Calculate the area under empirical type 2 receiver operating characteristic curves

Calculate the area under empirical type 2 receiver operating characteristic curves

Usage

auroc2(
  data,
  ...,
  .stimulus = "stimulus",
  .response = "response",
  .confidence = "confidence",
  .joint_response = "joint_response",
  K = NULL,
  by_response = TRUE
)

Arguments

data

The data frame to aggregate

...

Grouping columns in data. These columns will be converted to factors.

.stimulus

The name of "stimulus" column

.response

The name of "response" column

.confidence

The name of "confidence" column

.joint_response

The name of "joint_response" column

K

The number of confidence levels in data. If NULL, this is estimated from data using the maximum value of either the confidence column or joint response column.

by_response

If TRUE (default), calculate type 2 ROCs conditional on type 1 response.

Value

A tibble with columns:

• ...: the grouping columns in data

• {.response} (if by_response=TRUE): the type 1 response

• auroc2: the area under the type 2 ROC

See also

roc2(), auroc2_draws(), auroc2_rvars()

Examples

# calculate type 2 ROCs by stimulus
auroc2(example_data())
#> `hmetad` has inferred that there are K=4 confidence levels in the data. If this is incorrect, please set this manually using the argument `K=<K>`
#> # A tibble: 2 × 2
#>   response auroc2
#>      <int>  <dbl>
#> 1        0  0.661
#> 2        1  0.674

# calculate type 2 ROCs by condition, averaging over type 1 responses
auroc2(sim_metad_condition(), condition, by_response = FALSE)
#> `hmetad` has inferred that there are K=4 confidence levels in the data. If this is incorrect, please set this manually using the argument `K=<K>`
#> # A tibble: 2 × 2
#>   condition auroc2
#>       <int>  <dbl>
#> 1         1  0.701
#> 2         2  0.710