pool {rbmi} | R Documentation |
Pool analysis results obtained from the imputed datasets
Description
Pool analysis results obtained from the imputed datasets
Usage
pool(
results,
conf.level = 0.95,
alternative = c("two.sided", "less", "greater"),
type = c("percentile", "normal")
)
## S3 method for class 'pool'
as.data.frame(x, ...)
## S3 method for class 'pool'
print(x, ...)
Arguments
results |
an analysis object created by |
conf.level |
confidence level of the returned confidence interval. Must be a single number between 0 and 1. Default is 0.95. |
alternative |
a character string specifying the alternative hypothesis,
must be one of |
type |
a character string of either |
x |
a |
... |
not used. |
Details
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 beNA
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).
References
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.