MICE PMM imputation.

Multiple Imputation by Chained Equations.

Usage

impute_mice_pmm(missdf, ...)

impute_mice_mixed(missdf)

Arguments

missdf: a data frame with missing values to be imputed containing features in columns and samples in rows.
...: other parameters of mice::mice() besides method and data.

Value

A data.frame with imputed values by pmm used mice::mice().

Details

A function to replace NA in the data frame by predictive mean matching (pmm) used mice::mice().

Functions

impute_mice_mixed(): An alias from the missCompare package.

Silent defaults

If printFlag is not defined in the function call, it is set to FALSE.

If predictorMatrix is not defined in the function call, it is set to mice::quickpred.

Aliases

impute_mice_mixed is a wrapper of missCompare::impute_data() with the method set to 11 (which means that mice is automatically selecting predictive mean matching for numerical data). The amount of iterations n.iter is changed to 1 from default 10.

References

van Buuren S, Groothuis-Oudshoorn K (2011). “Mice: Multivariate Imputation by Chained Equations in R.” Journal of Statistical Software, 45, 1--67. ISSN 1548-7660, doi:10.18637/jss.v045.i03 .

Examples

data(sim_miss)
impute_mice_pmm(sim_miss)
#>             X1         X2        X3           X4          X5         X6
#> 1  0.155050833 0.88115677 0.5513393 0.1194841210 0.600405471 0.83668180
#> 2  0.968378809 0.60748407 0.6337790 0.6181194666 0.008132938 0.75207300
#> 3  0.468263086 0.57452956 0.2476089 0.5060450307 0.370125689 0.83668180
#> 4  0.776819652 0.80313329 0.5513393 0.1459775318 0.724248714 0.84777606
#> 5  0.407885741 0.79911692 0.2347611 0.1897135850 0.418270750 0.70873497
#> 6  0.538797149 0.80186265 0.2586147 0.5754945197 0.238248518 0.98576580
#> 7  0.830082966 0.75207300 0.9528881 0.0001881735 0.887881226 0.48833525
#> 8  0.187103555 0.57546133 0.8568746 0.3283282877 0.577775384 0.34722847
#> 9  0.779969688 0.94421829 0.2327532 0.0782780354 0.739950257 0.20304801
#> 10 0.193943927 0.21898736 0.5545242 0.6041721753 0.160679622 0.73995026
#> 11 0.434231178 0.47799791 0.8769438 0.3267990556 0.529571799 0.89369752
#> 12 0.002274518 0.04466879 0.8018626 0.7904525734 0.574529562 0.57452956
#> 13 0.834692139 0.16848571 0.5467373 0.8300829662 0.960086967 0.08444870
#> 14 0.948497073 0.36828087 0.2327532 0.8100510929 0.398616886 0.14825833
#> 15 0.956967818 0.56331137 0.6022678 0.2027530000 0.408468068 0.37028128
#> 16 0.948497073 0.57452956 0.7125391 0.5060450307 0.613321482 0.86117762
#> 17 0.600729976 0.73432757 0.6489036 0.5206679751 0.706590850 0.83668180
#> 18 0.261807405 0.70365677 0.1875624 0.0842680801 0.799116918 0.74054705
#> 19 0.643034251 0.73682406 0.2383929 0.2687653562 0.161331222 0.98576580
#> 20 0.526233507 0.36828087 0.6337790 0.8147157354 0.885932416 0.03208182