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Calculate empirical type 2 receiver operating characteristic curve

Source: R/roc2_draws.R
roc2.Rd

Given a dataset data, determine the cumulative probability of each type 2 responses conditional on accuracy, optionally conditional on type 1 response.

Usage

roc2(
  data,
  ...,
  .stimulus = "stimulus",
  .response = "response",
  .confidence = "confidence",
  .joint_response = "joint_response",
  K = NULL,
  bounds = FALSE,
  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.

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), 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

  • {.confidence}: the type 2 response

  • n_0: the number of rows in data with stimulus=0 and the corresponding joint_response

  • n_1: the number of rows in data with stimulus=1 and the corresponding joint_response

  • p_0: for incorrect trials, the proportion of rows in data with confidence equal to confidence

  • p_1: for correct trials the proportion of rows in data with confidence equal to confidence

  • p_fa2: for incorrect trials, the proportion of rows in data with confidence greater than confidence

  • p_hit2: for correct trials, the proportion of rows in data with confidence greater than confidence

See also

roc2_draws(), roc2_rvars()

Examples

# calculate type 2 ROCs by stimulus
roc2(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: 6 × 8
#> # Groups:   response [2]
#>   response confidence   n_0   n_1   p_0   p_1 p_hit2  p_fa2
#>      <int>      <int> <int> <int> <dbl> <dbl>  <dbl>  <dbl>
#> 1        0          1    75    86 0.15  0.172  0.756 0.522 
#> 2        0          2    46   101 0.092 0.202  0.469 0.229 
#> 3        0          3    26    94 0.052 0.188  0.202 0.0637
#> 4        1          1    74    86 0.148 0.172  0.749 0.5   
#> 5        1          2    44   104 0.088 0.208  0.446 0.203 
#> 6        1          3    24    75 0.048 0.15   0.227 0.0405

# calculate type 2 ROCs by condition, averaging over type 1 responses
roc2(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: 6 × 8
#> # Groups:   condition [2]
#>   condition confidence   n_0   n_1   p_0   p_1  p_fa2 p_hit2
#>       <int>      <int> <int> <int> <dbl> <dbl>  <dbl>  <dbl>
#> 1         1          1    14    19  0.14  0.19 0.555   0.726
#> 2         1          2     7    13  0.07  0.13 0.309   0.534
#> 3         1          3     6    17  0.06  0.17 0.121   0.290
#> 4         2          1    11    18  0.11  0.18 0.599   0.754
#> 5         2          2    10    20  0.1   0.2  0.202   0.479
#> 6         2          3     5    20  0.05  0.2  0.0312  0.206