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 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.

The summary methods yield

```
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
Regression Coefficients:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 29.91 1.63 26.74 33.14 1.00 2805 2550
moincome 14.54 0.74 13.09 15.94 1.00 2711 2583
Monotonic Simplex Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
moincome1[1] 0.69 0.04 0.60 0.78 1.00 2587 2036
moincome1[2] 0.21 0.05 0.11 0.31 1.00 3629 2929
moincome1[3] 0.10 0.04 0.02 0.19 1.00 2627 1409
Further Distributional Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 7.81 0.56 6.81 8.99 1.00 3464 2482
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).
```

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:

```
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
Regression Coefficients:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 23.24 2.53 18.28 28.31 1.00 3780 2867
income_num 13.95 0.91 12.17 15.77 1.00 3904 2563
Further Distributional Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 10.30 0.73 8.96 11.84 1.00 3423 2204
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:

```
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
Regression Coefficients:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 29.62 1.59 26.58 32.75 1.00 3078 2942
income2 30.32 2.27 26.03 34.77 1.00 3395 2700
income3 39.45 2.25 34.86 43.87 1.00 3200 2897
income4 43.97 2.24 39.54 48.38 1.00 3371 3434
Further Distributional Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 7.81 0.57 6.79 9.01 1.00 4344 3043
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.

```
Output of model 'fit1':
Computed from 4000 by 100 log-likelihood matrix.
Estimate SE
elpd_loo -349.7 8.7
p_loo 5.2 1.3
looic 699.3 17.3
------
MCSE of elpd_loo is 0.1.
MCSE and ESS estimates assume MCMC draws (r_eff in [0.6, 1.1]).
All Pareto k estimates are good (k < 0.7).
See help('pareto-k-diagnostic') for details.
Output of model 'fit2':
Computed from 4000 by 100 log-likelihood matrix.
Estimate SE
elpd_loo -375.9 5.8
p_loo 2.5 0.3
looic 751.8 11.6
------
MCSE of elpd_loo is 0.0.
MCSE and ESS estimates assume MCMC draws (r_eff in [0.7, 1.0]).
All Pareto k estimates are good (k < 0.7).
See help('pareto-k-diagnostic') for details.
Output of model 'fit3':
Computed from 4000 by 100 log-likelihood matrix.
Estimate SE
elpd_loo -349.7 8.6
p_loo 5.3 1.3
looic 699.5 17.3
------
MCSE of elpd_loo is 0.0.
MCSE and ESS estimates assume MCMC draws (r_eff in [0.7, 1.3]).
All Pareto k estimates are good (k < 0.7).
See help('pareto-k-diagnostic') for details.
Model comparisons:
elpd_diff se_diff
fit1 0.0 0.0
fit3 -0.1 0.2
fit2 -26.2 7.4
```

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.

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.

```
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
Regression Coefficients:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 29.90 1.59 26.82 33.10 1.00 2532 2983
moincome 14.53 0.73 13.10 15.91 1.00 2422 2814
Monotonic Simplex Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
moincome1[1] 0.69 0.04 0.61 0.78 1.00 2914 2092
moincome1[2] 0.21 0.05 0.10 0.31 1.00 3901 2693
moincome1[3] 0.10 0.04 0.02 0.19 1.00 2449 1506
Further Distributional Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 7.80 0.57 6.77 8.98 1.00 3053 2389
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.

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.

Suppose, we have additionally asked participants for their age.

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:

```
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
Regression Coefficients:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 29.73 5.94 19.34 42.56 1.00 1443 1644
age 0.01 0.15 -0.33 0.28 1.00 1434 1506
moincome 11.56 2.44 6.40 16.17 1.00 1264 1655
moincome:age 0.08 0.06 -0.04 0.22 1.00 1274 1627
Monotonic Simplex Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
moincome1[1] 0.74 0.09 0.57 0.93 1.00 1616 1590
moincome1[2] 0.16 0.08 0.01 0.33 1.00 1944 1704
moincome1[3] 0.10 0.06 0.01 0.23 1.00 1862 1890
moincome:age1[1] 0.40 0.23 0.02 0.85 1.00 1873 1932
moincome:age1[2] 0.34 0.22 0.02 0.81 1.00 1964 2247
moincome:age1[3] 0.26 0.19 0.01 0.72 1.00 2115 2014
Further Distributional Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 7.65 0.57 6.64 8.87 1.00 2714 2599
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).
```

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:

```
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
Multilevel Hyperparameters:
~city (Number of levels: 10)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept) 14.06 4.15 8.15 24.15 1.00 1303 1905
sd(moincome) 0.90 0.73 0.03 2.70 1.00 2098 1703
cor(Intercept,moincome) 0.13 0.54 -0.89 0.95 1.00 4513 2557
Regression Coefficients:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 27.72 8.20 12.73 44.56 1.00 1135 1500
age -0.01 0.17 -0.39 0.30 1.00 1492 1576
moincome 10.30 2.73 4.18 15.19 1.00 1487 1461
moincome:age 0.11 0.07 -0.02 0.27 1.00 1405 1374
Monotonic Simplex Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
moincome1[1] 0.73 0.12 0.45 0.93 1.00 2231 1537
moincome1[2] 0.16 0.10 0.01 0.39 1.00 2853 1837
moincome1[3] 0.11 0.08 0.01 0.28 1.00 3390 2292
moincome:age1[1] 0.45 0.23 0.03 0.86 1.00 2396 2254
moincome:age1[2] 0.33 0.21 0.02 0.79 1.00 3282 2596
moincome:age1[3] 0.23 0.17 0.01 0.66 1.00 3454 2706
Further Distributional Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 7.45 0.58 6.39 8.67 1.00 5171 3215
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.

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