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Simulate Model Parameters

Source: R/simulate_parameters.R
simulate_parameters.Rd

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.

Usage

simulate_parameters(model, ...)

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

Arguments

model

Statistical model (no Bayesian models).

...

Arguments passed to insight::get_varcov(), e.g. to allow simulated draws to be based on heteroscedasticity consistent variance covariance matrices.

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" (see map_estimate()), "trimmed" (which is just mean(x, trim = threshold)), "mode" or "all".

ci

Value or vector of probability of the CI (between 0 and 1) to be estimated. Default to 0.95 (95%).

ci_method

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

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

bootstrap_model(), bootstrap_parameters(), simulate_model()

Examples

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,  3.84] | < .001
#> Speciesversicolor             |       -1.15 | [-1.85, -0.46] | < .001
#> Speciesvirginica              |       -2.26 | [-3.13, -1.44] | < .001
#> Petal.Width                   |        0.40 | [-0.40,  1.34] | 0.358 
#> Petal.Length                  |        0.94 | [ 0.79,  1.09] | < .001
#> Speciesversicolor:Petal.Width |       -0.75 | [-1.82,  0.25] | 0.142 
#> Speciesvirginica:Petal.Width  |       -0.34 | [-1.36,  0.47] | 0.436 
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed)
#>   computed using a simulated multivariate normal distribution
#>   approximation.

# \donttest{
if (require("glmmTMB", quietly = TRUE)) {
  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")
}
#> # Fixed Effects
#> 
#> Parameter   | Coefficient |         80% CI |         95% CI |      p
#> --------------------------------------------------------------------
#> (Intercept) |        0.81 | [ 0.44,  1.16] | [ 0.25,  1.35] | 0.006 
#> minedno     |       -1.85 | [-2.27, -1.43] | [-2.45, -1.23] | < .001
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed)
#>   computed using a MCMC distribution approximation.
# }