brmsfamily {brms}

Special Family Functions for brms Models

Description

Family objects provide a convenient way to specify the details of the models used by many model fitting functions. The family functions presented here are for use with brms only and will **not** work with other model fitting functions such as glm or glmer. However, the standard family functions as described in family will work with brms. You can also specify custom families for use in brms with the custom_family function.

Usage

brmsfamily(
  family,
  link = NULL,
  link_sigma = "log",
  link_shape = "log",
  link_nu = "logm1",
  link_phi = "log",
  link_kappa = "log",
  link_beta = "log",
  link_zi = "logit",
  link_hu = "logit",
  link_zoi = "logit",
  link_coi = "logit",
  link_disc = "log",
  link_bs = "log",
  link_ndt = "log",
  link_bias = "logit",
  link_xi = "log1p",
  link_alpha = "identity",
  link_quantile = "logit",
  threshold = "flexible",
  refcat = NULL,
  bhaz = NULL
)

student(link = "identity", link_sigma = "log", link_nu = "logm1")

bernoulli(link = "logit")

beta_binomial(link = "logit", link_phi = "log")

negbinomial(link = "log", link_shape = "log")

geometric(link = "log")

lognormal(link = "identity", link_sigma = "log")

shifted_lognormal(link = "identity", link_sigma = "log", link_ndt = "log")

skew_normal(link = "identity", link_sigma = "log", link_alpha = "identity")

exponential(link = "log")

weibull(link = "log", link_shape = "log")

frechet(link = "log", link_nu = "logm1")

gen_extreme_value(link = "identity", link_sigma = "log", link_xi = "log1p")

exgaussian(link = "identity", link_sigma = "log", link_beta = "log")

wiener(
  link = "identity",
  link_bs = "log",
  link_ndt = "log",
  link_bias = "logit"
)

Beta(link = "logit", link_phi = "log")

dirichlet(link = "logit", link_phi = "log", refcat = NULL)

logistic_normal(link = "identity", link_sigma = "log", refcat = NULL)

von_mises(link = "tan_half", link_kappa = "log")

asym_laplace(link = "identity", link_sigma = "log", link_quantile = "logit")

cox(link = "log", bhaz = NULL)

hurdle_poisson(link = "log", link_hu = "logit")

hurdle_negbinomial(link = "log", link_shape = "log", link_hu = "logit")

hurdle_gamma(link = "log", link_shape = "log", link_hu = "logit")

hurdle_lognormal(link = "identity", link_sigma = "log", link_hu = "logit")

hurdle_cumulative(
  link = "logit",
  link_hu = "logit",
  link_disc = "log",
  threshold = "flexible"
)

zero_inflated_beta(link = "logit", link_phi = "log", link_zi = "logit")

zero_one_inflated_beta(
  link = "logit",
  link_phi = "log",
  link_zoi = "logit",
  link_coi = "logit"
)

zero_inflated_poisson(link = "log", link_zi = "logit")

zero_inflated_negbinomial(link = "log", link_shape = "log", link_zi = "logit")

zero_inflated_binomial(link = "logit", link_zi = "logit")

zero_inflated_beta_binomial(
  link = "logit",
  link_phi = "log",
  link_zi = "logit"
)

categorical(link = "logit", refcat = NULL)

multinomial(link = "logit", refcat = NULL)

cumulative(link = "logit", link_disc = "log", threshold = "flexible")

sratio(link = "logit", link_disc = "log", threshold = "flexible")

cratio(link = "logit", link_disc = "log", threshold = "flexible")

acat(link = "logit", link_disc = "log", threshold = "flexible")

Arguments

family

A character string naming the distribution family of the response variable to be used in the model. Currently, the following families are supported: gaussian, student, binomial, bernoulli, beta-binomial, poisson, negbinomial, geometric, Gamma, skew_normal, lognormal, shifted_lognormal, exgaussian, wiener, inverse.gaussian, exponential, weibull, frechet, Beta, dirichlet, von_mises, asym_laplace, gen_extreme_value, categorical, multinomial, cumulative, cratio, sratio, acat, hurdle_poisson, hurdle_negbinomial, hurdle_gamma, hurdle_lognormal, hurdle_cumulative, zero_inflated_binomial, zero_inflated_beta_binomial, zero_inflated_beta, zero_inflated_negbinomial, zero_inflated_poisson, and zero_one_inflated_beta.

link

A specification for the model link function. This can be a name/expression or character string. See the 'Details' section for more information on link functions supported by each family.

link_sigma

Link of auxiliary parameter sigma if being predicted.

link_shape

Link of auxiliary parameter shape if being predicted.

link_nu

Link of auxiliary parameter nu if being predicted.

link_phi

Link of auxiliary parameter phi if being predicted.

link_kappa

Link of auxiliary parameter kappa if being predicted.

link_beta

Link of auxiliary parameter beta if being predicted.

link_zi

Link of auxiliary parameter zi if being predicted.

link_hu

Link of auxiliary parameter hu if being predicted.

link_zoi

Link of auxiliary parameter zoi if being predicted.

link_coi

Link of auxiliary parameter coi if being predicted.

link_disc

Link of auxiliary parameter disc if being predicted.

link_bs

Link of auxiliary parameter bs if being predicted.

link_ndt

Link of auxiliary parameter ndt if being predicted.

link_bias

Link of auxiliary parameter bias if being predicted.

link_xi

Link of auxiliary parameter xi if being predicted.

link_alpha

Link of auxiliary parameter alpha if being predicted.

link_quantile

Link of auxiliary parameter quantile if being predicted.

threshold

A character string indicating the type of thresholds (i.e. intercepts) used in an ordinal model. "flexible" provides the standard unstructured thresholds, "equidistant" restricts the distance between consecutive thresholds to the same value, and "sum_to_zero" ensures the thresholds sum to zero.

refcat

Optional name of the reference response category used in categorical, multinomial, dirichlet and logistic_normal models. If NULL (the default), the first category is used as the reference. If NA, all categories will be predicted, which requires strong priors or carefully specified predictor terms in order to lead to an identified model.

bhaz

Currently for experimental purposes only.

Details

Below, we list common use cases for the different families. This list is not ment to be exhaustive.

• Family gaussian can be used for linear regression.

• Family student can be used for robust linear regression that is less influenced by outliers.

• Family skew_normal can handle skewed responses in linear regression.

• Families poisson, negbinomial, and geometric can be used for regression of unbounded count data.

• Families bernoulli, binomial, and beta_binomial can be used for binary regression (i.e., most commonly logistic regression).

• Families categorical and multinomial can be used for multi-logistic regression when there are more than two possible outcomes.

• Families cumulative, cratio ('continuation ratio'), sratio ('stopping ratio'), and acat ('adjacent category') leads to ordinal regression.

• Families Gamma, weibull, exponential, lognormal, frechet, inverse.gaussian, and cox (Cox proportional hazards model) can be used (among others) for time-to-event regression also known as survival regression.

• Families weibull, frechet, and gen_extreme_value ('generalized extreme value') allow for modeling extremes.

• Families beta, dirichlet, and logistic_normal can be used to model responses representing rates or probabilities.

• Family asym_laplace allows for quantile regression when fixing the auxiliary quantile parameter to the quantile of interest.

• Family exgaussian ('exponentially modified Gaussian') and shifted_lognormal are especially suited to model reaction times.

• Family wiener provides an implementation of the Wiener diffusion model. For this family, the main formula predicts the drift parameter 'delta' and all other parameters are modeled as auxiliary parameters (see brmsformula for details).

• Families hurdle_poisson, hurdle_negbinomial, hurdle_gamma, hurdle_lognormal, zero_inflated_poisson, zero_inflated_negbinomial, zero_inflated_binomial, zero_inflated_beta_binomial, zero_inflated_beta, zero_one_inflated_beta, and hurdle_cumulative allow to estimate zero-inflated and hurdle models. These models can be very helpful when there are many zeros in the data (or ones in case of one-inflated models) that cannot be explained by the primary distribution of the response.

Below, we list all possible links for each family. The first link mentioned for each family is the default.

• Families gaussian, student, skew_normal, exgaussian, asym_laplace, and gen_extreme_value support the links (as names) identity, log, inverse, and softplus.

• Families poisson, negbinomial, geometric, zero_inflated_poisson, zero_inflated_negbinomial, hurdle_poisson, and hurdle_negbinomial support log, identity, sqrt, and softplus.

• Families binomial, bernoulli, beta_binomial, zero_inflated_binomial, zero_inflated_beta_binomial, Beta, zero_inflated_beta, and zero_one_inflated_beta support logit, probit, probit_approx, cloglog, cauchit, identity, and log.

• Families cumulative, cratio, sratio, acat, and hurdle_cumulative support logit, probit, probit_approx, cloglog, and cauchit.

• Families categorical, multinomial, and dirichlet support logit.

• Families Gamma, weibull, exponential, frechet, and hurdle_gamma support log, identity, inverse, and softplus.

• Families lognormal and hurdle_lognormal support identity and inverse.

• Family logistic_normal supports identity.

• Family inverse.gaussian supports 1/mu^2, inverse, identity, log, and softplus.

• Family von_mises supports tan_half and identity.

• Family cox supports log, identity, and softplus for the proportional hazards parameter.

• Family wiener supports identity, log, and softplus for the main parameter which represents the drift rate.

Please note that when calling the Gamma family function of the stats package, the default link will be inverse instead of log although the latter is the default in brms. Also, when using the family functions gaussian, binomial, poisson, and Gamma of the stats package (see family), special link functions such as softplus or cauchit won't work. In this case, you have to use brmsfamily to specify the family with corresponding link function.

See Also

brm, family, customfamily

Examples

 # create a family object
 (fam1 <- student("log"))
 # alternatively use the brmsfamily function
 (fam2 <- brmsfamily("student", "log"))
 # both leads to the same object
 identical(fam1, fam2)