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Model families for bml

Source: R/families.R
bml-families.Rd

Family functions specify the outcome distribution and link function of a bml model, following the brms/glm convention of passing a family function rather than a string:

  • gaussian(): normal outcome, identity link

  • bernoulli() (alias binomial()): binary outcome, logit link

  • weibull(): Weibull survival model; outcome Surv(time, event)

  • cox(intervals = NULL): Cox proportional hazards model; outcome Surv(time, event). intervals controls the baseline hazard: NULL uses all unique event times (non-parametric); an integer k uses a piecewise-constant baseline hazard with k intervals (faster).

Family strings are accepted as first-class input too (as in brms): family = "gaussian", "bernoulli", "weibull", "cox".

Usage

bernoulli(link = "logit")

weibull(link = "log")

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

Arguments

Link function. Only the canonical links are supported by the JAGS backend ("identity" for gaussian, "logit" for bernoulli, "log" for weibull/cox); passing anything else errors.

intervals

For cox(): NULL for a non-parametric baseline hazard (all unique event times), or a positive integer number of intervals for a piecewise-constant baseline hazard.

Value

A bml_family object (a list with elements family and link, plus intervals for cox()).

Examples

if (FALSE) { # \dontrun{
bml(y ~ x + mm(...), data = dat, family = gaussian())
bml(y01 ~ x + mm(...), data = dat, family = bernoulli())
bml(Surv(t, ev) ~ x + mm(...), data = dat, family = weibull())
bml(Surv(t, ev) ~ x + mm(...), data = dat, family = cox(intervals = 10))
} # }