custom_family | R Documentation |

Define custom families (i.e. response distribution) for use in
brms models. It allows users to benefit from the modeling
flexibility of brms, while applying their self-defined likelihood
functions. All of the post-processing methods for `brmsfit`

objects can be made compatible with custom families.
See `vignette("brms_customfamilies")`

for more details.
For a list of built-in families see `brmsfamily`

.

```
custom_family(
name,
dpars = "mu",
links = "identity",
type = c("real", "int"),
lb = NA,
ub = NA,
vars = NULL,
loop = TRUE,
specials = NULL,
threshold = "flexible",
log_lik = NULL,
posterior_predict = NULL,
posterior_epred = NULL,
predict = NULL,
fitted = NULL,
env = parent.frame()
)
```

`name` |
Name of the custom family. |

`dpars` |
Names of the distributional parameters of
the family. One parameter must be named |

`links` |
Names of the link functions of the distributional parameters. |

`type` |
Indicates if the response distribution is
continuous ( |

`lb` |
Vector of lower bounds of the distributional
parameters. Defaults to |

`ub` |
Vector of upper bounds of the distributional
parameters. Defaults to |

`vars` |
Names of variables that are part of the likelihood function
without being distributional parameters. That is, |

`loop` |
Logical; Should the likelihood be evaluated via a loop
( |

`specials` |
A character vector of special options to enable for this custom family. Currently for internal use only. |

`threshold` |
Optional threshold type for custom ordinal families. Ignored for non-ordinal families. |

`log_lik` |
Optional function to compute log-likelihood values of
the model in |

`posterior_predict` |
Optional function to compute posterior prediction of
the model in |

`posterior_epred` |
Optional function to compute expected values of the
posterior predictive distribution of the model in |

`predict` |
Deprecated alias of 'posterior_predict'. |

`fitted` |
Deprecated alias of 'posterior_epred'. |

`env` |
An |

The corresponding probability density or mass `Stan`

functions need to have the same name as the custom family.
That is if a family is called `myfamily`

, then the
Stan functions should be called `myfamily_lpdf`

or
`myfamily_lpmf`

depending on whether it defines a
continuous or discrete distribution.

An object of class `customfamily`

inheriting
from class `brmsfamily`

.

`brmsfamily`

, `brmsformula`

,
`stanvar`

```
## Not run:
## demonstrate how to fit a beta-binomial model
## generate some fake data
phi <- 0.7
n <- 300
z <- rnorm(n, sd = 0.2)
ntrials <- sample(1:10, n, replace = TRUE)
eta <- 1 + z
mu <- exp(eta) / (1 + exp(eta))
a <- mu * phi
b <- (1 - mu) * phi
p <- rbeta(n, a, b)
y <- rbinom(n, ntrials, p)
dat <- data.frame(y, z, ntrials)
# define a custom family
beta_binomial2 <- custom_family(
"beta_binomial2", dpars = c("mu", "phi"),
links = c("logit", "log"), lb = c(NA, 0),
type = "int", vars = "vint1[n]"
)
# define the corresponding Stan density function
stan_density <- "
real beta_binomial2_lpmf(int y, real mu, real phi, int N) {
return beta_binomial_lpmf(y | N, mu * phi, (1 - mu) * phi);
}
"
stanvars <- stanvar(scode = stan_density, block = "functions")
# fit the model
fit <- brm(y | vint(ntrials) ~ z, data = dat,
family = beta_binomial2, stanvars = stanvars)
summary(fit)
# define a *vectorized* custom family (no loop over observations)
# notice also that 'vint' no longer has an observation index
beta_binomial2_vec <- custom_family(
"beta_binomial2", dpars = c("mu", "phi"),
links = c("logit", "log"), lb = c(NA, 0),
type = "int", vars = "vint1", loop = FALSE
)
# define the corresponding Stan density function
stan_density_vec <- "
real beta_binomial2_lpmf(int[] y, vector mu, real phi, int[] N) {
return beta_binomial_lpmf(y | N, mu * phi, (1 - mu) * phi);
}
"
stanvars_vec <- stanvar(scode = stan_density_vec, block = "functions")
# fit the model
fit_vec <- brm(y | vint(ntrials) ~ z, data = dat,
family = beta_binomial2_vec,
stanvars = stanvars_vec)
summary(fit_vec)
## End(Not run)
```

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