Bayesian inference for the Type 1 moult model

uz1_linpred(
  moult_cat_column,
  date_column,
  start_formula = ~1,
  duration_formula = ~1,
  sigma_formula = ~1,
  lump_non_moult = FALSE,
  data,
  init = "auto",
  log_lik = TRUE,
  ...
)

Arguments

moult_cat_column

the name the column in data containing moult categories, i.e. a numeric vector of categorical moult codes (1 = old plumage,2 = moulting,3 = new plumage)

date_column

the name the column in data containing sampling dates, encoded as days since an arbitrary reference date, i.e. a numeric vector

start_formula

model formula for start date

duration_formula

model formula for duration

sigma_formula

model formula for start date sigma

lump_non_moult

logical; should pre- and post-moult observations be treated as indistinguishable? if TRUE, the type 2L model will be fitted.

data

Input data frame

init

Specification of initial values for all or some parameters. Can be the string "auto" for an automatic guess based on the data, or any of the permitted rstan options: the digit 0, the strings "0" or "random", or a function. See the detailed documentation for the init argument in ?rstan::stan.

log_lik

boolean retain pointwise log-likelihood in output? This enables model assessment and selection via the loo package. Defaults to true, can lead to very large output arrays if sample size is large.

...

Arguments passed to rstan::sampling (e.g. iter, chains).

Value

An object of class stanfit returned by rstan::sampling