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Obtain posterior predictions of joint responses

Source: R/predicted_draws_metad.R
predicted_draws_metad.Rd

Given a data frame and a meta-d' model, adds predictions of joint type 1 and type 2 responses For predicted_draws_metad and add_predicted_draws_metad, predictions are returned in a tidy tibble with one row per posterior draw. For predicted_rvars_metad and add_predicted_rvars_metad, parameters are returned as posterior::rvars, with one row per row in newdata.

Usage

predicted_draws_metad(object, newdata, ...)

add_predicted_draws_metad(newdata, object, ...)

predicted_rvars_metad(object, newdata, ...)

add_predicted_rvars_metad(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_predicted_draws or tidybayes::add_predicted_rvars

Value

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

  • .row: the row of newdata

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

  • stimulus, joint_response, response, confidence: identifiers for the response type

  • .prediction: predicted type 1 and type 2 responses given the stimulus

See also

tidybayes::predicted_draws(), tidybayes::predicted_rvars()

Examples

newdata <- aggregate_metad(example_data)

# obtain model predictions
# equivalent to `add_predicted_draws_metad(newdata, example_model)`
predicted_draws_metad(example_model, newdata)

# obtain model predictions (posterior::rvar)
# equivalent to `add_predicted_rvars_metad(newdata, example_model)`
predicted_rvars_metad(example_model, newdata)
#> # A tibble: 16 × 9
#> # Groups:   .row, N_0, N_1, N, stimulus, joint_response, response, confidence
#> #   [16]
#>     .row   N_0   N_1 N[,"N_0_1"] stimulus joint_response response confidence
#>    <int> <int> <int>       <int>    <int>          <int>    <int>      <dbl>
#>  1     1   500   500          89        0              1        0          4
#>  2     1   500   500          89        0              2        0          3
#>  3     1   500   500          89        0              3        0          2
#>  4     1   500   500          89        0              4        0          1
#>  5     1   500   500          89        0              5        1          1
#>  6     1   500   500          89        0              6        1          2
#>  7     1   500   500          89        0              7        1          3
#>  8     1   500   500          89        0              8        1          4
#>  9     1   500   500          89        1              1        0          4
#> 10     1   500   500          89        1              2        0          3
#> 11     1   500   500          89        1              3        0          2
#> 12     1   500   500          89        1              4        0          1
#> 13     1   500   500          89        1              5        1          1
#> 14     1   500   500          89        1              6        1          2
#> 15     1   500   500          89        1              7        1          3
#> 16     1   500   500          89        1              8        1          4
#> # ℹ 2 more variables: N[2:16] <int>, .prediction <rvar[1d]>