Computes posterior mean confidence conditional on stimulus and
response (\(\mathbb{E}[C \;\vert\; S=s,R=r]\)), stimulus (averaging over
responses, \(\mathbb{E}[C \;\vert\; S=s]\)), response (averaging over
stimuli, \(\mathbb{E}[C \;\vert\; R=r]\)), neither (averaging over
stimuli and responses, \(\mathbb{E}[C]\)), or accuracy (\(\mathbb{E}[C
\;\vert\; A=(r=s)]\)). For mean_confidence_draws and
add_mean_confidence_draws, estimates are returned in a tidy tibble with
one row per posterior draw, stimulus, and response. For
mean_confidence_rvars and add_mean_confidence_rvars, estimates are
returned as posterior::rvars, with one row per row in newdata.
add_mean_confidence_draws is an alias of mean_confidence_draws with
argument order swapped.
Usage
mean_confidence_draws(
object,
newdata,
...,
.stimulus = "stimulus",
.response = "response",
by_stimulus = TRUE,
by_response = TRUE,
by_correct = FALSE
)
add_mean_confidence_draws(newdata, object, ...)
mean_confidence_rvars(
object,
newdata,
...,
.stimulus = "stimulus",
.response = "response",
by_stimulus = TRUE,
by_response = TRUE,
by_correct = FALSE
)
add_mean_confidence_rvars(newdata, object, ...)Arguments
- object
The
brmsmodel with themetadfamily- newdata
A data frame from which to generate posterior predictions
- ...
Additional arguments to tidybayes::epred_draws or tidybayes::epred_rvars
- .stimulus
The name of "stimulus" column
- .response
The name of "response" column
- by_stimulus
If TRUE, predict mean confidence separately by stimulus. Otherwise, predict mean confidence averaging over stimuli. Ignored if
by_correct==TRUE.- by_response
If TRUE, predict mean confidence separately by response Otherwise, predict mean confidence averaging over responses. Ignored if
by_correct==TRUE.- by_correct
If TRUE, predict mean confidence separately for correct and incorrect responses.
Value
a tibble containing posterior draws of mean confidence with the following columns:
.row: the row ofnewdata.chain,.iteration,.draw: formean_confidence_drawsandadd_mean_confidence_draws, identifiers for the posterior sample{.stimulus}: indicator for stimulus presence (ifby_stimulus==TRUE & by_correct==FALSE){.response}: indicator for type 1 response (ifby_response==TRUE & by_correct==FALSE)correct: indicator for the accuracy of the type 1 response (ifby_correct==TRUE).epred: the predicted mean confidence
Examples
# \donttest{
newdata <- tidyr::tibble(.row = 1)
# compute mean confidence by stimulus and response
# equivalent to `add_mean_confidence_draws(newdata, example_model())`
mean_confidence_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 2.38
#> 2 1 0 0 2 2.40
#> 3 1 0 0 3 2.41
#> 4 1 0 0 4 2.47
#> 5 1 0 0 5 2.40
#> 6 1 0 0 6 2.41
#> 7 1 0 0 7 2.45
#> 8 1 0 0 8 2.39
#> 9 1 0 0 9 2.46
#> 10 1 0 0 10 2.37
#> # ℹ 3,990 more rows
# compute mean confidence by stimulus
# equivalent to `add_mean_confidence_draws(newdata, example_model(), by_response = FALSE)`
mean_confidence_draws(example_model(), newdata, by_response = FALSE)
#> # A tibble: 2,000 × 4
#> # Groups: .row, stimulus [2]
#> .row stimulus .draw .epred
#> <int> <int> <int> <dbl>
#> 1 1 0 1 2.20
#> 2 1 0 2 2.21
#> 3 1 0 3 2.22
#> 4 1 0 4 2.25
#> 5 1 0 5 2.19
#> 6 1 0 6 2.21
#> 7 1 0 7 2.25
#> 8 1 0 8 2.20
#> 9 1 0 9 2.27
#> 10 1 0 10 2.22
#> # ℹ 1,990 more rows
# compute mean confidence by response
# equivalent to `add_mean_confidence_draws(newdata, example_model(), by_stimulus = FALSE)`
mean_confidence_draws(example_model(), newdata, by_stimulus = FALSE)
#> # A tibble: 2,000 × 4
#> # Groups: .row, response [2]
#> .row response .draw .epred
#> <int> <int> <int> <dbl>
#> 1 1 0 1 2.18
#> 2 1 0 2 2.24
#> 3 1 0 3 2.22
#> 4 1 0 4 2.25
#> 5 1 0 5 2.20
#> 6 1 0 6 2.22
#> 7 1 0 7 2.25
#> 8 1 0 8 2.21
#> 9 1 0 9 2.24
#> 10 1 0 10 2.19
#> # ℹ 1,990 more rows
# compute mean confidence by accuracy
# equivalent to `add_mean_confidence_draws(newdata, example_model(), by_correct = TRUE)`
mean_confidence_draws(example_model(), newdata, by_correct = TRUE)
#> # A tibble: 2,000 × 4
#> # Groups: .row, correct [2]
#> .row correct .draw .epred
#> <int> <int> <int> <dbl>
#> 1 1 0 1 1.77
#> 2 1 0 2 1.82
#> 3 1 0 3 1.79
#> 4 1 0 4 1.75
#> 5 1 0 5 1.78
#> 6 1 0 6 1.76
#> 7 1 0 7 1.81
#> 8 1 0 8 1.77
#> 9 1 0 9 1.76
#> 10 1 0 10 1.81
#> # ℹ 1,990 more rows
# compute mean confidence averaging over stimuli and responses
# equivalent to `add_mean_confidence_draws(newdata, example_model(), ...)`
mean_confidence_draws(example_model(), newdata, by_stimulus = FALSE, by_response = FALSE)
#> # A tibble: 1,000 × 3
#> # Groups: .row [1]
#> .row .draw .epred
#> <int> <int> <dbl>
#> 1 1 1 2.18
#> 2 1 2 2.20
#> 3 1 3 2.19
#> 4 1 4 2.25
#> 5 1 5 2.19
#> 6 1 6 2.19
#> 7 1 7 2.25
#> 8 1 8 2.19
#> 9 1 9 2.26
#> 10 1 10 2.20
#> # ℹ 990 more rows
# use `posterior::rvar` for increased efficiency
# equivalent to `add_mean_confidence_rvars(newdata, example_model())`
mean_confidence_rvars(example_model(), newdata)
#> # A tibble: 4 × 4
#> # Groups: .row, stimulus, response [4]
#> .row stimulus response .epred
#> <int> <int> <int> <rvar[1d]>
#> 1 1 0 0 2.4 ± 0.053
#> 2 1 0 1 1.8 ± 0.056
#> 3 1 1 0 1.8 ± 0.059
#> 4 1 1 1 2.4 ± 0.053
# }