Calculate posterior draws of type 1 response probabilities

Given a data frame and a meta-d' model, adds estimates of type 1 response probabilities (i.e., \(P(R=r \vert S=s)\) or \(P(R=r)\) for type 1 response \(R\) and stimulus \(S\)). For type1_draws_metad and add_type1_draws_metad, estimates are returned in a tidy tibble with one row per posterior draw. For type1_rvars_metad and add_type1_rvars_metad, parameters are returned as posterior::rvars, with one row per row in newdata.

Usage

type1_draws(
  object,
  newdata,
  ...,
  .stimulus = "stimulus",
  .response = "response",
  .confidence = "confidence",
  .joint_response = "joint_response",
  by_stimulus = TRUE
)

add_type1_draws(newdata, object, ...)

type1_rvars(
  object,
  newdata,
  ...,
  .stimulus = "stimulus",
  .response = "response",
  .confidence = "confidence",
  .joint_response = "joint_response",
  by_stimulus = TRUE
)

add_type1_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

.joint_response

The name of "joint_response" column

by_stimulus

If TRUE (default), calculate conditional type 1 response probabilities \(P(R=r \vert S=s)\). Otherwise, calculate unconditional response probabilities \(P(R=r)\) as an unweighted average over stimuli.

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}: identifiers for the response type

• .epred: probability of the type 1 response (optionally given the stimulus)

See also

type1_probabilities()

Examples

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

# obtain model predictions
# equivalent to `add_type1_draws(newdata, example_model())`
type1_draws(example_model(), newdata)
#> # A tibble: 4,000 × 5
#> # Groups:   .row, stimulus, response [4]
#>     .row stimulus response .draw .epred
#>    <int>    <int>    <int> <int>  <dbl>
#>  1     1        0        0     1  0.714
#>  2     1        0        0     2  0.691
#>  3     1        0        0     3  0.708
#>  4     1        0        0     4  0.693
#>  5     1        0        0     5  0.677
#>  6     1        0        0     6  0.711
#>  7     1        0        0     7  0.683
#>  8     1        0        0     8  0.702
#>  9     1        0        0     9  0.723
#> 10     1        0        0    10  0.722
#> # ℹ 3,990 more rows

# obtain model predictions (`posterior::rvar`)
# equivalent to `add_type1_rvars(newdata, example_model(), by_stimulus = FALSE)`
type1_rvars(example_model(), newdata, by_stimulus = FALSE)
#> # A tibble: 2 × 3
#> # Groups:   .row, response [2]
#>    .row response        .epred
#>   <int>    <int>    <rvar[1d]>
#> 1     1        0  0.51 ± 0.015
#> 2     1        1  0.49 ± 0.015
# }