# impute

##### Impute using a previously fitted model.

Impute one or more variables using a single R object representing a previously fitted model.

##### Usage

`impute(dat, formula, predictor = foretell, ...)`impute_(dat, variables, model, predictor = foretell, ...)

##### Arguments

- dat
`[data.frame]`

The data to be imputed.- formula
`[formula]`

object of the form`<imputed variables> ~ <model object>`

- predictor
`[function]`

with signature`object, newdata, ...`

that returns predicted values given a model`object`

and a new dataset`newdata`

. By default`foretell`

is used.- ...
- Extra arguments passed to
`predictor`

- variables
`[character]`

Names of columns in`dat`

to impute.- model
- A model object.

##### Details

`impute_`

is an explicit version of `impute`

that works better in
programming contexts, especially in cases involving nonstandard evaluation.

##### Model specification

Formulas are of the form

`IMPUTED_VARIABLES ~ MODEL_OBJECT `

The left-hand-side of the formula object lists the variable or variables to
be imputed. The right-hand-side must be a model object for which an `S3`

`predict`

method is implemented. Alternatively, one can specify a custom
predicting function. This function must accept at least a model and a
dataset, and return one predicted value for each row in the dataset.

`foretell`

implements usefull `predict`

methods for cases
where by default the predicted output is not of the same type as the predicted
variable (e.g. when using certain link functions in `glm`

)

##### See Also

Other imputation: `impute_cart`

,
`impute_hotdeck`

, `impute_lm`

##### Examples

```
irisNA <- iris
iris[1:3,1] <- NA
my_model <- lm(Sepal.Length ~ Sepal.Width + Species, data=iris)
impute(irisNA, Sepal.Length ~ my_model)
```

*Documentation reproduced from package simputation, version 0.2.2, License: GPL-3*