parameters (version 0.8.5)

bootstrap_parameters: Parameters bootstrapping

Description

Compute bootstrapped parameters and their related indices such as Confidence Intervals (CI) and p-values.

Usage

bootstrap_parameters(
  model,
  iterations = 1000,
  centrality = "median",
  ci = 0.95,
  ci_method = "quantile",
  test = "p-value",
  ...
)

Arguments

model

Statistical model.

iterations

The number of draws to simulate/bootstrap.

centrality

The point-estimates (centrality indices) to compute. Character (vector) or list with one or more of these options: "median", "mean", "MAP" or "all".

ci

Value or vector of probability of the CI (between 0 and 1) to be estimated. Default to .89 (89%) for Bayesian models and .95 (95%) for frequentist models.

ci_method

The type of index used for Credible Interval. Can be "HDI" (default, see hdi), "ETI" (see eti) or "SI" (see si).

test

The indices to compute. Character (vector) with one or more of these options: "p-value" (or "p"), "p_direction" (or "pd"), "rope", "p_map", "equivalence_test" (or "equitest"), "bayesfactor" (or "bf") or "all" to compute all tests. For each "test", the corresponding bayestestR function is called (e.g. rope or p_direction) and its results included in the summary output.

...

Arguments passed to or from other methods.

Value

Bootstrapped parameters.

Details

This function first calls bootstrap_model to generate bootstrapped coefficients. The resulting replicated for each coefficient are treated as "distribution", and is passed to describe_posterior() to calculate the related indices defined in the "test" argument.

References

Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their application (Vol. 1). Cambridge university press.

See Also

bootstrap_model, simulate_parameters, simulate_model

Examples

# NOT RUN {
library(parameters)

model <- lm(Sepal.Length ~ Species * Petal.Width, data = iris)
bootstrap_parameters(model)
# }