pool {rbmi}

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 analyse().

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 "two.sided" (default), "greater" or "less".

type

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().

x

a pool object generated by pool().

...

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 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).

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.