glmmTMB (version 1.1.10)

priors: use of priors in glmmTMB

Description

(EXPERIMENTAL/subject to change)

Details

glmmTMB can accept prior specifications, for doing maximum a posteriori (MAP) estimation (or Hamiltonian MC with the tmbstan package), or (outside of a Bayesian framework) for the purposes of regularizing parameter estimates

The priors argument to glmmTMB must (if not NULL) be a data frame with columns

prior

character; the prior specification, e.g. "normal(0,2)"

class

the name of the underlying parameter vector on which to impose the prior ("fixef", "fixef_zi", "fixef_disp", "ranef", "ranef_zi", "psi")

coef

(optional) a string (if present) specifying the particular elements of the parameter vector to apply the prior to. coef should specify an integer parameter index, a column name from the fixed effect model matrix or a grouping variable for a random effect (the behaviour is currently undefined if there is more one than random effect term with the same grouping variable in a model ...); one can also append "_cor" or "_sd" to a random-effects class specification to denote the correlation parameters, or all of the standard deviation parameters, corresponding to a particular random effect term. If the class element is missing, or a particular element is blank, then all of the elements of the specified parameter vector use independent priors with the given specification. The exception is for the fixed-effect parameter vectors ("fixef", "fixef_zi", "fixef_disp"), where the intercept (if present) is not included; the prior on the intercept must be set explicitly.

`The available prior distributions are:

• "normal" (mean/sd parameterization)

• "t" (mean/sd/df)

• "cauchy" (location/scale)

• "gamma" (mean/shape); applied on the SD (not the log-SD) scale

• "lkj" (correlation) [WARNING, maybe buggy at present!]

The first three are typically used for fixed effect parameters; the fourth for standard deviation parameters; and the last for correlation structures. See the "priors" vignette for examples and further information.

Examples

data("sleepstudy", package = "lme4")
prior1 <- data.frame(prior = c("normal(250,3)","t(0,3,3)","gamma(10,1)"),
                     class = c("fixef", "fixef", "ranef_sd"),
                     coef = c("(Intercept)", "Days", "Subject"))
g1 <- glmmTMB(Reaction ~ 1 + Days + (1 + Days |Subject), sleepstudy)
update(g1, priors = prior1)
prior2 <- data.frame(prior = c("t(0,3,3)","gamma(10,1)"),
                     class = c("fixef", "ranef_sd"),
                     coef = c("", "Subject"))
update(g1, priors = prior2)
## no prior is set for the intercept in this case - see Details above
prior3 <- data.frame(prior = "t(0, 3, 3)",
                     class = "fixef")
update(g1, priors = prior3)