pmm_donor_selection

For each missing value, finds k nearest observed values based on model
score (predicted value) distance, then aggregates using the chosen method.

internal

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

pmm_donor_selection function

<dl><dt>y_obs</dt>
<dd>Observed values of target variable</dd>
<dt>score_obs</dt>
<dd>Model scores for observed rows</dd>
<dt>score_miss</dt>
<dd>Model scores for missing rows</dd>
<dt>k</dt>
<dd>Number of nearest donors</dd>
<dt>agg_method</dt>
<dd>One of "mean", "median", "random", or a function</dd></dl>

Arguments

Score-based PMM donor selection — pmm_donor_selection

<dl>

<dt>y_obs</dt>
<dd>Observed values of target variable</dd>


<dt>score_obs</dt>
<dd>Model scores for observed rows</dd>


<dt>score_miss</dt>
<dd>Model scores for missing rows</dd>


<dt>k</dt>
<dd>Number of nearest donors</dd>


<dt>agg_method</dt>
<dd>One of "mean", "median", "random", or a function</dd>

</dl>

Score-based PMM donor selection

pmm_donor_selection: Score-based PMM donor selection

Description

Usage

Value

Arguments