Compute simulated draws of parameters and their related indices such as Confidence Intervals (CI) and p-values. Simulating parameter draws can be seen as a (computationally faster) alternative to bootstrapping.

# S3 method for glmmTMB
simulate_parameters(
  model,
  iterations = 1000,
  centrality = "median",
  ci = 0.95,
  ci_method = "quantile",
  test = "p-value",
  ...
)

simulate_parameters(model, ...)

# S3 method for default
simulate_parameters(
  model,
  iterations = 1000,
  centrality = "median",
  ci = 0.95,
  ci_method = "quantile",
  test = "p-value",
  ...
)

Arguments

model

Statistical model (no Bayesian models).

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 .95 (95%).

ci_method

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

test

The indices of effect existence to compute. Character (vector) or list with one or more of these options: "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

A data frame with simulated parameters.

Details

Technical Details

simulate_parameters() is a computationally faster alternative to bootstrap_parameters(). Simulated draws for coefficients are based on a multivariate normal distribution (MASS::mvrnorm()) with mean mu = coef(model) and variance Sigma = vcov(model).

Models with Zero-Inflation Component

For models from packages glmmTMB, pscl, GLMMadaptive and countreg, the component argument can be used to specify which parameters should be simulated. For all other models, parameters from the conditional component (fixed effects) are simulated. This may include smooth terms, but not random effects.

Note

There is also a plot()-method implemented in the see-package.

References

Gelman A, Hill J. Data analysis using regression and multilevel/hierarchical models. Cambridge; New York: Cambridge University Press 2007: 140-143

See also

Examples

library(parameters)

model <- lm(Sepal.Length ~ Species * Petal.Width + Petal.Length, data = iris)
simulate_parameters(model)
#> # Fixed Effects
#> 
#> Parameter                     | Coefficient |         95% CI |      p
#> ---------------------------------------------------------------------
#> (Intercept)                   |        3.53 | [ 3.21,  3.83] | < .001
#> Speciesversicolor             |       -1.17 | [-1.84, -0.50] | < .001
#> Speciesvirginica              |       -2.27 | [-3.14, -1.39] | < .001
#> Petal.Width                   |        0.40 | [-0.48,  1.32] | 0.411 
#> Petal.Length                  |        0.94 | [ 0.79,  1.09] | < .001
#> Speciesversicolor:Petal.Width |       -0.73 | [-1.73,  0.27] | 0.183 
#> Speciesvirginica:Petal.Width  |       -0.37 | [-1.33,  0.53] | 0.487 
if (FALSE) {
if (require("glmmTMB")) {
  model <- glmmTMB(
    count ~ spp + mined + (1 | site),
    ziformula = ~mined,
    family = poisson(),
    data = Salamanders
  )
  simulate_parameters(model, centrality = "mean")
  simulate_parameters(model, ci = c(.8, .95), component = "zero_inflated")
}
}