Author: Paul Bürkner
Date: 2021-08-23

# Estimating Monotonic Effects with brms

#### Paul Bürkner

#### 2021-08-23

## Introduction

This vignette is about monotonic effects, a special way of handling discrete predictors that are on an ordinal or higher scale (Bürkner & Charpentier, in review). A predictor, which we want to model as monotonic (i.e., having a monotonically increasing or decreasing relationship with the response), must either be integer valued or an ordered factor. As opposed to a continuous predictor, predictor categories (or integers) are not assumed to be equidistant with respect to their effect on the response variable. Instead, the distance between adjacent predictor categories (or integers) is estimated from the data and may vary across categories. This is realized by parameterizing as follows: One parameter, \(b\), takes care of the direction and size of the effect similar to an ordinary regression parameter. If the monotonic effect is used in a linear model, \(b\) can be interpreted as the expected average difference between two adjacent categories of the ordinal predictor. An additional parameter vector, \(\zeta\), estimates the normalized distances between consecutive predictor categories which thus defines the shape of the monotonic effect. For a single monotonic predictor, \(x\), the linear predictor term of observation \(n\) looks as follows:

\[\eta_n = b D \sum_{i = 1}^{x_n} \zeta_i\]

The parameter \(b\) can take on any real value, while \(\zeta\) is a simplex, which means that it satisfies \(\zeta_i \in [0,1]\) and \(\sum_{i = 1}^D \zeta_i = 1\) with \(D\) being the number of elements of \(\zeta\). Equivalently, \(D\) is the number of categories (or highest integer in the data) minus 1, since we start counting categories from zero to simplify the notation.

## A Simple Monotonic Model

A main application of monotonic effects are ordinal predictors that can be modeled this way without falsely treating them either as continuous or as unordered categorical predictors. In Psychology, for instance, this kind of data is omnipresent in the form of Likert scale items, which are often treated as being continuous for convenience without ever testing this assumption. As an example, suppose we are interested in the relationship of yearly income (in $) and life satisfaction measured on an arbitrary scale from 0 to 100. Usually, people are not asked for the exact income. Instead, they are asked to rank themselves in one of certain classes, say: ‘below 20k’, ‘between 20k and 40k’, ‘between 40k and 100k’ and ‘above 100k’. We use some simulated data for illustration purposes.

    income_options <- c("below_20", "20_to_40", "40_to_100", "greater_100")
    income <- factor(sample(income_options, 100, TRUE),
                     levels = income_options, ordered = TRUE)
    mean_ls <- c(30, 60, 70, 75)
    ls <- mean_ls[income] + rnorm(100, sd = 7)
    dat <- data.frame(income, ls)

We now proceed with analyzing the data modeling `income` as a monotonic effect.

    fit1 <- brm(ls ~ mo(income), data = dat)

The summary methods yield

    summary(fit1)

     Family: gaussian
      Links: mu = identity; sigma = identity
    Formula: ls ~ mo(income)
       Data: dat (Number of observations: 100)
      Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
             total post-warmup draws = 4000

    Population-Level Effects:
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    Intercept    32.64      1.80    29.11    36.23 1.00     2432     2108
    moincome     14.35      0.82    12.79    16.00 1.00     2325     2041

    Simplex Parameters:
                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    moincome1[1]     0.60      0.04     0.51     0.68 1.00     2955     2501
    moincome1[2]     0.29      0.05     0.19     0.39 1.00     3209     2084
    moincome1[3]     0.11      0.05     0.02     0.21 1.00     2475     1515

    Family Specific Parameters:
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sigma     7.79      0.55     6.83     8.97 1.00     3360     2858

    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).

    plot(fit1, variable = "simo", regex = TRUE)

    plot(conditional_effects(fit1))

The distributions of the simplex parameter of `income`, as shown in the `plot` method, demonstrate that the largest difference (about 70% of the difference between minimum and maximum category) is between the first two categories.

Now, let’s compare of monotonic model with two common alternative models. (a) Assume `income` to be continuous:

    dat$income_num <- as.numeric(dat$income)
    fit2 <- brm(ls ~ income_num, data = dat)

    summary(fit2)

     Family: gaussian
      Links: mu = identity; sigma = identity
    Formula: ls ~ income_num
       Data: dat (Number of observations: 100)
      Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
             total post-warmup draws = 4000

    Population-Level Effects:
               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    Intercept     25.56      2.43    20.67    30.44 1.00     4228     2428
    income_num    13.91      0.91    12.08    15.69 1.00     4294     2638

    Family Specific Parameters:
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sigma     9.35      0.67     8.14    10.74 1.00     3799     2808

    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).

or (b) Assume `income` to be an unordered factor:

    contrasts(dat$income) <- contr.treatment(4)
    fit3 <- brm(ls ~ income, data = dat)

    summary(fit3)

     Family: gaussian
      Links: mu = identity; sigma = identity
    Formula: ls ~ income
       Data: dat (Number of observations: 100)
      Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
             total post-warmup draws = 4000

    Population-Level Effects:
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    Intercept    32.27      1.81    28.63    35.83 1.00     2319     2466
    income2      26.04      2.29    21.71    30.61 1.00     2531     2835
    income3      38.62      2.35    34.08    43.28 1.00     2678     2868
    income4      43.55      2.45    38.72    48.31 1.00     2421     2700

    Family Specific Parameters:
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sigma     7.80      0.57     6.78     9.03 1.00     3501     2824

    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).

We can easily compare the fit of the three models using leave-one-out cross-validation.

    loo(fit1, fit2, fit3)

    Output of model 'fit1':

    Computed from 4000 by 100 log-likelihood matrix

             Estimate   SE
    elpd_loo   -348.9  6.9
    p_loo         4.6  0.7
    looic       697.9 13.7
    ------
    Monte Carlo SE of elpd_loo is 0.0.

    All Pareto k estimates are good (k < 0.5).
    See help('pareto-k-diagnostic') for details.

    Output of model 'fit2':

    Computed from 4000 by 100 log-likelihood matrix

             Estimate   SE
    elpd_loo   -366.5  6.2
    p_loo         2.7  0.4
    looic       733.0 12.4
    ------
    Monte Carlo SE of elpd_loo is 0.0.

    All Pareto k estimates are good (k < 0.5).
    See help('pareto-k-diagnostic') for details.

    Output of model 'fit3':

    Computed from 4000 by 100 log-likelihood matrix

             Estimate   SE
    elpd_loo   -349.1  6.9
    p_loo         4.8  0.8
    looic       698.1 13.8
    ------
    Monte Carlo SE of elpd_loo is 0.0.

    All Pareto k estimates are good (k < 0.5).
    See help('pareto-k-diagnostic') for details.

    Model comparisons:
         elpd_diff se_diff
    fit1   0.0       0.0
    fit3  -0.1       0.2
    fit2 -17.6       5.3

The monotonic model fits better than the continuous model, which is not surprising given that the relationship between `income` and `ls` is non-linear. The monotonic and the unordered factor model have almost identical fit in this example, but this may not be the case for other data sets.

## Setting Prior Distributions

In the previous monotonic model, we have implicitly assumed that all differences between adjacent categories were a-priori the same, or formulated correctly, had the same prior distribution. In the following, we want to show how to change this assumption. The canonical prior distribution of a simplex parameter is the Dirichlet distribution, a multivariate generalization of the beta distribution. It is non-zero for all valid simplexes (i.e., \(\zeta_i \in [0,1]\) and \(\sum_{i = 1}^D \zeta_i = 1\)) and zero otherwise. The Dirichlet prior has a single parameter \(\alpha\) of the same length as \(\zeta\). The higher \(\alpha_i\) the higher the a-priori probability of higher values of \(\zeta_i\). Suppose that, before looking at the data, we expected that the same amount of additional money matters more for people who generally have less money. This translates into a higher a-priori values of \(\zeta_1\) (difference between ‘below_20’ and ‘20_to_40’) and hence into higher values of \(\alpha_1\). We choose \(\alpha_1 = 2\) and \(\alpha_2 = \alpha_3 = 1\), the latter being the default value of \(\alpha\). To fit the model we write:

    prior4 <- prior(dirichlet(c(2, 1, 1)), class = "simo", coef = "moincome1")
    fit4 <- brm(ls ~ mo(income), data = dat,
                prior = prior4, sample_prior = TRUE)

The `1` at the end of `"moincome1"` may appear strange when first working with monotonic effects. However, it is necessary as one monotonic term may be associated with multiple simplex parameters, if interactions of multiple monotonic variables are included in the model.

    summary(fit4)

     Family: gaussian
      Links: mu = identity; sigma = identity
    Formula: ls ~ mo(income)
       Data: dat (Number of observations: 100)
      Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
             total post-warmup draws = 4000

    Population-Level Effects:
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    Intercept    32.63      1.78    29.09    36.25 1.00     2219     2190
    moincome     14.33      0.80    12.77    15.98 1.00     2081     1953

    Simplex Parameters:
                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    moincome1[1]     0.60      0.04     0.51     0.69 1.00     2410     2403
    moincome1[2]     0.29      0.05     0.19     0.39 1.00     2740     2333
    moincome1[3]     0.11      0.05     0.02     0.20 1.00     2117     1291

    Family Specific Parameters:
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sigma     7.79      0.58     6.78     8.98 1.00     2443     2775

    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).

We have used `sample_prior = TRUE` to also obtain draws from the prior distribution of `simo_moincome1` so that we can visualized it.

    plot(fit4, variable = "prior_simo", regex = TRUE, N = 3)

As is visible in the plots, `simo_moincome1[1]` was a-priori on average twice as high as `simo_moincome1[2]` and `simo_moincome1[3]` as a result of setting \(\alpha_1\) to 2.

## Modeling interactions of monotonic variables

Suppose, we have additionally asked participants for their age.

    dat$age <- rnorm(100, mean = 40, sd = 10)

We are not only interested in the main effect of age but also in the interaction of income and age. Interactions with monotonic variables can be specified in the usual way using the `*` operator:

    fit5 <- brm(ls ~ mo(income)*age, data = dat)

    summary(fit5)

     Family: gaussian
      Links: mu = identity; sigma = identity
    Formula: ls ~ mo(income) * age
       Data: dat (Number of observations: 100)
      Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
             total post-warmup draws = 4000

    Population-Level Effects:
                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    Intercept       36.39      5.53    24.08    46.07 1.01     1099     1700
    age             -0.10      0.14    -0.35     0.22 1.01     1037     1699
    moincome        15.07      2.79    10.23    21.09 1.01      841     1922
    moincome:age    -0.01      0.07    -0.16     0.11 1.01      823     1670

    Simplex Parameters:
                     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    moincome1[1]         0.61      0.07     0.50     0.76 1.00     1576     1274
    moincome1[2]         0.28      0.06     0.14     0.39 1.00     2304     1599
    moincome1[3]         0.11      0.05     0.01     0.22 1.00     1466      776
    moincome:age1[1]     0.39      0.26     0.02     0.90 1.00     1220     1790
    moincome:age1[2]     0.31      0.23     0.01     0.81 1.00     1990     1962
    moincome:age1[3]     0.29      0.22     0.01     0.81 1.00     1399     2005

    Family Specific Parameters:
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sigma     7.70      0.57     6.66     8.88 1.00     2804     2611

    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).

    conditional_effects(fit5, "income:age")

## Modelling Monotonic Group-Level Effects

Suppose that the 100 people in our sample data were drawn from 10 different cities; 10 people per city. Thus, we add an identifier for `city` to the data and add some city-related variation to `ls`.

    dat$city <- rep(1:10, each = 10)
    var_city <- rnorm(10, sd = 10)
    dat$ls <- dat$ls + var_city[dat$city]

With the following code, we fit a multilevel model assuming the intercept and the effect of `income` to vary by city:

    fit6 <- brm(ls ~ mo(income)*age + (mo(income) | city), data = dat)

    summary(fit6)

     Family: gaussian
      Links: mu = identity; sigma = identity
    Formula: ls ~ mo(income) * age + (mo(income) | city)
       Data: dat (Number of observations: 100)
      Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
             total post-warmup draws = 4000

    Group-Level Effects:
    ~city (Number of levels: 10)
                            Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sd(Intercept)              10.57      3.89     4.22    20.24 1.00     1780     1752
    sd(moincome)                2.05      1.51     0.10     5.65 1.00      833     1348
    cor(Intercept,moincome)     0.15      0.51    -0.81     0.95 1.00     2706     2382

    Population-Level Effects:
                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    Intercept       38.17      6.99    22.91    51.24 1.00     1305     1770
    age             -0.12      0.15    -0.38     0.22 1.00     1201     1691
    moincome        13.72      3.09     8.30    20.30 1.00     1033     1430
    moincome:age     0.01      0.07    -0.16     0.13 1.00      930     1437

    Simplex Parameters:
                     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    moincome1[1]         0.63      0.08     0.50     0.83 1.00     1731     1071
    moincome1[2]         0.27      0.08     0.09     0.39 1.00     2149     1093
    moincome1[3]         0.10      0.05     0.01     0.22 1.00     2768     1803
    moincome:age1[1]     0.37      0.25     0.02     0.88 1.00     1828     2478
    moincome:age1[2]     0.35      0.24     0.01     0.85 1.00     2221     2509
    moincome:age1[3]     0.29      0.21     0.01     0.77 1.00     3036     2984

    Family Specific Parameters:
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sigma     7.50      0.60     6.42     8.80 1.00     4543     3176

    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).

reveals that the effect of `income` varies only little across cities. For the present data, this is not overly surprising given that, in the data simulations, we assumed `income` to have the same effect across cities.

## References

Bürkner P. C. & Charpentier, E. (in review). Monotonic Effects: A Principled Approach for Including Ordinal Predictors in Regression Models. PsyArXiv preprint.