# Estimating trial-level effects

## Introduction

By default, the `hmetad` package uses aggregated data (i.e., counts of the number of trials with the same stimulus, type 1 response, and type 2 response.) This is because data aggregation makes model fitting and simulation much more efficient. But sometimes researchers will be interested in trial-level effects.

One common example would be what is often called “crossed random effects”. For example, in a design where all participants make responses to the same set of items, a researcher might want to estimate both participant-level and item-level effects on their model parameters.

We can simulate data from such a design like so:

    library(tidyverse)
    library(tidybayes)
    library(hmetad)

    ## average model parameters
    K <- 3 ## number of confidence levels
    mu_log_M <- -0.5
    mu_dprime <- 1.5
    mu_c <- 0
    mu_c2_0 <- rep(-1, K - 1)
    mu_c2_1 <- rep(-1, K - 1)

    ## participant-level standard deviations
    sd_log_M_participant <- 0.25
    sd_dprime_participant <- 0.5
    sd_c_participant <- 0.33
    sd_c2_0_participant <- cov_matrix(rep(0.25, K - 1), diag(K - 1))
    sd_c2_1_participant <- cov_matrix(rep(0.25, K - 1), diag(K - 1))

    ## item-level standard deviations
    sd_log_M_item <- 0.1
    sd_dprime_item <- 0.5
    sd_c_item <- 0.75
    sd_c2_0_item <- cov_matrix(rep(0.1, K - 1), diag(K - 1))
    sd_c2_1_item <- cov_matrix(rep(0.1, K - 1), diag(K - 1))

    ## simulate data
    d <- expand_grid(
      participant = 1:50,
      item = 1:25
    ) |>
      ## simulate participant-level differences
      group_by(participant) |>
      mutate(
        z_log_M_participant = rnorm(1, sd = sd_log_M_participant),
        z_dprime_participant = rnorm(1, sd = sd_dprime_participant),
        z_c_participant = rnorm(1, sd = sd_c_participant),
        z_c2_0_participant = list(rmulti_normal(1, mu = rep(0, K - 1), Sigma = sd_c2_0_participant)),
        z_c2_1_participant = list(rmulti_normal(1, mu = rep(0, K - 1), Sigma = sd_c2_1_participant))
      ) |>
      ## simulate item-level differences
      group_by(item) |>
      mutate(
        z_log_M_item = rnorm(1, sd = sd_log_M_item),
        z_dprime_item = rnorm(1, sd = sd_dprime_item),
        z_c_item = rnorm(1, sd = sd_c_item),
        z_c2_0_item = list(rmulti_normal(1, mu = rep(0, K - 1), Sigma = sd_c2_0_item)),
        z_c2_1_item = list(rmulti_normal(1, mu = rep(0, K - 1), Sigma = sd_c2_1_item))
      ) |>
      ungroup() |>
      ## compute model parameters
      mutate(
        log_M = mu_log_M + z_log_M_participant + z_log_M_item,
        dprime = mu_dprime + z_dprime_participant + z_dprime_item,
        c = mu_c + z_c_participant + z_c_item,
        c2_0_diff = map2(
          z_c2_0_participant, z_c2_0_item,
          ~ exp(mu_c2_0 + .x + .y)
        ),
        c2_1_diff = map2(
          z_c2_1_participant, z_c2_1_item,
          ~ exp(mu_c2_1 + .x + .y)
        )
      ) |>
      ## simulate two trials per participant/item (stimulus = 0 and stimulus = 1)
      mutate(trial = pmap(list(dprime, c, log_M, c2_0_diff, c2_1_diff), sim_metad, N_trials = 2)) |>
      select(participant, item, trial) |>
      unnest(trial)

    #> # A tibble: 2,500 × 16
    #>    participant  item trial stimulus response correct confidence dprime       c meta_dprime     M meta_c2_0
    #>          <int> <int> <int>    <int>    <int>   <int>      <int>  <dbl>   <dbl>       <dbl> <dbl> <list>
    #>  1           1     1     1        0        1       0          3  0.725 -0.843        0.614 0.847 <dbl [2]>
    #>  2           1     1     1        1        1       1          3  0.725 -0.843        0.614 0.847 <dbl [2]>
    #>  3           1     2     1        0        0       1          2  1.74  -1.24         1.12  0.642 <dbl [2]>
    #>  4           1     2     1        1        1       1          3  1.74  -1.24         1.12  0.642 <dbl [2]>
    #>  5           1     3     1        0        0       1          3  0.746  0.614        0.565 0.758 <dbl [2]>
    #>  6           1     3     1        1        0       0          2  0.746  0.614        0.565 0.758 <dbl [2]>
    #>  7           1     4     1        0        0       1          2  1.50  -0.0110       1.21  0.809 <dbl [2]>
    #>  8           1     4     1        1        1       1          3  1.50  -0.0110       1.21  0.809 <dbl [2]>
    #>  9           1     5     1        0        0       1          1  1.21  -0.231        1.13  0.931 <dbl [2]>
    #> 10           1     5     1        1        1       1          1  1.21  -0.231        1.13  0.931 <dbl [2]>
    #> # ℹ 2,490 more rows
    #> # ℹ 4 more variables: meta_c2_1 <list>, theta <dbl>, theta_1 <dbl>, theta_2 <dbl>

Don’t worry about the details of the simulation code- what matters is that we have a data set with repeated measures for participants:

    count(d, participant)
    #> # A tibble: 50 × 2
    #>    participant     n
    #>          <int> <int>
    #>  1           1    50
    #>  2           2    50
    #>  3           3    50
    #>  4           4    50
    #>  5           5    50
    #>  6           6    50
    #>  7           7    50
    #>  8           8    50
    #>  9           9    50
    #> 10          10    50
    #> # ℹ 40 more rows

And repeated measures for items:

    count(d, item)
    #> # A tibble: 25 × 2
    #>     item     n
    #>    <int> <int>
    #>  1     1   100
    #>  2     2   100
    #>  3     3   100
    #>  4     4   100
    #>  5     5   100
    #>  6     6   100
    #>  7     7   100
    #>  8     8   100
    #>  9     9   100
    #> 10    10   100
    #> # ℹ 15 more rows

## Standard model with data aggregation

If we would like, we can use the `fit_metad` function on this data with participant-level and item-level effects. However, if we aggregate the data ourselves, we can see that the aggregation doesn’t really help us here:

    aggregate_metad(d, participant, item)
    #> `hmetad` has inferred that there are K=3 confidence levels in the data. If this is incorrect, please set this manually using the argument `K=<K>`
    #> # A tibble: 1,250 × 5
    #>    participant  item   N_0   N_1 N[,"N_0_1"] [,"N_0_2"] [,"N_0_3"] [,"N_0_4"] [,"N_0_5"] [,"N_0_6"] [,"N_1_1"]
    #>          <int> <int> <int> <int>       <int>      <int>      <int>      <int>      <int>      <int>      <int>
    #>  1           1     1     1     1           0          0          0          0          0          1          0
    #>  2           1     2     1     1           0          1          0          0          0          0          0
    #>  3           1     3     1     1           1          0          0          0          0          0          0
    #>  4           1     4     1     1           0          1          0          0          0          0          0
    #>  5           1     5     1     1           0          0          1          0          0          0          0
    #>  6           1     6     1     1           1          0          0          0          0          0          0
    #>  7           1     7     1     1           0          0          0          0          1          0          0
    #>  8           1     8     1     1           1          0          0          0          0          0          0
    #>  9           1     9     1     1           1          0          0          0          0          0          0
    #> 10           1    10     1     1           1          0          0          0          0          0          0
    #> # ℹ 1,240 more rows
    #> # ℹ 1 more variable: N[8:12] <int>

As you can see, the aggregated data set has 1250 rows (with two observations per row), which is not much smaller than the trial-level data that we started with! So, in this case, it will probably be easier not to aggregate our data. Nevertheless, there is nothing stopping us from fitting the model like normal:1

    # Priors are chosen arbitrarily for this example.
    # Please choose your own wisely!
    priors <- prior(normal(0, .25), class = Intercept) +
      set_prior(
        "normal(0, .25)",
        class = "Intercept",
        dpar = c("dprime", "c")
      ) +
      set_prior("normal(-0.5, .1)", class = "Intercept", dpar = metac2_parameters(K = 3)) +
      prior(normal(0, 1), class = sd) +
      set_prior("normal(0, 1)", class = "sd", dpar = c("dprime", "c", metac2_parameters(K = 3)))

    m.multinomial <- fit_metad(
      bf(
        N ~ 1 + (1 | participant) + (1 | item),
        dprime + c +
          metac2zero1diff + metac2zero2diff +
          metac2one1diff + metac2one2diff ~
          1 + (1 | participant) + (1 | item)
      ),
      data = d, init = 0, cores = 4, prior = priors
    )
    #> `hmetad` has inferred that there are K=3 confidence levels in the data. If this is incorrect, please set this manually using the argument `K=<K>`
    #> Compiling Stan program...
    #> Start sampling
    #> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
    #> Running the chains for more iterations may help. See
    #> https://mc-stan.org/misc/warnings.html#bulk-ess

    #>  Family: metad__3__normal__absolute__multinomial
    #>   Links: mu = log; dprime = identity; c = identity; metac2zero1diff = log; metac2zero2diff = log; metac2one1diff = log; metac2one2diff = log
    #> Formula: N ~ 1 + (1 | participant) + (1 | item)
    #>          dprime ~ 1 + (1 | participant) + (1 | item)
    #>          c ~ 1 + (1 | participant) + (1 | item)
    #>          metac2zero1diff ~ 1 + (1 | participant) + (1 | item)
    #>          metac2zero2diff ~ 1 + (1 | participant) + (1 | item)
    #>          metac2one1diff ~ 1 + (1 | participant) + (1 | item)
    #>          metac2one2diff ~ 1 + (1 | participant) + (1 | item)
    #>    Data: data.aggregated (Number of observations: 1250)
    #>   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
    #>          total post-warmup draws = 4000
    #>
    #> Multilevel Hyperparameters:
    #> ~item (Number of levels: 25)
    #>                               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    #> sd(Intercept)                     0.14      0.10     0.01     0.37 1.00     1074     1538
    #> sd(dprime_Intercept)              0.57      0.14     0.35     0.88 1.00      757     1379
    #> sd(c_Intercept)                   0.76      0.12     0.57     1.03 1.00      614     1061
    #> sd(metac2zero1diff_Intercept)     0.07      0.05     0.00     0.20 1.00     1876     1525
    #> sd(metac2zero2diff_Intercept)     0.13      0.09     0.01     0.32 1.00      927     1399
    #> sd(metac2one1diff_Intercept)      0.11      0.08     0.00     0.31 1.00     1085     1964
    #> sd(metac2one2diff_Intercept)      0.18      0.10     0.01     0.41 1.00      921     1546
    #>
    #> ~participant (Number of levels: 50)
    #>                               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    #> sd(Intercept)                     0.12      0.09     0.01     0.32 1.00     1247     1931
    #> sd(dprime_Intercept)              0.44      0.08     0.29     0.62 1.00     1200     2111
    #> sd(c_Intercept)                   0.35      0.05     0.27     0.45 1.00     1329     2091
    #> sd(metac2zero1diff_Intercept)     0.19      0.10     0.02     0.38 1.01      717      955
    #> sd(metac2zero2diff_Intercept)     0.30      0.10     0.11     0.49 1.01     1012     1093
    #> sd(metac2one1diff_Intercept)      0.20      0.10     0.02     0.41 1.01      815     1241
    #> sd(metac2one2diff_Intercept)      0.36      0.10     0.15     0.56 1.00      887      716
    #>
    #> Regression Coefficients:
    #>                           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    #> Intercept                    -0.23      0.09    -0.41    -0.07 1.00     3386     2946
    #> dprime_Intercept              1.10      0.15     0.77     1.36 1.00      930     1255
    #> c_Intercept                   0.15      0.14    -0.12     0.42 1.02      275      496
    #> metac2zero1diff_Intercept    -0.83      0.06    -0.93    -0.72 1.00     3284     2931
    #> metac2zero2diff_Intercept    -0.79      0.06    -0.91    -0.67 1.00     2643     2965
    #> metac2one1diff_Intercept     -0.84      0.06    -0.95    -0.71 1.00     3121     2733
    #> metac2one2diff_Intercept     -0.79      0.07    -0.92    -0.65 1.00     2296     2603
    #>
    #> 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).

## Data preparation

Fitting the trial-level model does not require data aggregation, however it still requires a small amount of data preparation. To fit the model, we will need two things:

- a column with the stimulus per trial (`0` or `1`), and
- a column containing the joint type 1/type 2 responses per trial (between `1` and `2*K`).

Our data already has a `stimulus` column but separate columns for the two responses. So, we can add in a joint response column now:

    d <- d |>
      mutate(joint_response = joint_response(response, confidence, K)) |>
      relocate(joint_response, .after = "stimulus")

    #> # A tibble: 2,500 × 17
    #>    participant  item trial stimulus joint_response response correct confidence dprime       c meta_dprime     M
    #>          <int> <int> <int>    <int>          <int>    <int>   <int>      <int>  <dbl>   <dbl>       <dbl> <dbl>
    #>  1           1     1     1        0              6        1       0          3  0.725 -0.843        0.614 0.847
    #>  2           1     1     1        1              6        1       1          3  0.725 -0.843        0.614 0.847
    #>  3           1     2     1        0              2        0       1          2  1.74  -1.24         1.12  0.642
    #>  4           1     2     1        1              6        1       1          3  1.74  -1.24         1.12  0.642
    #>  5           1     3     1        0              1        0       1          3  0.746  0.614        0.565 0.758
    #>  6           1     3     1        1              2        0       0          2  0.746  0.614        0.565 0.758
    #>  7           1     4     1        0              2        0       1          2  1.50  -0.0110       1.21  0.809
    #>  8           1     4     1        1              6        1       1          3  1.50  -0.0110       1.21  0.809
    #>  9           1     5     1        0              3        0       1          1  1.21  -0.231        1.13  0.931
    #> 10           1     5     1        1              4        1       1          1  1.21  -0.231        1.13  0.931
    #> # ℹ 2,490 more rows
    #> # ℹ 5 more variables: meta_c2_0 <list>, meta_c2_1 <list>, theta <dbl>, theta_1 <dbl>, theta_2 <dbl>

## Model fitting

Now that we have our data, we can fit the trial-level model using `joint_response` as our response variable, `stimulus` as an extra variable passed to `brms`, and the argument `categorical=TRUE` to tell `fit_metad` not to aggregate the data:

    m.categorical <- fit_metad(
      bf(
        joint_response | vint(stimulus) ~ 1 + (1 | participant) + (1 | item),
        dprime + c +
          metac2zero1diff + metac2zero2diff +
          metac2one1diff + metac2one2diff ~
          1 + (1 | participant) + (1 | item)
      ),
      data = d, categorical = TRUE, init = 0, cores = 4, prior = priors
    )
    #> `hmetad` has inferred that there are K=3 confidence levels in the data. If this is incorrect, please set this manually using the argument `K=<K>`
    #> Compiling Stan program...
    #> Start sampling

    #>  Family: metad__3__normal__absolute__categorical
    #>   Links: mu = log; dprime = identity; c = identity; metac2zero1diff = log; metac2zero2diff = log; metac2one1diff = log; metac2one2diff = log
    #> Formula: joint_response | vint(stimulus) ~ 1 + (1 | participant) + (1 | item)
    #>          dprime ~ 1 + (1 | participant) + (1 | item)
    #>          c ~ 1 + (1 | participant) + (1 | item)
    #>          metac2zero1diff ~ 1 + (1 | participant) + (1 | item)
    #>          metac2zero2diff ~ 1 + (1 | participant) + (1 | item)
    #>          metac2one1diff ~ 1 + (1 | participant) + (1 | item)
    #>          metac2one2diff ~ 1 + (1 | participant) + (1 | item)
    #>    Data: data.aggregated (Number of observations: 2500)
    #>   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
    #>          total post-warmup draws = 4000
    #>
    #> Multilevel Hyperparameters:
    #> ~item (Number of levels: 25)
    #>                               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    #> sd(Intercept)                     0.14      0.10     0.01     0.37 1.00     1467     1998
    #> sd(dprime_Intercept)              0.57      0.14     0.36     0.89 1.00      854     1603
    #> sd(c_Intercept)                   0.76      0.12     0.57     1.02 1.00      792     1543
    #> sd(metac2zero1diff_Intercept)     0.07      0.05     0.00     0.19 1.00     2130     2015
    #> sd(metac2zero2diff_Intercept)     0.13      0.09     0.01     0.33 1.00     1264     1853
    #> sd(metac2one1diff_Intercept)      0.12      0.09     0.00     0.31 1.00     1053     1268
    #> sd(metac2one2diff_Intercept)      0.18      0.10     0.02     0.41 1.00      927     1443
    #>
    #> ~participant (Number of levels: 50)
    #>                               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    #> sd(Intercept)                     0.12      0.09     0.00     0.31 1.00     1295     1610
    #> sd(dprime_Intercept)              0.44      0.08     0.29     0.62 1.00     1513     2242
    #> sd(c_Intercept)                   0.35      0.05     0.27     0.45 1.00     1330     2340
    #> sd(metac2zero1diff_Intercept)     0.18      0.10     0.01     0.38 1.00      739     1234
    #> sd(metac2zero2diff_Intercept)     0.30      0.10     0.07     0.51 1.01      698      502
    #> sd(metac2one1diff_Intercept)      0.21      0.10     0.02     0.41 1.00      702      973
    #> sd(metac2one2diff_Intercept)      0.36      0.10     0.17     0.56 1.01      954      837
    #>
    #> Regression Coefficients:
    #>                           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    #> Intercept                    -0.23      0.09    -0.42    -0.06 1.00     3799     2616
    #> dprime_Intercept              1.09      0.15     0.78     1.35 1.00      851     1703
    #> c_Intercept                   0.14      0.13    -0.12     0.40 1.01      501     1045
    #> metac2zero1diff_Intercept    -0.83      0.05    -0.93    -0.72 1.00     4309     2970
    #> metac2zero2diff_Intercept    -0.79      0.06    -0.91    -0.66 1.00     2891     2937
    #> metac2one1diff_Intercept     -0.83      0.06    -0.95    -0.71 1.00     2537     2565
    #> metac2one2diff_Intercept     -0.79      0.07    -0.92    -0.65 1.00     2479     2625
    #>
    #> 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).

As you can see, aside from the way that the data is formatted, this model is exactly the same as the multinomial model above.

## Extracting model estimates

Obtaining posterior estimates over model parameters, predictions, and other estimates is very similar to the multinomial model (for details, see `vignette("hmetad")`). So, here we will focus on the type 1 ROC curves, this time using `roc1_rvars` instead of `roc1_draws` for increased efficiency.

To get the posterior estimates, we need to specify a dataset to make predictions for, as well as a random effects formula to use in model predictions. For example, to estimate a ROC averaging over participants and items, we can use an empty data set with `re_formula=NA`:

    roc1_rvars(m.multinomial, tibble(.row = 1), re_formula = NA)
    #> # A tibble: 5 × 6
    #> # Groups:   .row, joint_response, response, confidence [5]
    #>    .row joint_response response confidence           p_fa         p_hit
    #>   <int>          <int>    <int>      <dbl>     <rvar[1d]>    <rvar[1d]>
    #> 1     1              1        0          3  0.600 ± 0.058  0.89 ± 0.031
    #> 2     1              2        0          2  0.413 ± 0.058  0.79 ± 0.047
    #> 3     1              3        0          1  0.244 ± 0.048  0.65 ± 0.059
    #> 4     1              4        1          1  0.136 ± 0.034  0.47 ± 0.062
    #> 5     1              5        1          2  0.063 ± 0.020  0.29 ± 0.054

The process is exactly the same for the categorical model:

    roc1_rvars(m.categorical, tibble(.row = 1), re_formula = NA)
    #> # A tibble: 5 × 6
    #> # Groups:   .row, joint_response, response, confidence [5]
    #>    .row joint_response response confidence           p_fa         p_hit
    #>   <int>          <int>    <int>      <dbl>     <rvar[1d]>    <rvar[1d]>
    #> 1     1              1        0          3  0.605 ± 0.057  0.89 ± 0.028
    #> 2     1              2        0          2  0.418 ± 0.057  0.79 ± 0.043
    #> 3     1              3        0          1  0.248 ± 0.047  0.66 ± 0.055
    #> 4     1              4        1          1  0.138 ± 0.033  0.47 ± 0.058
    #> 5     1              5        1          2  0.064 ± 0.019  0.30 ± 0.051

Next, to get participant-level ROCs (averaging over items), we can use a dataset with one row per participant and only the participant-level random effects:

    roc1_rvars(m.categorical, distinct(d, participant), re_formula = ~ (1 | participant))
    #> # A tibble: 250 × 7
    #> # Groups:   .row, participant, joint_response, response, confidence [250]
    #>     .row participant joint_response response confidence          p_fa         p_hit
    #>    <int>       <int>          <int>    <int>      <dbl>    <rvar[1d]>    <rvar[1d]>
    #>  1     1           1              1        0          3  0.54 ± 0.103  0.93 ± 0.038
    #>  2     2           2              1        0          3  0.75 ± 0.077  0.91 ± 0.042
    #>  3     3           3              1        0          3  0.84 ± 0.066  0.96 ± 0.024
    #>  4     4           4              1        0          3  0.45 ± 0.099  0.78 ± 0.073
    #>  5     5           5              1        0          3  0.63 ± 0.093  0.93 ± 0.035
    #>  6     6           6              1        0          3  0.44 ± 0.099  0.91 ± 0.043
    #>  7     7           7              1        0          3  0.60 ± 0.092  0.88 ± 0.051
    #>  8     8           8              1        0          3  0.40 ± 0.095  0.67 ± 0.088
    #>  9     9           9              1        0          3  0.72 ± 0.083  0.88 ± 0.052
    #> 10    10          10              1        0          3  0.50 ± 0.099  0.68 ± 0.087
    #> # ℹ 240 more rows

We can use a similar process to get item-level ROCs (averaging over participants):

    roc1_rvars(m.categorical, distinct(d, item), re_formula = ~ (1 | item))
    #> # A tibble: 125 × 7
    #> # Groups:   .row, item, joint_response, response, confidence [125]
    #>     .row  item joint_response response confidence          p_fa          p_hit
    #>    <int> <int>          <int>    <int>      <dbl>    <rvar[1d]>     <rvar[1d]>
    #>  1     1     1              1        0          3  0.92 ± 0.026  0.98 ± 0.0115
    #>  2     2     2              1        0          3  0.93 ± 0.023  1.00 ± 0.0023
    #>  3     3     3              1        0          3  0.59 ± 0.064  0.74 ± 0.0521
    #>  4     4     4              1        0          3  0.50 ± 0.066  0.91 ± 0.0292
    #>  5     5     5              1        0          3  0.71 ± 0.055  0.93 ± 0.0252
    #>  6     6     6              1        0          3  0.50 ± 0.067  0.92 ± 0.0249
    #>  7     7     7              1        0          3  0.47 ± 0.067  0.84 ± 0.0413
    #>  8     8     8              1        0          3  0.43 ± 0.064  0.89 ± 0.0324
    #>  9     9     9              1        0          3  0.56 ± 0.065  0.88 ± 0.0334
    #> 10    10    10              1        0          3  0.41 ± 0.065  0.87 ± 0.0349
    #> # ℹ 115 more rows

## Other benefits

Aside from representing the data in a more convenient format, the trial-level model should be more useful for things like model comparison using the `loo` package, multivariate models, and mediation models. These features should mostly work out of the box but they are still under active development, so stay tuned!

1. Note that in practice, fitting hierarchical models will usually require setting informed priors and adjusting the Stan sampler settings.↩︎