Calculate derived parameters.
Usage
# S3 method for class 'mb_analysis'
mcmc_derive_data(
object,
new_data = data_set(object),
new_expr = NULL,
new_values = list(),
term = "prediction",
modify_new_data = NULL,
ref_data = FALSE,
ref_fun2 = proportional_change2,
new_expr_vec = getOption("mb.new_expr_vec", FALSE),
random_effects = NULL,
parallel = getOption("mb.parallel", FALSE),
quiet = getOption("mb.quiet", TRUE),
beep = getOption("mb.beep", FALSE),
...
)Arguments
- object
An object inheriting from class mb_analysis.
- new_data
The data frame to calculate the predictions for.
- new_expr
A string of R code specifying the predictive relationship.
- new_values
A named list of new or replacement values to pass to new_expr.
- term
A string of the term in new_expr.
- modify_new_data
A single argument function to modify new data (in list form) immediately prior to calculating new_expr.
- ref_data
A flag or a data frame with 1 row indicating the reference values for calculating the effects size.
- ref_fun2
A function whose first argument takes a vector of two numbers and returns a scalar of a metric of the difference between them.
- new_expr_vec
A flag specifying whether to vectorize the new_expr code.
- random_effects
A named list specifying the random effects and the associated factors.
- parallel
A flag indicating whether to do predictions using parallel backend provided by foreach.
- quiet
A flag indicating whether to disable tracing information.
- beep
A flag indicating whether to beep on completion of the analysis.
- ...
Additional arguments.