Creates a JAGS formula syntax, prepares data input, and
returns modified prior list for further processing in the JAGS_fit
function.
JAGS_formula(formula, parameter, data, prior_list)formula specifying the right hand side of the assignment (the
left hand side is ignored). If the formula contains -1, it will be
automatically converted to include an intercept with a spike(0) prior.
name of the parameter to be created with the formula
data.frame containing predictors included in the formula
named list of prior distribution of parameters specified within
the formula. When using -1 in the formula, an "intercept" prior
can be explicitly specified; otherwise, prior("spike", list(0)) is
automatically added.
JAGS_formula returns a list containing the formula JAGS syntax,
JAGS data object, and modified prior_list.
When a formula with -1 (no intercept) is specified, the
function automatically removes the -1, adds an intercept back to the
formula, and includes a spike(0) prior for the intercept to ensure equivalent
model behavior while maintaining consistent formula parsing.
# simulate data
set.seed(1)
df <- data.frame(
y = rnorm(60),
x_cont = rnorm(60),
x_bin = rbinom(60, 1, .5),
x_fac3 = factor(rep(c("A", "B", "C"), 20), levels = c("A", "B", "C")),
x_fac4 = factor(rep(c("A", "B", "C", "D"), 15), levels = c("A", "B", "C", "D"))
)
# specify priors with intercept
prior_list <- list(
"intercept" = prior("normal", list(0, 1)),
"x_cont" = prior("normal", list(0, .5)),
"x_fac3" = prior_factor("normal", list(0, 1), contrast = "treatment"),
"x_fac4" = prior_factor("mnormal", list(0, 1), contrast = "orthonormal"),
"x_fac3:x_fac4" = prior_factor("mnormal", list(0, .5), contrast = "orthonormal")
)
# create the formula object
formula_obj <- JAGS_formula(
formula = ~ x_cont + x_fac3 * x_fac4,
parameter = "mu", data = df, prior_list = prior_list)
# using -1 notation (automatically adds spike(0) intercept)
prior_list_no_intercept <- list(
"x_fac3" = prior_factor("normal", list(0, 1), contrast = "treatment")
)
formula_no_intercept <- JAGS_formula(
formula = ~ x_fac3 - 1,
parameter = "mu", data = df, prior_list = prior_list_no_intercept)
# Equivalent to specifying intercept = prior("spike", list(0))