Robust imputation

Multiple imputation using classical and robust methods accounting for model and imputation uncertainty.

imputeRobust(
  form,
  data,
  boot = TRUE,
  robustboot = "stratified",
  method = "MM",
  takeAll = TRUE,
  alpha = 0.75,
  uncert = "pmm",
  family = "Gaussian",
  value_back = "all"
)

Arguments

form

Model formulas as a list.

data

Data set to impute

boot

Accounting for model uncertainty with a classical bootstrap, Default: TRUE

robustboot

Accounting for model uncertainty with robust bootstrap methods, Default: 'stratified'

method

Imputation method, Default: 'MM'

takeAll

Missing values are intialized when TRUE, Default: TRUE

alpha

Relative size of good data points. Used for the robust bootstrap methods, Default: 0.75

uncert

Imputation uncertainty method, Default: 'pmm'

family

Not supported and ignored. Foreseen for future versions, Default: 'Gaussian'

value_back

Only observations with imputed values as return object (ymiss), or the whole data set, Default: 'all'

Value

Imputed data set.

Details

Complex formulas can be provided for each variable in your data set.

See also

initialise lmrob gam pdist

Other imputation methods: hotdeck(), impPCA(), imputeRobustChain(), irmi(), kNN(), matchImpute(), medianSamp(), rangerImpute(), regressionImp(), sampleCat(), vimpute(), xgboostImpute()

Examples

if (FALSE) { # \dontrun{
if(interactive()){
 #EXAMPLE1
 }
} # }