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.
simulate_parameters(model, ...)# S3 method for default
simulate_parameters(
model,
iterations = 1000,
centrality = "median",
ci = 0.95,
ci_method = "quantile",
test = "p-value",
...
)
Statistical model (no Bayesian models).
Arguments passed to or from other methods.
The number of draws to simulate/bootstrap.
The point-estimates (centrality indices) to compute. Character (vector) or list with one or more of these options: "median", "mean", "MAP" or "all".
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.
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.
A data frame with simulated parameters.
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).
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.
Gelman A, Hill J. Data analysis using regression and multilevel/hierarchical models. Cambridge; New York: Cambridge University Press 2007: 140-143
# NOT RUN {
library(parameters)
model <- lm(Sepal.Length ~ Species * Petal.Width + Petal.Length, data = iris)
simulate_parameters(model)
# }
# NOT RUN {
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")
}
# }
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