R: Built-in imputation methods.

imputations {mlr} | R Documentation

## Built-in imputation methods.

### Description

The built-ins are:

- `imputeConstant(const)` for imputation using a constant value,

- `imputeMedian()` for imputation using the median,

- `imputeMode()` for imputation using the mode,

- `imputeMin(multiplier)` for imputing constant values shifted below the minimum using `min(x) - multiplier * diff(range(x))`,

- `imputeMax(multiplier)` for imputing constant values shifted above the maximum using `max(x) + multiplier * diff(range(x))`,

- `imputeNormal(mean, sd)` for imputation using normally distributed random values. Mean and standard deviation will be calculated from the data if not provided.

- `imputeHist(breaks, use.mids)` for imputation using random values with probabilities calculated using `table` or `hist`.

- `imputeLearner(learner, features = NULL)` for imputations using the response of a classification or regression learner.

### Usage

    imputeConstant(const)

    imputeMedian()

    imputeMean()

    imputeMode()

    imputeMin(multiplier = 1)

    imputeMax(multiplier = 1)

    imputeUniform(min = NA_real_, max = NA_real_)

    imputeNormal(mu = NA_real_, sd = NA_real_)

    imputeHist(breaks, use.mids = TRUE)

    imputeLearner(learner, features = NULL)

### Arguments

`const` | (any) Constant valued use for imputation.
`multiplier` | (`numeric(1)`) Value that stored minimum or maximum is multiplied with when imputation is done.
`min` | (`numeric(1)`) Lower bound for uniform distribution. If NA (default), it will be estimated from the data.
`max` | (`numeric(1)`) Upper bound for uniform distribution. If NA (default), it will be estimated from the data.
`mu` | (`numeric(1)`) Mean of normal distribution. If missing it will be estimated from the data.
`sd` | (`numeric(1)`) Standard deviation of normal distribution. If missing it will be estimated from the data.
`breaks` | (`numeric(1)`) Number of breaks to use in graphics::hist. If missing, defaults to auto-detection via “Sturges”.
`use.mids` | (`logical(1)`) If `x` is numeric and a histogram is used, impute with bin mids (default) or instead draw uniformly distributed samples within bin range.
`learner` | (Learner | `character(1)`) Supervised learner. Its predictions will be used for imputations. If you pass a string the learner will be created via makeLearner. Note that the target column is not available for this operation.
`features` | (character) Features to use in `learner` for prediction. Default is `NULL` which uses all available features except the target column of the original task.

### See Also

Other impute: `impute`, `makeImputeMethod`, `makeImputeWrapper`, `reimpute`

[Package mlr version 2.16.0 Index]