# impute_hotdeck

##### Hot deck imputation

Hot-deck imputation methods include random and sequential hot deck, k-nearest neighbours imputation and predictive mean matching.

##### Usage

```
impute_rhd(dat, formula, pool = c("complete", "univariate", "multivariate"),
prob, backend = getOption("simputation.hdbackend", default =
c("simputation", "VIM")), ...)
```impute_shd(dat, formula, pool = c("complete", "univariate", "multivariate"),
order = c("locf", "nocb"), backend = getOption("simputation.hdbackend",
default = c("simputation", "VIM")), ...)

impute_pmm(dat, formula, predictor = impute_lm, pool = c("complete",
"univariate", "multivariate"), ...)

impute_knn(dat, formula, pool = c("complete", "univariate", "multivariate"),
k = 5, backend = getOption("simputation.hdbackend", default =
c("simputation", "VIM")), ...)

##### Arguments

- dat
`[data.frame]`

, with variables to be imputed and their predictors.- formula
`[formula]`

imputation model description (see Details below).- pool
`[character]`

Specify donor pool when`backend="simputation"`

`"complete"`

. Only records for which the variables on the left-hand-side of the model formula are complete are used as donors. If a record has multiple missings, all imputations are taken from a single donor.`"univariate"`

. Imputed variables are treated one by one and independently so the order of variable imputation is unimportant. If a record has multiple missings, separate donors are drawn for each missing value.`"multivariate"`

. A donor pool is created for each missing data pattern. If a record has multiple missings, all imputations are taken from a single donor.

- prob
`[numeric]`

Sampling probability weights (passed through to`sample`

). Must be of length`nrow(dat)`

.- backend
`[character]`

Choose the backend for imputation.- ...
- further arguments passed to
`VIM::hotdeck`

if`VIM`

is chosen as backend, otherwise they are passed to`order`

for`impute_shd`

and`backend="simputation"`

`VIM::hotdeck`

for`impute_shd`

and`impute_rhd`

when`backend="VIM"`

.`VIM:kNN`

for`impute_knn`

when`backend="VIM"`

- The
`predictor`

function for`impute_pmm`

.

- order
`[character]`

Last Observation Carried Forward or Next Observarion Carried Backward. Only for`backend="simputation"`

- predictor
`[function]`

Imputation to use for predictive part in predictive mean matching. Any of the`impute_`

functions of this package (it makes no sense to use a hot-deck imputation).- k
`[numeric]`

Number of nearest neighbours to draw the donor from.

##### Model specification

Formulas are of the form

`IMPUTED_VARIABLES ~ MODEL_SPECIFICATION [ | GROUPING_VARIABLES ] `

The left-hand-side of the formula object lists the variable or variables to be imputed. The interpretation of the independent variables on the right-hand-side depends on the imputation method.

`impute_rhd`

Variables in`MODEL_SPECIFICATION`

and/or`GROUPING_VARIABLES`

are used to split the data set into groups prior to imputation. Use`~ 1`

to specify that no grouping is to be applied.`impute_shd`

Variables in`MODEL_SPECIFICATION`

are used to sort the data. When multiple variables are specified, each variable after the first serves as tie-breaker for the previous one.`impute_knn`

The predictors are used to determine Gower's distance between records (see`gower_topn`

). This may include the variables to be imputed..`impute_pmm`

Predictive mean matching. The`MODEL_SPECIFICATION`

is passed through to the`predictor`

function.

If grouping variables are specified, the data set is split according to the values of those variables, and model estimation and imputation occur independently for each group.

Grouping using `dplyr::group_by`

is also supported. If groups are
defined in both the formula and using `dplyr::group_by`

, the data is
grouped by the union of grouping variables. Any missing value in one of the
grouping variables results in an error.

##### Methodology

**Random hot deck imputation** with `impute_rhd`

can be applied to
numeric, categorical or mixed data. A missing value is copied from a sampled
record. Optionally samples are taken within a group, or with non-uniform
sampling probabilities. See Andridge and Little (2010) for an overview
of hot deck imputation methods.

**Sequential hot deck imputation** with `impute_rhd`

can be applied
to numeric, categorical, or mixed data. The dataset is sorted using the
`predictor variables'. Missing values or combinations thereof are copied
from the previous record where the value(s) are available in the case
of LOCF and from the next record in the case of NOCF.

**Predictive mean matching** with `impute_pmm`

can be applied to
numeric data. Missing values or combinations thereof are first imputed using
a predictive model. Next, these predictions are replaced with observed
(combinations of) values nearest to the prediction. The nearest value is the
observed value with the smallest absolute deviation from the prediction.

**K-nearest neighbour imputation** with `impute_knn`

can be applied
to numeric, categorical, or mixed data. For each record containing missing
values, the \(k\) most similar completed records are determined based on
Gower's (1977) similarity coefficient. From these records the actual donor is
sampled.

##### Using the VIM backend

The https://CRAN.R-project.org/package=VIM package has efficient implementations of several popular imputation methods. In particular, its random and sequential hotdeck implementation is faster and more memory-efficient than that of the current package. Moreover, VIM offers more fine-grained control over the imputation process then simputation.

If you have this package installed, it can be used by setting
`backend="VIM"`

for functions supporting this option. Alternatively, one
can set `options(simputation.hdbackend="VIM")`

so it becomes the
default.

Simputation will map the simputation call to a function in the VIM package. In particular:

`impute_rhd`

is mapped to`VIM::hotdeck`

where imputed variables are passed to the`variable`

argument and the union of predictor and grouping variables are passed to`domain_var`

. Extra arguments in`...`

are passed to`VIM::hotdeck`

as well. Argument`pool`

is ignored.`impute_shd`

is mapped to`VIM::hotdeck`

where imputed variables are passed to the`variable`

argument, predictor variables to`ord_var`

and grouping variables to`domain_var`

. Extra arguments in`...`

are passed to`VIM::hotdeck`

as well. Arguments`pool`

and`order`

are ignored. In`VIM`

the donor pool is determined on a per-variable basis, equivalent to setting`pool="univariate"`

with the simputation backend. VIM is LOCF-based. Differences between simputation and`VIM`

likely occurr when the sorting variables contain missings.`impute_knn`

is mapped to`VIM::kNN`

where imputed variables are passed to`variable`

, predictor variables are passed to`dist_var`

and grouping variables are ignored with a message. Extra arguments in`...`

are passed to`VIM::kNN`

as well. Argument`pool`

is ignored. Note that simputation adheres stricktly to the Gower's original definition of the distance measure, while VIM uses a generalized variant that can take ordered factors into account.

By default, VIM's imputation functions add indicator variables to the
original data to trace what values have been imputed. This is switched off by
default for consistency with the rest of the simputation package, but it may
be turned on again by setting `imp_var=TRUE`

.

##### References

Andridge, R.R. and Little, R.J., 2010. A review of hot deck imputation for survey non-response. International statistical review, 78(1), pp.40-64.

Gower, J.C., 1971. A general coefficient of similarity and some of its properties. Biometrics, pp.857--871.

##### See Also

Other imputation: `impute_cart`

,
`impute_lm`

, `impute`

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