Make formula for measurement error and missing data model

Usage

make_inlamemi_formula(
  formula_moi,
  formula_imp,
  formula_mis = NULL,
  family_moi = "gaussian",
  error_type = "classical",
  error_variable = NULL,
  prior.beta.error,
  prior.gamma.error = NULL,
  vars = NULL
)

Arguments

formula_moi: an object of class "formula", describing the main model to be fitted.
formula_imp: an object of class "formula", describing the imputation model for the mismeasured and/or missing observations.
formula_mis: an object of class "formula", describing the missingness model. Does not need to have a response variable, since this will always be a binary missingness indicator.
family_moi: a string indicating the likelihood family for the model of interest (the main model).
error_type: type of error (one or more of "classical", "berkson", "missing")
error_variable: character vector with the name(s) of the variable(s) with error.
prior.beta.error: parameters for the Gaussian prior for the coefficient of the error prone variable.
prior.gamma.error: parameters for the Gaussian prior for the coefficient of the variable with missingness in the missingness model.
vars: Results from a call to "extract_variables_from_formula" function. If this is not passed as an argument, it is called inside the function.

Value

An object of class "formula".

Examples

make_inlamemi_formula(formula_moi = y ~ x + z,
                      formula_imp = x ~ z,
                      error_type = "classical",
                      prior.beta.error = c(0, 1/1000)
                      )
#> list(y_moi, x_classical, x_imp) ~ -1 + beta.0 + beta.z + alpha.x.0 + 
#>     alpha.x.z + f(beta.x, copy = "id.x", hyper = list(beta = list(param = c(0, 
#>     0.001), fixed = FALSE))) + f(id.x, weight.x, model = "iid", 
#>     values = 1:n, hyper = list(prec = list(initial = -15, fixed = TRUE)))
#> <environment: 0x5610770737e0>