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Pool estimates across imputations (Rubin's rules)

Source: R/imputation-pool.R
poolEstimates.Rd

Combines the coefficient estimates from a list of models fitted to multiply imputed data sets using Rubin's rules with the Barnard-Rubin degrees-of- freedom adjustment. The pooled estimate, total variance, df, confidence interval, and fraction of missing information reproduce mice::pool.

Usage

poolEstimates(fits, conf_level = 0.95)

Arguments

fits

A list of fitted models (each with coef() and vcov()), or a list of tidy data frames with columns term, estimate, and std.error (and optionally df.residual).

conf_level

Confidence level for the pooled interval (default 0.95).

Value

An AnalysisResult (type "pooled_estimates") whose result$estimates is a data frame of pooled estimate, std.error, df, statistic, p.value, conf.low, conf.high, and fmi per term.

References

Rubin 1987; Barnard & Rubin 1999. mice::pool.

See also

multipleImputation(), analyseEstimand()

Examples

set.seed(1)
fits <- lapply(1:5, function(i) lm(mpg ~ hp + wt,
  data = mtcars[sample(nrow(mtcars), replace = TRUE), ]))
poolEstimates(fits)
#> <AnalysisResult> pooled_estimates 
#>   estimate: 37.90362826, -0.02973744, -4.27704284 
#>   method: rubin_pool 
#>   fields: estimates