Derive Data

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

A data frame at which to derive the term. Pass character(0) to extract a scalar term from new_expr.

new_expr

An R expression (e.g. { ... }) or a character string of R code specifying the predictive relationship. If NULL, uses the expression set in model() and stored in the mb_analysis object.

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. If FALSE, no reference applied. If TRUE, the reference is a 1 row data.frame with all variables held at reference value.

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.

Value

A object of class mcmc_data.

See also

• The prediction article for worked patterns including group-level summaries.

• predict.mb_analysis() for tidy posterior summaries at new covariate values.

• mcmc_derive.mb_analysis() for scalar derived quantities and arithmetic on mcmcr posteriors.

• mcmcr::combine_samples() for combining MCMC samples across independent analyses on shared data keys.

• newdata::xnew_data() for building covariate grids.

Examples

if (FALSE) { # \dontrun{
# `analysis` is a fitted mb_analysis with factors site, treatment and
# new_expr that defines per-row term `eCount`.
data <- data_set(analysis)

# Per-group posterior summaries via group_by() + summarise().
# Default .fun = sum; use mean for the per-treatment posterior mean.
mcmc_derive_data(analysis, new_data = data, term = "^eCount$") |>
  group_by(treatment) |>
  summarise(.fun = mean) |>
  coef()

# Custom summarise function, e.g. range within group
mcmc_derive_data(analysis, new_data = data, term = "^eCount$") |>
  group_by(treatment) |>
  summarise(.fun = function(x) max(x) - min(x)) |>
  coef()
} # }