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Calculate empirical pseudo-type 1 receiver operating characteristic curves

Source: R/roc1_draws.R
roc1.Rd

Given a dataset data, determine the cumulative probability of each combination of type 1 and type 2 responses conditional on stimulus.

Usage

roc1(
  data,
  ...,
  .stimulus = "stimulus",
  .response = "response",
  .confidence = "confidence",
  .joint_response = "joint_response",
  K = NULL,
  bounds = FALSE
)

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.

Value

A tibble with columns:

  • ...: the grouping columns in data

  • {.response}: the type 1 response

  • {.confidence}: the type 2 response

  • {.joint_response}: the joint type 1/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: where stimulus=0, the proportion of rows in data with joint response equal to .joint_response

  • p_1: where stimulus=1, the proportion of rows in data with joint response equal to .joint_response

  • p_fa: where stimulus=0, the proportion of rows in data with joint response greater than .joint_response

  • p_hit: where stimulus=1, the proportion of rows in data with joint response greater than .joint_response

See also

roc1_draws(), roc1_rvars()

Examples

# calculate type 1 ROCs
roc1(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: 7 × 9
#>   response confidence joint_response   n_0   n_1   p_0   p_1   p_fa p_hit
#>      <int>      <int>          <int> <int> <int> <dbl> <dbl>  <dbl> <dbl>
#> 1        0          4              1    71    10 0.142 0.02  0.858  0.98 
#> 2        0          3              2    94    26 0.188 0.052 0.67   0.928
#> 3        0          2              3   101    46 0.202 0.092 0.468  0.836
#> 4        0          1              4    86    75 0.172 0.15  0.296  0.686
#> 5        1          1              5    74    86 0.148 0.172 0.148  0.514
#> 6        1          2              6    44   104 0.088 0.208 0.0600 0.306
#> 7        1          3              7    24    75 0.048 0.15  0.0120 0.156

# calculate type 1 ROCs by condition
roc1(sim_metad_condition(), condition)
#> `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: 14 × 10
#> # Groups:   condition [2]
#>    condition response confidence joint_response   n_0   n_1   p_0   p_1   p_fa
#>        <int>    <int>      <int>          <int> <int> <int> <dbl> <dbl>  <dbl>
#>  1         1        0          4              1    13     2  0.26  0.04 0.74  
#>  2         1        0          3              2     6     2  0.12  0.04 0.62  
#>  3         1        0          2              3    10     3  0.2   0.06 0.42  
#>  4         1        0          1              4     5     6  0.1   0.12 0.32  
#>  5         1        1          1              5     8    11  0.16  0.22 0.16  
#>  6         1        1          2              6     4     9  0.08  0.18 0.0800
#>  7         1        1          3              7     1     8  0.02  0.16 0.0600
#>  8         2        0          4              1     9     2  0.18  0.04 0.82  
#>  9         2        0          3              2    10     7  0.2   0.14 0.62  
#> 10         2        0          2              3     6     5  0.12  0.1  0.5   
#> 11         2        0          1              4     8     1  0.16  0.02 0.34  
#> 12         2        1          1              5    13    10  0.26  0.2  0.0800
#> 13         2        1          2              6     2    10  0.04  0.2  0.0400
#> 14         2        1          3              7     2     8  0.04  0.16 0     
#> # ℹ 1 more variable: p_hit <dbl>