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Obtain posterior draws of the area under the pseudo type 1 receiver operating characteristic (ROC) curve.

Source: R/auroc1_draws.R
auroc1_draws.Rd

Given a data frame and a meta-d' model, adds estimates of the area under the type 1 ROC curve. For auroc1_draws and add_auroc1_draws, estimates are returned in a tidy tibble with one row per posterior draw. For auroc1_rvars and add_auroc1_rvars, parameters are returned as posterior::rvars, with one row per row in newdata.

Usage

auroc1_draws(
  object,
  newdata,
  ...,
  .response = "response",
  .confidence = "confidence",
  .joint_response = "joint_response"
)

add_auroc1_draws(newdata, object, ...)

auroc1_rvars(
  object,
  newdata,
  ...,
  .response = "response",
  .confidence = "confidence",
  .joint_response = "joint_response"
)

add_auroc1_rvars(newdata, object, ...)

Arguments

object

The brms model with the metad family

newdata

A data frame from which to generate posterior predictions

...

Additional parameters passed to tidybayes::epred_draws or tidybayes::epred_rvars

.response

The name of "response" column

.confidence

The name of "confidence" column

.joint_response

The name of "joint_response" column

Value

a tibble containing posterior draws of the area under the pseudo type 1 ROC with the following columns:

  • .row: the row of newdata

  • .chain, .iteration, .draw: for auroc1_draws and add_auroc1_draws, identifiers for the posterior sample

  • auroc1: the area under the pseudo type 1 ROC curve

See also

auroc1(), roc1_draws(), roc1_rvars()

Examples

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

# compute pseudo-type 1 ROC curve
# equivalent to `auroc1_draws(example_model(), newdata)`
add_auroc1_draws(newdata, example_model())
#> `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>`
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.25 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.029 seconds (Warm-up)
#> Chain 1:                0.024 seconds (Sampling)
#> Chain 1:                0.053 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.026 seconds (Warm-up)
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#> Chain 2:                0.044 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: Rejecting initial value:
#> Chain 3:   Error evaluating the log probability at the initial value.
#> Chain 3: Exception: Exception: multinomial_logit_lpmf: log-probabilities parameter[8] is -inf, but must be finite! (in 'anon_model', line 43, column 2 to line 46, column 66) (in 'anon_model', line 81, column 6 to column 185)
#> Chain 3: Rejecting initial value:
#> Chain 3:   Error evaluating the log probability at the initial value.
#> Chain 3: Exception: Exception: multinomial_logit_lpmf: log-probabilities parameter[8] is -inf, but must be finite! (in 'anon_model', line 43, column 2 to line 46, column 66) (in 'anon_model', line 81, column 6 to column 185)
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.8e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.026 seconds (Warm-up)
#> Chain 3:                0.019 seconds (Sampling)
#> Chain 3:                0.045 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: Rejecting initial value:
#> Chain 4:   Error evaluating the log probability at the initial value.
#> Chain 4: Exception: Exception: multinomial_logit_lpmf: log-probabilities parameter[7] is -inf, but must be finite! (in 'anon_model', line 43, column 2 to line 46, column 66) (in 'anon_model', line 81, column 6 to column 185)
#> Chain 4: 
#> Chain 4: Gradient evaluation took 2e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.031 seconds (Warm-up)
#> Chain 4:                0.022 seconds (Sampling)
#> Chain 4:                0.053 seconds (Total)
#> Chain 4: 
#> # A tibble: 1,000 × 3
#> # Groups:   .row [1]
#>     .row .draw auroc1
#>    <int> <int>  <dbl>
#>  1     1     1  0.754
#>  2     1     2  0.754
#>  3     1     3  0.749
#>  4     1     4  0.768
#>  5     1     5  0.742
#>  6     1     6  0.764
#>  7     1     7  0.758
#>  8     1     8  0.761
#>  9     1     9  0.777
#> 10     1    10  0.756
#> # ℹ 990 more rows

# use posterior::rvar for additional efficiency
# equivalent to `add_auroc1_draws(newdata, example_model())`
auroc1_rvars(example_model(), newdata)
#> # A tibble: 1 × 2
#>    .row        auroc1
#>   <int>    <rvar[1d]>
#> 1     1  0.77 ± 0.015
# }