Parameters bootstrapping
Compute bootstrapped parameters and their related indices such as Confidence Intervals (CI) and p-values.
bootstrap_parameters( model, iterations = 1000, centrality = "median", ci = 0.95, ci_method = "quantile", test = "p-value", ... )
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: |
ci |
Value or vector of probability of the CI (between 0 and 1)
to be estimated. Default to |
ci_method |
The type of index used for Credible Interval. Can be
|
test |
The indices to compute. Character (vector) with one or more of these options: |
... |
Arguments passed to or from other methods. |
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.
Note that that p-values returned here are estimated under the assumption of
translation equivariance: that shape of the sampling distribution is
unaffected by the null being true or not. If this assumption does not hold,
p-values can be biased, and it is suggested to use proper permutation tests
to obtain non-parametric p-values.
A data frame summarizing the bootstrapped parameters.
emmeans
The output can be passed directly to the various functions from the
emmeans package, to obtain bootstrapped estimates, contrasts, simple
slopes, etc, and their confidence intervals. These can then be passed to
model_parameter() to obtain standard errors, p-values, etc (see
example).
Note that that p-values returned here are estimated under the assumption of
translation equivariance: that shape of the sampling distribution is
unaffected by the null being true or not. If this assumption does not hold,
p-values can be biased, and it is suggested to use proper permutation tests
to obtain non-parametric p-values.
Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their application (Vol. 1). Cambridge university press.
## Not run:
if (require("boot", quietly = TRUE)) {
set.seed(2)
model <- lm(Sepal.Length ~ Species * Petal.Width, data = iris)
b <- bootstrap_parameters(model)
print(b)
if (require("emmeans")) {
est <- emmeans(b, trt.vs.ctrl ~ Species)
print(model_parameters(est))
}
}
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