Calculate posterior draws of type 2 response probabilities

Given a data frame and a meta-d' model, adds estimates of type 2 response probabilities (i.e., \(P(C=c \vert S=s, R=r)\), \(P(C=c \vert S=s)\), \(P(C=c \vert R=r)\) or \(P(C=c)\) for stimulus \(S\)), type 1 response \(R\), and type 2 response \(C\). For type2_draws_metad and add_type2_draws_metad, estimates are returned in a tidy tibble with one row per posterior draw. For type2_rvars_metad and add_type2_rvars_metad, parameters are returned as posterior::rvars, with one row per row in newdata.

Usage

type2_draws(
  object,
  newdata,
  ...,
  .stimulus = "stimulus",
  .response = "response",
  .confidence = "confidence",
  by_stimulus = TRUE,
  by_response = TRUE,
  by_correct = FALSE
)

add_type2_draws(newdata, object, ...)

type2_rvars(
  object,
  newdata,
  ...,
  .stimulus = "stimulus",
  .response = "response",
  .confidence = "confidence",
  by_stimulus = TRUE,
  by_response = TRUE,
  by_correct = FALSE
)

add_type2_rvars(newdata, object, ...)

Arguments

object

The brms model with the metad family

newdata

A data frame from which to generate posterior predictions

...

Additional arguments passed to tidybayes::add_epred_draws or tidybayes::add_epred_rvars

.stimulus

The name of "stimulus" column

.response

The name of "response" column

.confidence

The name of "confidence" column

by_stimulus

If TRUE (default), calculate type 2 response probabilities separately by stimulus. Otherwise, calculate unconditional type 2 response probabilities as an unweighted average over stimuli.

by_response

If TRUE (default), calculate type 2 response probabilities separately by type 1 response. Otherwise, calculate unconditional type 2 response probabilities as an unweighted average over type 1 responses.

by_correct

If FALSE (default), calculate type 2 response probabilities conditional on stimulus and/or type 1 response. If TRUE, instead calculate probabilities conditional on accuracy.

Value

a tibble containing posterior draws of model parameters with the following columns:

• .row: the row of newdata

• .chain, .iteration, .draw: for epred_draws_metad, identifiers for the posterior sample

• {.stimulus}, {.response}, {.confidence}: identifiers for the response type

• .epred: probability of the type 1 and type 2 response given the stimulus, \(P(R, C \;\vert\; S)\)

See also

type2_probabilities()

Examples

# \donttest{
newdata <- tidyr::tibble(.row = 1)

# obtain model predictions
# equivalent to `add_type2_draws(newdata, example_model())`
type2_draws(example_model(), newdata)
#> # A tibble: 16,000 × 6
#> # Groups:   .row, stimulus, response, confidence [16]
#>     .row stimulus response .draw confidence .epred
#>    <int>    <int>    <int> <int>      <int>  <dbl>
#>  1     1        0        0     1          1  0.259
#>  2     1        0        0     1          2  0.288
#>  3     1        0        0     1          3  0.273
#>  4     1        0        0     1          4  0.180
#>  5     1        0        0     2          1  0.265
#>  6     1        0        0     2          2  0.276
#>  7     1        0        0     2          3  0.249
#>  8     1        0        0     2          4  0.210
#>  9     1        0        0     3          1  0.250
#> 10     1        0        0     3          2  0.282
#> # ℹ 15,990 more rows

# obtain model predictions (`posterior::rvar`)
# equivalent to `add_type2_rvars(newdata, example_model(), by_stimulus = FALSE)`
type2_rvars(example_model(), newdata, by_stimulus = FALSE)
#> # A tibble: 8 × 4
#> # Groups:   .row, response [2]
#>    .row response confidence        .epred
#>   <int>    <int>      <int>    <rvar[1d]>
#> 1     1        0          1  0.32 ± 0.021
#> 2     1        0          2  0.29 ± 0.021
#> 3     1        0          3  0.24 ± 0.019
#> 4     1        0          4  0.16 ± 0.016
#> 5     1        1          1  0.33 ± 0.020
#> 6     1        1          2  0.30 ± 0.020
#> 7     1        1          3  0.20 ± 0.018
#> 8     1        1          4  0.17 ± 0.016
# }