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Obtain posterior draws of the pseudo type 1 receiver operating characteristic (ROC) curve.

Source: R/roc_draws.R
roc1_draws.Rd

Given a data frame and a meta-d' model, adds estimates of the cumulative probability over joint_responses. For roc1_draws and add_roc1_draws, estimates are returned in a tidy tibble with one row per posterior draw and per joint response. For roc1_rvars and add_roc1_rvars, parameters are returned as posterior::rvars, with one row per row in newdata and per joint response.

Usage

roc1_draws(object, newdata, ..., bounds = FALSE)

add_roc1_draws(newdata, object, ...)

roc1_rvars(object, newdata, ..., bounds = FALSE)

add_roc1_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

bounds

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

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 roc1_draws and add_roc1_draws, identifiers for the posterior sample

  • joint_response: the combined type 1 / type 2 response (\(J \in [1, 2K]\)) for \(K\) confidence levels)

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

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

  • p_fa: the cumulative probability of a 'present'/'old' response for stimulus==0 (\(P(J \ge j \;\vert\; S=0)\))

  • p_hit: the cumulative probability of a 'present'/'old' response for stimulus==1 (\(P(J \ge j \;\vert\; S=1)\))

See also

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

Examples

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

# compute pseudo-type 1 ROC curve
# equivalent to ``
roc1_draws(example_model, newdata)
#> # A tibble: 7,000 × 9
#> # Groups:   .row, joint_response, response, confidence [7]
#>     .row joint_response response confidence .chain .iteration .draw  p_fa p_hit
#>    <int>          <int>    <int>      <dbl>  <int>      <int> <int> <dbl> <dbl>
#>  1     1              1        0          4     NA         NA     1 0.809 0.974
#>  2     1              1        0          4     NA         NA     2 0.791 0.976
#>  3     1              1        0          4     NA         NA     3 0.829 0.979
#>  4     1              1        0          4     NA         NA     4 0.826 0.980
#>  5     1              1        0          4     NA         NA     5 0.804 0.975
#>  6     1              1        0          4     NA         NA     6 0.815 0.978
#>  7     1              1        0          4     NA         NA     7 0.817 0.978
#>  8     1              1        0          4     NA         NA     8 0.824 0.976
#>  9     1              1        0          4     NA         NA     9 0.816 0.981
#> 10     1              1        0          4     NA         NA    10 0.828 0.975
#> # ℹ 6,990 more rows
add_roc1_draws(newdata, example_model)
#> # A tibble: 7,000 × 9
#> # Groups:   .row, joint_response, response, confidence [7]
#>     .row joint_response response confidence .chain .iteration .draw  p_fa p_hit
#>    <int>          <int>    <int>      <dbl>  <int>      <int> <int> <dbl> <dbl>
#>  1     1              1        0          4     NA         NA     1 0.809 0.974
#>  2     1              1        0          4     NA         NA     2 0.791 0.976
#>  3     1              1        0          4     NA         NA     3 0.829 0.979
#>  4     1              1        0          4     NA         NA     4 0.826 0.980
#>  5     1              1        0          4     NA         NA     5 0.804 0.975
#>  6     1              1        0          4     NA         NA     6 0.815 0.978
#>  7     1              1        0          4     NA         NA     7 0.817 0.978
#>  8     1              1        0          4     NA         NA     8 0.824 0.976
#>  9     1              1        0          4     NA         NA     9 0.816 0.981
#> 10     1              1        0          4     NA         NA    10 0.828 0.975
#> # ℹ 6,990 more rows

# use posterior::rvar for additional efficiency
# equivalent to `add_roc1_draws(newdata, example_model)`
roc1_rvars(example_model, newdata)
#> # A tibble: 7 × 6
#> # Groups:   .row, joint_response, response, confidence [7]
#>    .row joint_response response confidence            p_fa          p_hit
#>   <int>          <int>    <int>      <dbl>      <rvar[1d]>     <rvar[1d]>
#> 1     1              1        0          4  0.812 ± 0.0165  0.98 ± 0.0044
#> 2     1              2        0          3  0.664 ± 0.0201  0.93 ± 0.0086
#> 3     1              3        0          2  0.484 ± 0.0211  0.84 ± 0.0141
#> 4     1              4        0          1  0.299 ± 0.0207  0.69 ± 0.0209
#> 5     1              5        1          1  0.157 ± 0.0136  0.52 ± 0.0211
#> 6     1              6        1          2  0.059 ± 0.0079  0.31 ± 0.0196
#> 7     1              7        1          3  0.019 ± 0.0039  0.16 ± 0.0160

# include the ROC bounds
# equivalent to `add_roc1_draws(newdata, example_model, bounds = TRUE)`
roc1_draws(example_model, newdata, bounds = TRUE)
#> # A tibble: 9,000 × 9
#> # Groups:   .row, joint_response, response, confidence [9]
#>     .row joint_response response confidence .chain .iteration .draw  p_fa p_hit
#>    <int>          <dbl>    <dbl>      <dbl>  <int>      <int> <int> <dbl> <dbl>
#>  1     1              0        0          5     NA         NA     1     1     1
#>  2     1              0        0          5     NA         NA     2     1     1
#>  3     1              0        0          5     NA         NA     3     1     1
#>  4     1              0        0          5     NA         NA     4     1     1
#>  5     1              0        0          5     NA         NA     5     1     1
#>  6     1              0        0          5     NA         NA     6     1     1
#>  7     1              0        0          5     NA         NA     7     1     1
#>  8     1              0        0          5     NA         NA     8     1     1
#>  9     1              0        0          5     NA         NA     9     1     1
#> 10     1              0        0          5     NA         NA    10     1     1
#> # ℹ 8,990 more rows