The purpose of this vignette is to discuss the parameterizations of the families (i.e., response distributions) used in brms. For a more general overview of the package see `vignette("brms_overview")`

.

Throughout this vignette, we denote values of the response variable as \(y\), a density function as \(f\), and use \(\mu\) to refer to the main model parameter, which is usually the mean of the response distribution or some closely related quantity. In a regression framework, \(\mu\) is not estimated directly but computed as \(\mu = g(\eta)\), where \(\eta\) is a predictor term (see `help(brmsformula)`

for details) and \(g\) is the response function (i.e., inverse of the link function).

The density of the **gaussian** family is given by \[
f(y) = \frac{1}{\sqrt{2\pi}\sigma} \exp\left(-\frac{1}{2}\left(\frac{y - \mu}{\sigma}\right)^2\right)
\]

where \(\sigma\) is the residual standard deviation. The density of the **student** family is given by \[
f(y) = \frac{\Gamma((\nu + 1)/2)}{\Gamma(\nu/2)} \frac{1}{\sqrt{\nu\pi}\sigma}\left(1 + \frac{1}{\nu} \left(\frac{y - \mu}{\sigma}\right)^2\right)^{-(\nu+1)/2}
\]

\(\Gamma\) denotes the gamma function and \(\nu > 1\) are the degrees of freedom. As \(\nu \rightarrow \infty\), the student distribution becomes the gaussian distribution. The density of the **skew_normal** family is given by \[
f(y) = \frac{1}{\sqrt{2\pi}\sigma}
\exp\left(-\frac{1}{2} \left(\frac{y - \xi}{\omega}\right)^2 \right)
\left(1 + \text{erf} \left( \alpha \left(\frac{y - \xi}{\omega \sqrt{2}} \right) \right) \right)
\]

where \(\xi\) is the location parameter, \(\omega\) is the positive scale parameter, \(\alpha\) the skewness parameter, and \(\text{erf}\) denotes the error function of the gaussian distribution. To parameterize the skew-normal distribution in terms of the mean \(\mu\) and standard deviation \(\sigma\), \(\omega\) and \(\xi\) are computed as \[ \omega = \frac{\sigma}{\sqrt{1 - \frac{2}{\pi} \frac{\alpha^2}{1 + \alpha^2}}} \]

\[ \xi = \mu - \omega \frac{\alpha}{\sqrt{1 + \alpha^2}} \sqrt{\frac{2}{\pi}} \]

If \(\alpha = 0\), the skew-normal distribution becomes the gaussian distribution. For location shift models, \(y\) can be any real value.

The density of the **binomial** family is given by \[
f(y) = {N \choose y} \mu^{y} (1-\mu)^{N - y}
\] where \(N\) is the number of trials and \(y \in \{0, ... , N\}\). When all \(N\) are \(1\) (i.e., \(y \in \{0,1\}\)), the **bernoulli** distribution for binary data arises.

For \(y \in \mathbb{N}_0\), the density of the **poisson** family is given by \[
f(y) = \frac{\mu^{y}}{y!} \exp(-\mu)
\] The density of the **negbinomial** (negative binomial) family is \[
f(y) = {y + \phi - 1 \choose y} \left(\frac{\mu}{\mu + \phi}\right)^{y}
\left(\frac{\phi}{\mu + \phi}\right)^\phi
\] where \(\phi\) is a positive precision parameter. For \(\phi \rightarrow \infty\), the negative binomial distribution becomes the poisson distribution. The density of the **geometric** family arises if \(\phi\) is set to \(1\).

With survival models we mean all models that are defined on the positive reals only, that is \(y \in \mathbb{R}^+\). The density of the **lognormal** family is given by \[
f(y) = \frac{1}{\sqrt{2\pi}\sigma x} \exp\left(-\frac{1}{2}\left(\frac{\log(y) - \mu}{\sigma}\right)^2\right)
\] where \(\sigma\) is the residual standard deviation on the log-scale. The density of the **Gamma** family is given by \[
f(y) = \frac{(\alpha / \mu)^\alpha}{\Gamma(\alpha)} y^{\alpha-1}
\exp\left(-\frac{\alpha y}{\mu}\right)
\] where \(\alpha\) is a positive shape parameter. The density of the **weibull** family is given by \[
f(y) = \frac{\alpha}{s} \left(\frac{y}{s}\right)^{\alpha-1}
\exp\left(-\left(\frac{y}{s}\right)^\alpha\right)
\] where \(\alpha\) is again a positive shape parameter and \(s = \mu / \Gamma(1 + 1 / \alpha)\) is the scale parameter to that \(\mu\) is the mean of the distribution. The **exponential** family arises if \(\alpha\) is set to \(1\) for either the gamma or Weibull distribution. The density of the **inverse.gaussian** family is given by \[
f(y) = \left(\frac{\alpha}{2 \pi y^3}\right)^{1/2} \exp \left(\frac{-\alpha (y - \mu)^2}{2 \mu^2 y} \right)
\] where \(\alpha\) is a positive shape parameter.

Modeling extremes requires special distributions. One may use the **weibull** distribution (see above) or the **frechet** distribution with density \[
f(y) = \frac{\nu}{s} \left(\frac{y}{s}\right)^{-1-\nu} \exp\left(-\left(\frac{y}{s}\right)^{-\nu}\right)
\] where \(s = \mu / \Gamma(1 - 1 / \nu)\) is a positive scale parameter and \(\nu > 1\) is a shape parameter so that \(\mu\) predicts the mean of the Frechet distribution. A generalization of both distributions is the generalized extreme value distribution (family **gen_extreme_value**) with density \[
f(y) = \frac{1}{\sigma} t(y)^{-1 - 1 / \xi} \exp(-t(y))
\] where \[
t(y) = \left(1 + \xi \left(\frac{y - \mu}{\sigma} \right)\right)^{-1 / \xi}
\] with positive scale parameter \(\sigma\) and shape parameter \(\xi\).

One family that is especially suited to model reaction times is the **exgaussian** (‘exponentially modified Gaussian’) family. Its density is given by

\[ f(y) = \frac{1}{2 \beta} \exp\left(\frac{1}{2 \beta} \left(2\xi + \sigma^2 / \beta - 2 y \right) \right) \text{erfc}\left(\frac{\xi + \sigma^2 / \beta - y}{\sqrt{2} \sigma} \right) \] where \(\beta\) is the scale (inverse rate) of the exponential component, \(\xi\) is the mean of the Gaussian componenent, \(\sigma\) is the standard deviation of the Gaussian component, and \(\text{erfc}\) is the complementary error function. We parameterize \(\mu = \xi + \beta\) so that the main predictor term equals the mean of the distribution.

Another family well suited for modelling response times is the **shifted_lognormal** distribution. It’s density equals that of the **lognormal** distribution except that the whole distribution is shifted to the right by a positive parameter called *ndt* (for consistency with the **wiener** diffusion model explained below).

A family concerned with the combined modelling of reaction times and corresponding binary responses is the **wiener** diffusion model. It has four model parameters each with a natural interpreation. The parameter \(\alpha > 0\) describes the separation between two boundaries of the diffusion process, \(\tau > 0\) describes the non-decision time (e.g., due to image or motor processing), \(\beta \in [0, 1]\) describes the initial bias in favor of the upper alternative, and \(\delta \in \mathbb{R}\) describes the drift rate to the boundaries (a positive value indicates a drift towards to upper boundary). The density for the reaction time at the upper boundary is given by

\[ f(y) = \frac{\alpha}{(y-\tau)^3/2} \exp \! \left(- \delta \alpha \beta - \frac{\delta^2(y-\tau)}{2}\right) \sum_{k = - \infty}^{\infty} (2k + \beta) \phi \! \left(\frac{2k + \alpha \beta}{\sqrt{y - \tau}}\right) \]

where \(\phi(x)\) denotes the standard normal density function. The density at the lower boundary can be obtained by substituting \(1 - \beta\) for \(\beta\) and \(-\delta\) for \(\delta\) in the above equation. In brms the parameters \(\alpha\), \(\tau\), and \(\beta\) are modeled as auxiliary parameters named *bs* (‘boundary separation’), *ndt* (‘non-decision time’), and *bias* respectively, whereas the drift rate \(\delta\) is modeled via the ordinary model formula that is as \(\delta = \mu\).

Quantile regression is implemented via family **asym_laplace** (asymmetric Laplace distribution) with density

\[
f(y) = \frac{p (1 - p)}{\sigma} \exp\left(-\rho_p\left(\frac{y - \mu}{\sigma}\right)\right)
\] where \(\rho_p\) is given by \(\rho_p(x) = x (p - I_{x < 0})\) and \(I_A\) is the indicator function of set \(A\). The parameter \(\sigma\) is a positive scale parameter and \(p\) is the *quantile* parameter taking on values in \((0, 1)\). For this distribution, we have \(P(Y < g(\eta)) = p\). Thus, quantile regression can be performed by fixing \(p\) to the quantile to interest.

The density of the **Beta** family for \(y \in (0,1)\) is given by \[
f(y) = \frac{y^{\mu \phi - 1} (1-y)^{(1-\mu) \phi-1}}{B(\mu \phi, (1-\mu) \phi)}
\] where \(B\) is the beta function and \(\phi\) is a positive precision parameter. A multivariate generalization of the **Beta** family is the **dirichlet** family with density \[
f(y) = \frac{1}{B((\mu_{1}, \ldots, \mu_{K}) \phi)}
\prod_{k=1}^K y_{k}^{\mu_{k} \phi - 1}.
\] The **dirichlet** distribution is only implemented with the multivariate logit link function so that \[
\mu_{j} = \frac{\exp(\eta_{j})}{\sum_{k = 1}^{K} \exp(\eta_{k})}
\] For reasons of identifiability, \(\eta_{1}\) is set to \(0\).

The density of the **von_mises** family for \(y \in (-\pi,\pi)\) is given by \[
f(y) = \frac{\exp(\kappa \cos(y - \mu))}{2\pi I_0(\kappa)}
\] where \(I_0\) is the modified Bessel function of order 0 and \(\kappa\) is a positive precision parameter.

For ordinal and categorical models, \(y\) is one of the categories \(1, ..., K\). The intercepts of ordinal models are called thresholds and are denoted as \(\tau_k\), with \(k \in \{1, ..., K-1\}\), whereas \(\eta\) does not contain a fixed effects intercept. Note that the applied link functions \(h\) are technically distribution functions \(\mathbb{R} \rightarrow [0,1]\). The density of the **cumulative** family (implementing the most basic ordinal model) is given by \[
f(y) = g(\tau_{y + 1} - \eta) - g(\tau_{y} - \eta)
\]

The densities of the **sratio** (stopping ratio) and **cratio** (continuation ratio) families are given by \[
f(y) = g(\tau_{y + 1} - \eta) \prod_{k = 1}^{y} (1 - g(\tau_{k} - \eta))
\] and \[
f(y) = (1 - g(\eta - \tau_{y + 1})) \prod_{k = 1}^{y} g(\eta - \tau_{k})
\]

respectively. Note that both families are equivalent for symmetric link functions such as logit or probit. The density of the **acat** (adjacent category) family is given by \[
f(y) = \frac{\prod_{k=1}^{y} g(\eta - \tau_{k})
\prod_{k=y+1}^K(1-g(\eta - \tau_{k}))}{\sum_{k=0}^K\prod_{j=1}^k g(\eta-\tau_{j})
\prod_{j=k+1}^K(1-g(\eta - \tau_{j}))}
\] For the logit link, this can be simplified to \[
f(y) = \frac{\exp \left(\sum_{k=1}^{y} (\eta - \tau_{k}) \right)}
{\sum_{k=0}^K \exp\left(\sum_{j=1}^k (\eta - \tau_{j}) \right)}
\] The linear predictor \(\eta\) can be generalized to also depend on the category \(k\) for a subset of predictors. This leads to category specific effects (for details on how to specify them see `help(brm)`

). Note that **cumulative** and **sratio** models use \(\tau - \eta\), whereas **cratio** and **acat** use \(\eta - \tau\). This is done to ensure that larger values of \(\eta\) increase the probability of *higher* reponse categories.

The **categorical** family is currently only implemented with the multivariate logit link function and has density \[
f(y) = \mu_{y} = \frac{\exp(\eta_{y})}{\sum_{k = 1}^{K} \exp(\eta_{k})}
\] Note that \(\eta\) does also depend on the category \(k\). For reasons of identifiability, \(\eta_{1}\) is set to \(0\). A generalization of the **categorical** family to more than one trial is the **multinomial** family with density \[
f(y) = {N \choose y_{1}, y_{2}, \ldots, y_{K}}
\prod_{k=1}^K \mu_{k}^{y_{k}}
\] where, for each category, \(\mu_{k}\) is estimated via the multivariate logit link function shown above.

**Zero-inflated** and **hurdle** families extend existing families by adding special processes for responses that are zero. The densitiy of a **zero-inflated** family is given by \[
f_z(y) = z + (1 - z) f(0) \quad \text{if } y = 0 \\
f_z(y) = (1 - z) f(y) \quad \text{if } y > 0
\] where \(z\) denotes the zero-inflation probability. Currently implemented families are **zero_inflated_poisson**, **zero_inflated_binomial**, **zero_inflated_negbinomial**, and **zero_inflated_beta**.

The density of a **hurdle** family is given by \[
f_z(y) = z \quad \text{if } y = 0 \\
f_z(y) = (1 - z) f(y) / (1 - f(0)) \quad \text{if } y > 0
\] Currently implemented families are **hurdle_poisson**, **hurdle_negbinomial**, **hurdle_gamma**, and **hurdle_lognormal**.

The density of a **zero-one-inflated** family is given by \[
f_{\alpha, \gamma}(y) = \alpha (1 - \gamma) \quad \text{if } y = 0 \\
f_{\alpha, \gamma}(y) = \alpha \gamma \quad \text{if } y = 1 \\
f_{\alpha, \gamma}(y) = (1 - \alpha) f(y) \quad \text{if } y \notin \{0, 1\}
\] where \(\alpha\) is the zero-one-inflation probability (i.e. the probability that zero or one occurs) and \(\gamma\) is the conditional one-inflation probability (i.e. the probability that one occurs rather than zero). Currently implemented families are **zero_one_inflated_beta**.