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Multiply impute missing data

Source: R/imputation-mi.R
multipleImputation.Rd

Wraps mice::mice for missing-at-random multiple imputation (the default), or delegates to rbmi for reference-based (jump-to-reference) imputation. Imputations are reproducible under a fixed seed.

Usage

multipleImputation(
  data,
  method = "pmm",
  m = 5,
  predictors = NULL,
  seed = NULL,
  reference_based = FALSE,
  ...
)

Arguments

data

A data frame with missing values (NA).

method

Imputation method passed to mice (default "pmm"); recycled across incomplete columns.

m

Number of imputations (default 5).

predictors

Optional: a character vector of variables to use as predictors for every imputed column, or a full mice predictorMatrix. NULL uses the mice default.

seed

Optional integer seed for reproducibility.

reference_based

Logical; if TRUE, perform reference-based (jump-to-reference) imputation via rbmi (which must be installed).

...

Further arguments passed to mice::mice.

Value

A mice "mids" object (MAR path) with m and method recorded.

References

van Buuren & Groothuis-Oudshoorn 2011 (mice); Carpenter et al. 2013 (reference-based MI); rbmi.

See also

poolEstimates(), analyseEstimand()

Examples

# \donttest{
if (requireNamespace("mice", quietly = TRUE)) {
  imp <- multipleImputation(mice::nhanes, m = 5, seed = 1)
}
# }