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Impute missing values via Multiple Imputation (mice)

Source: R/impute_missing.R
impute_mice.Rd

Wraps mice to impute only numeric columns; non-numeric columns are untouched. Requires the mice package to be installed (Suggests). If no numeric columns contain NAs, data is returned unchanged.

Usage

impute_mice(data, m = 5, cols = NULL, verbose = FALSE, ...)

Arguments

data

A data.frame or tibble containing missing values.

m

Integer; number of imputations to run (passed to mice). Default 5.

cols

Optional character vector of numeric columns to impute. Defaults to all numeric columns with at least one NA.

verbose

Logical; if TRUE, prints progress and a completion summary. Default FALSE.

...

Additional arguments passed to mice::mice().

Value

A data.frame/tibble with numeric columns imputed by mice.

Details

Notes:

  • At least two numeric columns are typically needed by mice to borrow strength.

  • This function runs m imputations and returns the first completed dataset.

  • Messages from mice are suppressed; use verbose = TRUE here for high-level progress.

References

Rubin DB (1987). Multiple Imputation for Nonresponse in Surveys. Wiley. doi:10.1002/9780470316696 . ; van Buuren S, Groothuis-Oudshoorn K (2011). “mice: Multivariate Imputation by Chained Equations in R.” Journal of Statistical Software, 45(3), 1–67. doi:10.18637/jss.v045.i03 .

Examples

# \donttest{
if (requireNamespace("mice", quietly = TRUE)) {
  df <- tibble::tibble(a = c(1, NA, 3), b = c(2, 4, NA), c = 5)
  impute_mice(df, m = 2, verbose = TRUE)
}
#> impute_mice(): preparing inputs (3 rows, 2 columns)
#> impute_mice(): results: imputed 2 values across 2 columns.
#> # A tibble: 3 × 3
#>       a     b     c
#>   <dbl> <dbl> <dbl>
#> 1     1     2     5
#> 2     3     4     5
#> 3     3     4     5
# }