generate_MAPC_formula: Generate MAPC formula for INLA

Description

Based on APC-format, generate the proper formula to pass to INLA for fitting MAPC models.

Usage

generate_MAPC_formula(
  df,
  APC_format,
  response,
  stratify_var,
  age = "age",
  period = "period",
  cohort = "cohort",
  intercept = FALSE,
  apc_prior = "rw1",
  apc_hyper = NULL,
  random_term = TRUE,
  extra.fixed = NULL,
  extra.random = NULL,
  extra.models = NULL,
  extra.hyper = NULL
)

Value

A formula object that can be passed to INLA to fit the desired MAPC model.

Arguments

df: Data frame for which MAPC models should be fit
APC_format: A string where lower-case letters indicate stratum-specific time effects and upper-case letters indicate shared time effects.
response: A string, name of the column in df that represents the response variable.
stratify_var: Stratification variable. At least one time effect should be stratum-specific, and at least one should be shared.
age: Name of age column
period: Name of period column
cohort: Name of cohort column
intercept: Boolean, indicating if an overall intercept should be included in the formula.
Defaults to TRUE (optional).
apc_prior: Which prior model to use for the time effects.
Defaults to "rw1" (optional).
apc_hyper: If the user wants non-default hyperpriors for the random time effects, this can be achieved by passing the entire prior specification as a string. If e.g. hyper = list(theta = list(prior="pc.prec", param=c(0.5,0.01))) is desired, pass the string "list(theta = list(prior="pc.prec", param=c(0.5,0.01)))" to this argument.
random_term: Indicator, indicating if a random term should be included in the model.
Defaults to TRUE (optional).
extra.fixed: Name of additional fixed effects.
Defaults to NULL (optional).
extra.random: Name of additional random effects.
Defaults to NULL (optional).
extra.models: Models for additional random effects. Supported INLA models include 'iid', 'rw1' and 'rw2'.
Defaults to NULL (optional).
extra.hyper: If the user wants non-default hyperpriors for the additional random effects, this can be achieved by passing the entire prior specification as a string. If e.g. hyper = list(theta = list(prior="pc.prec", param=c(0.5,0.01))) is desired, pass the string "list(theta = list(prior="pc.prec", param=c(0.5,0.01)))" to this argument.