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hmetad

The hmetad package is designed to fit the meta-d’ model for confidence ratings (Maniscalco & Lau, 2012, 2014). The hmetad package uses a Bayesian modeling approach, building on and superseding previous development of the Hmeta-d toolbox (Fleming, 2017). A key advance is implementation as a custom family in the brms package, which itself provides a friendly interface to the probabilistic programming language Stan.

This provides major benefits:

Model designs can be specified as simple R formulas
Support for complex model designs (e.g., multilevel models, distributional models, multivariate models)
Interfaces to other packages surrounding brms (e.g., tidybayes, ggdist, bayesplot, loo, posterior, bridgesampling, bayestestR)
Computation of model-implied quantities (e.g., mean confidence, type 1 and type 2 receiver operating characteristic curves)
Derivation of novel metacognitive bias metrics, and well as established metrics of metacognitive efficiency
Increased sampling efficiency and better convergence diagnostics

Installation

hmetad is available via CRAN and can be installed using:

install.packages("hmetad")

Alternatively, you can install the development version of hmetad from GitHub with:

# install.packages("pak")
pak::pak("metacoglab/hmetad")

Quick setup

Let’s say you have some data from a binary decision task with ordinal confidence ratings:

#> # A tibble: 1,000 × 5
#>    trial stimulus response correct confidence
#>    <int>    <int>    <int>   <int>      <int>
#>  1     1        0        0       1          2
#>  2     2        1        1       1          1
#>  3     3        0        0       1          1
#>  4     4        0        0       1          3
#>  5     5        0        1       0          1
#>  6     6        1        0       0          2
#>  7     7        1        0       0          1
#>  8     8        0        0       1          1
#>  9     9        0        1       0          2
#> 10    10        0        0       1          3
#> # ℹ 990 more rows

You can fit an intercepts-only meta-d’ model using fit_metad:

library(hmetad)

m <- fit_metad(N ~ 1,
  data = d,
  prior = prior(normal(0, 1), class = Intercept) +
    set_prior("normal(0, 1)", class = c("dprime", "c", metac2_parameters(K = 4)))
)

#>  Family: metad__4__normal__absolute__multinomial 
#>   Links: mu = log 
#> Formula: N ~ 1 
#>    Data: data.aggregated (Number of observations: 1) 
#>   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#>          total post-warmup draws = 4000
#> 
#> Regression Coefficients:
#>           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept    -0.17      0.15    -0.46     0.12 1.00     3876     3016
#> 
#> Further Distributional Parameters:
#>                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> dprime              1.10      0.08     0.94     1.27 1.00     4747     3430
#> c                  -0.05      0.04    -0.13     0.03 1.00     3890     3526
#> metac2zero1diff     0.49      0.04     0.42     0.56 1.00     4760     3070
#> metac2zero2diff     0.45      0.04     0.38     0.52 1.00     5892     3409
#> metac2zero3diff     0.47      0.04     0.39     0.57 1.00     5573     2954
#> metac2one1diff      0.52      0.04     0.46     0.60 1.00     4692     3480
#> metac2one2diff      0.51      0.04     0.43     0.58 1.00     5756     3242
#> metac2one3diff      0.47      0.05     0.38     0.57 1.00     6404     2916
#> 
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).

Now let’s say you have a more complicated design, such as a within-participant manipulation:

#> # A tibble: 5,000 × 7
#> # Groups:   participant, condition [50]
#>    participant condition trial stimulus response correct confidence
#>          <int>     <int> <int>    <int>    <int>   <int>      <int>
#>  1           1         1     1        1        1       1          4
#>  2           1         1     2        1        1       1          4
#>  3           1         1     3        0        0       1          2
#>  4           1         1     4        1        1       1          4
#>  5           1         1     5        1        1       1          4
#>  6           1         1     6        0        0       1          1
#>  7           1         1     7        1        1       1          3
#>  8           1         1     8        1        1       1          4
#>  9           1         1     9        0        0       1          3
#> 10           1         1    10        0        1       0          3
#> # ℹ 4,990 more rows

To account for the repeated measures in this design, you can simply adjust the formula to include participant-level effects:

m <- fit_metad(
  bf(
    N ~ condition + (condition | participant),
    dprime + c +
      metac2zero1diff + metac2zero2diff + metac2zero3diff +
      metac2one1diff + metac2one2diff + metac2one3diff ~
      condition + (condition | participant)
  ),
  data = d, init = "0",
  prior = prior(normal(0, 1)) +
    set_prior("normal(0, 1)", dpar = c("dprime", "c", metac2_parameters(K = 4)))
)

#>  Family: metad__4__normal__absolute__multinomial 
#>   Links: mu = log; dprime = identity; c = identity; metac2zero1diff = log; metac2zero2diff = log; metac2zero3diff = log; metac2one1diff = log; metac2one2diff = log; metac2one3diff = log 
#> Formula: N ~ condition + (condition | participant) 
#>          dprime ~ condition + (condition | participant)
#>          c ~ condition + (condition | participant)
#>          metac2zero1diff ~ condition + (condition | participant)
#>          metac2zero2diff ~ condition + (condition | participant)
#>          metac2zero3diff ~ condition + (condition | participant)
#>          metac2one1diff ~ condition + (condition | participant)
#>          metac2one2diff ~ condition + (condition | participant)
#>          metac2one3diff ~ condition + (condition | participant)
#>    Data: data.aggregated (Number of observations: 50) 
#>   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#>          total post-warmup draws = 4000
#> 
#> Multilevel Hyperparameters:
#> ~participant (Number of levels: 25) 
#>                                                          Estimate Est.Error
#> sd(Intercept)                                                0.98      0.33
#> sd(condition)                                                0.63      0.19
#> sd(dprime_Intercept)                                         0.61      0.19
#> sd(dprime_condition)                                         0.52      0.12
#> sd(c_Intercept)                                              1.36      0.21
#> sd(c_condition)                                              0.92      0.14
#> sd(metac2zero1diff_Intercept)                                0.11      0.08
#> sd(metac2zero1diff_condition)                                0.07      0.05
#> sd(metac2zero2diff_Intercept)                                0.07      0.06
#> sd(metac2zero2diff_condition)                                0.04      0.04
#> sd(metac2zero3diff_Intercept)                                0.14      0.11
#> sd(metac2zero3diff_condition)                                0.11      0.08
#> sd(metac2one1diff_Intercept)                                 0.13      0.10
#> sd(metac2one1diff_condition)                                 0.08      0.06
#> sd(metac2one2diff_Intercept)                                 0.11      0.11
#> sd(metac2one2diff_condition)                                 0.08      0.07
#> sd(metac2one3diff_Intercept)                                 0.09      0.08
#> sd(metac2one3diff_condition)                                 0.06      0.05
#> cor(Intercept,condition)                                    -0.87      0.13
#> cor(dprime_Intercept,dprime_condition)                      -0.78      0.18
#> cor(c_Intercept,c_condition)                                -0.94      0.03
#> cor(metac2zero1diff_Intercept,metac2zero1diff_condition)    -0.26      0.58
#> cor(metac2zero2diff_Intercept,metac2zero2diff_condition)    -0.31      0.58
#> cor(metac2zero3diff_Intercept,metac2zero3diff_condition)    -0.25      0.58
#> cor(metac2one1diff_Intercept,metac2one1diff_condition)      -0.24      0.58
#> cor(metac2one2diff_Intercept,metac2one2diff_condition)      -0.38      0.56
#> cor(metac2one3diff_Intercept,metac2one3diff_condition)      -0.24      0.58
#>                                                          l-95% CI u-95% CI Rhat
#> sd(Intercept)                                                0.40     1.71 1.00
#> sd(condition)                                                0.31     1.05 1.00
#> sd(dprime_Intercept)                                         0.21     0.99 1.00
#> sd(dprime_condition)                                         0.30     0.78 1.01
#> sd(c_Intercept)                                              1.03     1.84 1.00
#> sd(c_condition)                                              0.69     1.24 1.00
#> sd(metac2zero1diff_Intercept)                                0.00     0.32 1.00
#> sd(metac2zero1diff_condition)                                0.00     0.20 1.00
#> sd(metac2zero2diff_Intercept)                                0.00     0.24 1.00
#> sd(metac2zero2diff_condition)                                0.00     0.16 1.00
#> sd(metac2zero3diff_Intercept)                                0.01     0.41 1.00
#> sd(metac2zero3diff_condition)                                0.01     0.30 1.00
#> sd(metac2one1diff_Intercept)                                 0.01     0.37 1.00
#> sd(metac2one1diff_condition)                                 0.00     0.23 1.00
#> sd(metac2one2diff_Intercept)                                 0.00     0.42 1.00
#> sd(metac2one2diff_condition)                                 0.00     0.28 1.00
#> sd(metac2one3diff_Intercept)                                 0.00     0.29 1.00
#> sd(metac2one3diff_condition)                                 0.00     0.20 1.00
#> cor(Intercept,condition)                                    -0.98    -0.52 1.00
#> cor(dprime_Intercept,dprime_condition)                      -0.95    -0.29 1.01
#> cor(c_Intercept,c_condition)                                -0.98    -0.88 1.00
#> cor(metac2zero1diff_Intercept,metac2zero1diff_condition)    -0.98     0.90 1.00
#> cor(metac2zero2diff_Intercept,metac2zero2diff_condition)    -0.99     0.89 1.00
#> cor(metac2zero3diff_Intercept,metac2zero3diff_condition)    -0.98     0.90 1.00
#> cor(metac2one1diff_Intercept,metac2one1diff_condition)      -0.98     0.92 1.00
#> cor(metac2one2diff_Intercept,metac2one2diff_condition)      -0.99     0.87 1.00
#> cor(metac2one3diff_Intercept,metac2one3diff_condition)      -0.98     0.92 1.00
#>                                                          Bulk_ESS Tail_ESS
#> sd(Intercept)                                                 995     1215
#> sd(condition)                                                 964      882
#> sd(dprime_Intercept)                                          707      438
#> sd(dprime_condition)                                          600      435
#> sd(c_Intercept)                                               695     1034
#> sd(c_condition)                                               777     1264
#> sd(metac2zero1diff_Intercept)                                1456     1808
#> sd(metac2zero1diff_condition)                                1292     2185
#> sd(metac2zero2diff_Intercept)                                1901     1776
#> sd(metac2zero2diff_condition)                                1573     2053
#> sd(metac2zero3diff_Intercept)                                1751     1904
#> sd(metac2zero3diff_condition)                                 846     1513
#> sd(metac2one1diff_Intercept)                                 1639     2016
#> sd(metac2one1diff_condition)                                 1082     1915
#> sd(metac2one2diff_Intercept)                                 1263     1529
#> sd(metac2one2diff_condition)                                 1102     1469
#> sd(metac2one3diff_Intercept)                                 2182     2461
#> sd(metac2one3diff_condition)                                 1391     1810
#> cor(Intercept,condition)                                     1109     1196
#> cor(dprime_Intercept,dprime_condition)                        503      322
#> cor(c_Intercept,c_condition)                                  840     1574
#> cor(metac2zero1diff_Intercept,metac2zero1diff_condition)     2113     2536
#> cor(metac2zero2diff_Intercept,metac2zero2diff_condition)     2630     2606
#> cor(metac2zero3diff_Intercept,metac2zero3diff_condition)     1298     2073
#> cor(metac2one1diff_Intercept,metac2one1diff_condition)       1895     2288
#> cor(metac2one2diff_Intercept,metac2one2diff_condition)       1777     2593
#> cor(metac2one3diff_Intercept,metac2one3diff_condition)       2240     2535
#> 
#> Regression Coefficients:
#>                           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
#> Intercept                    -0.37      0.33    -1.05     0.22 1.00     2321
#> dprime_Intercept              0.96      0.17     0.61     1.29 1.00     2655
#> c_Intercept                  -0.29      0.27    -0.83     0.23 1.01      440
#> metac2zero1diff_Intercept    -1.14      0.13    -1.41    -0.89 1.00     5817
#> metac2zero2diff_Intercept    -1.22      0.14    -1.50    -0.94 1.00     5432
#> metac2zero3diff_Intercept    -1.21      0.17    -1.55    -0.89 1.00     5732
#> metac2one1diff_Intercept     -1.10      0.13    -1.37    -0.84 1.00     5740
#> metac2one2diff_Intercept     -0.90      0.13    -1.15    -0.66 1.00     5427
#> metac2one3diff_Intercept     -1.29      0.14    -1.58    -1.01 1.00     5348
#> condition                     0.21      0.20    -0.17     0.62 1.00     2375
#> dprime_condition              0.02      0.13    -0.22     0.29 1.00     1769
#> c_condition                   0.17      0.18    -0.19     0.56 1.01      450
#> metac2zero1diff_condition     0.07      0.09    -0.09     0.24 1.00     6184
#> metac2zero2diff_condition     0.16      0.09    -0.01     0.33 1.00     5679
#> metac2zero3diff_condition     0.05      0.11    -0.16     0.26 1.00     5139
#> metac2one1diff_condition      0.04      0.09    -0.12     0.21 1.00     5380
#> metac2one2diff_condition     -0.06      0.08    -0.22     0.10 1.00     5096
#> metac2one3diff_condition      0.19      0.09     0.01     0.37 1.00     4889
#>                           Tail_ESS
#> Intercept                     2756
#> dprime_Intercept              2936
#> c_Intercept                    821
#> metac2zero1diff_Intercept     2963
#> metac2zero2diff_Intercept     2356
#> metac2zero3diff_Intercept     2583
#> metac2one1diff_Intercept      3103
#> metac2one2diff_Intercept      3000
#> metac2one3diff_Intercept      2876
#> condition                     2830
#> dprime_condition              2532
#> c_condition                    886
#> metac2zero1diff_condition     2844
#> metac2zero2diff_condition     3018
#> metac2zero3diff_condition     2776
#> metac2one1diff_condition      3233
#> metac2one2diff_condition      3120
#> metac2one3diff_condition      2743
#> 
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).

References

Fleming, S. M. (2017). HMeta-d: Hierarchical bayesian estimation of metacognitive efficiency from confidence ratings. Neuroscience of Consciousness, 2017(1), nix007.

Maniscalco, B., & Lau, H. (2012). A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings. Consciousness and Cognition, 21(1), 422–430.

Maniscalco, B., & Lau, H. (2014). Signal detection theory analysis of type 1 and type 2 data: Meta-d′, response-specific meta-d′, and the unequal variance SDT model. In The cognitive neuroscience of metacognition (pp. 25–66). Springer.

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They may not be fully stable and should be used with caution. We make no claims about them.
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