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Calculate empirical mean confidence

Source: R/mean_confidence_draws.R
mean_confidence.Rd

Given a dataset data, determine the mean confidence rating, optionally conditional on stimulus and/or type 1 response.

Usage

mean_confidence(
  data,
  ...,
  .stimulus = "stimulus",
  .response = "response",
  .confidence = "confidence",
  .joint_response = "joint_response",
  K = NULL,
  by_stimulus = TRUE,
  by_response = TRUE,
  by_correct = 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.

by_stimulus

If TRUE (default), calculate mean confidence conditional on stimulus. Ignored if by_correct=TRUE.

by_response

If TRUE (default), calculate mean confidence conditional on type 2 response. Ignored if by_correct=TRUE.

by_correct

If FALSE (default), calculate mean confidence conditional on stimulus and/or type 1 response. If TRUE, instead calculate mean confidence conditional on accuracy.

Value

A tibble with columns:

  • ...: the grouping columns in data

  • {.stimulus}: the stimulus (if by_stimulus=TRUE)

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

  • correct: the accuracy (if by_correct=TRUE)

  • mean_confidence: the mean confidence rating

See also

mean_confidence_draws(), mean_confidence_rvars()

Examples

# calculate mean confidence by stimulus and response
mean_confidence(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: 4 × 3
#> # Groups:   stimulus [2]
#>   stimulus response mean_confidence
#>      <int>    <int>           <dbl>
#> 1        0        0            2.43
#> 2        0        1            1.74
#> 3        1        0            1.82
#> 4        1        1            2.42

# calculate mean confidence by accuracy
mean_confidence(example_data(), by_correct = TRUE)
#> `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
#>   correct mean_confidence
#>     <int>           <dbl>
#> 1       0            1.78
#> 2       1            2.42

# calculate mean confidence by condition, averaging over type 1 responses
mean_confidence(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: 4 × 3
#> # Groups:   condition [2]
#>   condition stimulus mean_confidence
#>       <int>    <int>           <dbl>
#> 1         1        0            2.14
#> 2         1        1            2.06
#> 3         2        0            2.06
#> 4         2        1            2.32