View source: R/apply_imputation.R

apply_imputation | R Documentation |

Apply a function for imputation over rows, columns or combinations of both

apply_imputation( ds, FUN = mean, type = "columnwise", convert_tibble = TRUE, ... )

`ds` |
A data frame or matrix with missing values. |

`FUN` |
The function to be applied for imputation. |

`type` |
A string specifying the values used for imputation; one of: "columnwise", "rowwise", "total", "Two-Way" or "Winer" (see details). |

`convert_tibble` |
If |

`...` |
Further arguments passed to |

The functionality of `apply_imputation`

is inspired by the
`apply`

function. The function applies a function
`FUN`

to impute the missing values in `ds`

. `FUN`

must be a
function, which takes a vector as input and returns exactly one value. The
argument `type`

is comparable to `apply`

's
`MARGIN`

argument. It specifies the values that are used for the
calculation of the imputation values. For example, `type = "columnwise"`

and `FUN = mean`

will impute the mean of the observed values in a column
for all missing values in this column. In contrast, `type = "rowwise"`

and `FUN = mean`

will impute the mean of the observed values in a row
for all missing values in this row.

List of all implemented `types`

:

"columnwise" (the default): imputes column by column; all observed values of a column are given to

`FUN`

and the returned value is used as the imputation value for all missing values of the column."rowwise": imputes row by row; all observed values of a row are given to

`FUN`

and the returned value is used as the imputation value for all missing values of the row."total": All observed values of

`ds`

are given to`FUN`

and the returned value is used as the imputation value for all missing values of`ds`

."Winer": The mean value from "columnwise" and "rowwise" is used as the imputation value.

"Two-Way": The sum of the values from "columnwise" and "rowwise" minus "total" is used as the imputation value.

If no value can be given to `FUN`

(for example, if no value in a column
is observed and `type = "columnwise"`

), then a warning will be issued
and no value will be imputed in the corresponding column or row.

An object of the same class as `ds`

with imputed missing values.

If you use tibbles and `convert_tibble`

is `TRUE`

the tibble is
first converted to a data frame, then imputed and converted back. If
`convert_tibble`

is `FALSE`

no conversion is done. However,
depending on the tibble and the package version of tibble you use,
imputation may not be possible and some errors will be thrown.

Beland, S., Pichette, F., & Jolani, S. (2016). Impact on
Cronbach's *alpha* of simple treatment methods for missing
data. *The Quantitative Methods for Psychology*, 12(1), 57-73.

A convenient interface exists for common cases like mean imputation:
`impute_mean`

, `impute_median`

,
`impute_mode`

. All these functions
call `apply_imputation`

.

ds <- data.frame(X = 1:20, Y = 101:120) ds_mis <- delete_MCAR(ds, 0.2) ds_imp_app <- apply_imputation(ds_mis, FUN = mean, type = "total") # the same result can be achieved via impute_mean(): ds_imp_mean <- impute_mean(ds_mis, type = "total") all.equal(ds_imp_app, ds_imp_mean)

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