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, ...)
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.
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).
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]
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]
Von Hippel, Paul T and Bartlett, Jonathan W. Maximum likelihood multiple imputation: Faster imputations and consistent standard errors without posterior draws. 2021.