Bootstrap Confidence Intervals
Calculate confidence intervals from bootstrapped model effects.
bootCI(mod, conf = 0.95, type = "bca", digits = 3, bci.arg = NULL, ...)
mod |
A fitted model object. Alternatively, a boot object (class
|
conf |
A numeric value specifying the confidence level for the intervals. |
type |
The type of confidence interval to return (defaults to
|
digits |
The number of significant digits to return for numeric values. |
bci.arg |
A named list of any additional arguments to |
... |
Arguments to |
This is essentially a wrapper for boot.ci from the boot
package, returning confidence intervals of the specified type and level
calculated from bootstrapped model effects. If a model or models is
supplied, bootstrapping will first be performed via bootEff. Effects
for which the confidence intervals do not contain zero are highlighted with
an asterix.
Nonparametric bias-corrected and accelerated confidence intervals
(BCa, Efron 1987) are calculated by default, which should provide
the most accurate coverage across a range of bootstrap sampling
distributions (Puth et al. 2015). They will, however, be
inappropriate
for parametric resampling - in which case the default will be set to the
bootstrap percentile method instead ("perc").
A data frame of the effects and bootstrapped confidence intervals, or a list or nested list of same.
All bootstrapped confidence intervals will tend to underestimate the true nominal coverage to some extent when sample size is small (Chernick & Labudde 2009), so the appropriate caution should be exercised in interpretation in such cases. Comparison of different interval types may be informative. For example, normal-theory based intervals may outperform bootstrap percentile methods when n < 34 (Hesterberg 2015). Ultimately however, the bootstrap is not a solution to small sample size.
Chernick, M. R., & Labudde, R. A. (2009). Revisiting Qualms about Bootstrap Confidence Intervals. American Journal of Mathematical and Management Sciences, 29(3–4), 437–456. https://doi.org/c8zv
Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171–185. https://doi.org/gfww2z
Hesterberg, T. C. (2015). What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum. The American Statistician, 69(4), 371–386. https://doi.org/gd85v5
Puth, M.-T., Neuhäuser, M., & Ruxton, G. D. (2015). On the variety of methods for calculating confidence intervals by bootstrapping. Journal of Animal Ecology, 84(4), 892–897. https://doi.org/f8n9rq
# CIs from bootstrapped SEM (Shipley.SEM.CI <- bootCI(Shipley.SEM.Boot)) # From original SEM (models) # (not typically recommended - better to use saved boot objects) # system.time( # Shipley.SEM.CI <- bootCI(Shipley.SEM, ran.eff = "site", seed = 53908) # )
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