rangerImpute

Impute missing values based on random-forest models via <code>vimpute()</code>.

Provides methods for imputation and visualization of
missing values. It includes graphical tools to explore the amount, structure
and patterns of missing and/or imputed values, supporting exploratory
data analysis and helping to investigate potential missingness mechanisms
(details in Alfons, Templ and Filzmoser, <doi:10.1007/s11634-011-0102-y>).
The quality of imputations can be assessed visually using a wide range of
univariate, bivariate and multivariate plots.
The package further provides several imputation methods,
including efficient implementations of k-nearest neighbour and hot-deck
imputation (Kowarik and Templ 2013, <doi:10.18637/jss.v074.i07>),
iterative robust model-based multiple
imputation (Templ 2011, <doi:10.1016/j.csda.2011.04.012>;
Templ 2023, <doi:10.3390/math11122729>), and machine learning–based
approaches such as robust GAM-based multiple imputation
(Templ 2024, <doi:10.1007/s11222-024-10429-1>) as well as random forest
and gradient boosting (XGBoost) imputation
(Niederhametner et al., <doi:10.1177/18747655251339401>).
General background and practical guidance on imputation are provided in the
Springer book by
Templ (2023) <doi:10.1007/978-3-031-30073-8>.

Matthias Templ

Visualization and Imputation of Missing Values

Alexander Kowarik

Andreas Alfons

Johannes Gussenbauer

Nina Niederhametner

Eileen Vattheuer

Gregor de Cillia

Bernd Prantner

Wolfgang Rannetbauer

rangerImpute function

<dl><dt>formula</dt>
<dd>model formula for the imputation</dd>
<dt>data</dt>
<dd>A <code>data.frame</code> containing the data</dd>
<dt>imp_var</dt>
<dd><code>TRUE</code>/<code>FALSE</code> if a <code>TRUE</code>/<code>FALSE</code> variables for each imputed
variable should be created show the imputation status</dd>
<dt>imp_suffix</dt>
<dd>suffix used for TF imputation variables</dd>
<dt>...</dt>
<dd>Additional ranger hyperparameters (e.g. <code>num.trees</code>, <code>mtry</code>,
<code>min.node.size</code>), forwarded to the ranger learner via <code>vimpute()</code>.</dd>
<dt>verbose</dt>
<dd>Show the number of observations used for training
and evaluating the RF-Model.</dd>
<dt>median</dt>
<dd><code>TRUE</code>/<code>FALSE</code>. If <code>TRUE</code>, ranger regression predictions are
aggregated tree-wise using the median (via <code>vimpute()</code>).</dd></dl>

Arguments

Random Forest Imputation — rangerImpute

<dl>

<dt>formula</dt>
<dd>model formula for the imputation</dd>


<dt>data</dt>
<dd>A <code>data.frame</code> containing the data</dd>


<dt>imp_var</dt>
<dd><code>TRUE</code>/<code>FALSE</code> if a <code>TRUE</code>/<code>FALSE</code> variables for each imputed
variable should be created show the imputation status</dd>


<dt>imp_suffix</dt>
<dd>suffix used for TF imputation variables</dd>


<dt>...</dt>
<dd>Additional ranger hyperparameters (e.g. <code>num.trees</code>, <code>mtry</code>,
<code>min.node.size</code>), forwarded to the ranger learner via <code>vimpute()</code>.</dd>


<dt>verbose</dt>
<dd>Show the number of observations used for training
and evaluating the RF-Model.</dd>


<dt>median</dt>
<dd><code>TRUE</code>/<code>FALSE</code>. If <code>TRUE</code>, ranger regression predictions are
aggregated tree-wise using the median (via <code>vimpute()</code>).</dd>

</dl>

rangerImpute: Random Forest Imputation

Description

Usage

Value

Arguments

See Also

Examples