     NA         NA     1  0.180 0.0459
#>  2     1        0          3     NA         NA     2  0.210 0.0706
#>  3     1        0          3     NA         NA     3  0.190 0.0517
#>  4     1        0          3     NA         NA     4  0.202 0.0380
#>  5     1        0          3     NA         NA     5  0.194 0.0515
#>  6     1        0          3     NA         NA     6  0.215 0.0612
#>  7     1        0          3     NA         NA     7  0.197 0.0462
#>  8     1        0          3     NA         NA     8  0.187 0.0498
#>  9     1        0          3     NA         NA     9  0.222 0.0494
#> 10     1        0          3     NA         NA    10  0.193 0.0563
#> # ℹ 9,990 more rows
# }