Pool analysis results obtained from the imputed datasets
pool(
results,
conf.level = 0.95,
alternative = c("two.sided", "less", "greater"),
type = c("percentile", "normal")
)# S3 method for pool
as.data.frame(x, ...)
# S3 method for pool
print(x, ...)
mcse(x, results)
# S3 method for mcse
as.data.frame(x, ...)
# S3 method for mcse
print(x, ..., pval_digits = 2, pval_eps = 1e-06, pval_nsmall = 5)
an analysis object created by analyse().
confidence level of the returned confidence interval. Must be a single number between 0 and 1. Default is 0.95.
a character string specifying the alternative hypothesis,
must be one of "two.sided" (default), "greater" or "less".
a character string of either "percentile" (default) or
"normal". Determines what method should be used to calculate the bootstrap confidence
intervals. See details.
Only used if method_condmean(type = "bootstrap") was specified
in the original call to draws().
a pool object generated by pool().
not used.
number of significant digits to print for p-values' MCSE.
the minimum p-values' MCSE to print.
the minimum number of digits to print for p-values' MCSE.
The calculation used to generate the point estimate, standard errors and
confidence interval depends upon the method specified in the original
call to draws(); In particular:
method_approxbayes() & method_bayes() both use Rubin's rules to pool estimates
and variances across multiple imputed datasets, and the Barnard-Rubin rule to pool
degree's of freedom; see Little & Rubin (2002).
Here, the mcse() function can compute the Monte Carlo standard error (MCSE) of the
pooled estimates, via a Jackknife variance estimator for all parameters; see
Efron & Gong (1983) and Royston, Carlin & White (2009).
method_condmean(type = "bootstrap") uses percentile or normal approximation;
see Efron & Tibshirani (1994). Note that for the percentile bootstrap, no standard error is
calculated, i.e. the standard errors will be NA in the object / data.frame.
method_condmean(type = "jackknife") uses the standard jackknife variance formula;
see Efron & Tibshirani (1994).
method_bmlmi uses pooling procedure for Bootstrapped Maximum Likelihood MI (BMLMI).
See Von Hippel & Bartlett (2021).
Bradley Efron and Robert J Tibshirani. An introduction to the bootstrap. CRC press, 1994. [Section 11]
Bradley Efron and Gail Gong. A leisurely look at the bootstrap, the jackknife, and cross-validation. The American Statistician, 37(1):36-48, 1983.
Roderick J. A. Little and Donald B. Rubin. Statistical Analysis with Missing Data, Second Edition. John Wiley & Sons, Hoboken, New Jersey, 2002. [Section 5.4]
Royston, P., Carlin, J. B., & White, I. R. Multiple imputation of missing values: New features for mim. Stata Journal, 9(2): 252-264, 2009.
Von Hippel, Paul T and Bartlett, Jonathan W. Maximum likelihood multiple imputation: Faster imputations and consistent standard errors without posterior draws. 2021.