Hmisc areg imputation.

Multiple Imputation using Predictive Mean Matching.

Usage

impute_areg(missdf, verbose = FALSE, ...)

Arguments

missdf: a data frame with missing values to be imputed containing features in columns and samples in rows.
verbose: boolean, if TRUE, prints the typical prompts of Hmisc::aregImpute().
...: other parameters of Hmisc::aregImpute() besides formula, formula, data and type.

Value

A data.frame with imputed values by Hmisc::aregImpute().

Details

A function to replace NA in the data frame by Hmisc::aregImpute().

aregImpute() allows users to customize the number of imputed datasets to create. As one of the aims of the imputomics is to standardize the input and the output, the n.impute is being set to 1.

Silent defaults

burnin = 5 and nk = 0.

References

Jr FEH, functions s, maintains latex functions) CD( (2023). “Hmisc: Harrell Miscellaneous.”

Examples

data(sim_miss)
impute_areg(sim_miss)
#>             X1         X2        X3           X4          X5         X6
#> 1  0.155050833 0.88115677 0.2327532 0.1194841210 0.600405471 0.75207300
#> 2  0.968378809 0.60748407 0.6337790 0.6181194666 0.008132938 0.75207300
#> 3  0.468263086 0.57452956 0.2476089 0.1194841210 0.370125689 0.75207300
#> 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 0.75207300
#> 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 0.6337790 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 0.155050833 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 0.60748407 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 0.5467373 0.8147157354 0.885932416 0.03208182