Confidence intervals for models fit using marginal standardization based on parametric bootstrapping.
# S3 method for margstd_boot
confint(
object,
parm = NULL,
level = 0.95,
bootrepeats = 1000,
bootci = c("bca", "normal", "nonpar"),
jacksd = FALSE,
...
)Matrix: First column, lower bound; second column, upper bound.
Model fitted through marginal standardization
Not used, for compatibility
Confidence level, defaults to 0.95.
Bootstrap repeats. Defaults to 1000. Consider increasing.
Type of bootstrap confidence interval:
"bca" Default. Parametric BCa (bias-corrected accelerated)
confidence intervals.
"normal" Parametric normality-based confidence intervals,
which require lower repeat numbers but are less accurate and
may result in invalid results for ratios.
"nonpar" Non-parametric BCa confidence intervals,
which should be used with caution because of the risk
of sparse-data bias with non-parametric bootstrapping.
Return jackknife Monte-Carlo error for the confidence limits?
Only functional with BCa confidence intervals. Defaults to FALSE.
Not used