midastouch_donors

For each missing value, finds k nearest donors using a combined score:
closeness in predicted value (score) AND closeness in covariate space
(Mahalanobis distance). Donors closer in covariate space are upweighted.

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

midastouch_donors function

<dl><dt>y_obs</dt>
<dd>Observed values of the target variable</dd>
<dt>X_obs</dt>
<dd>Predictor matrix for observed rows (n_obs x p)</dd>
<dt>X_miss</dt>
<dd>Predictor matrix for missing rows (n_miss x p)</dd>
<dt>score_obs</dt>
<dd>Model predictions for observed rows</dd>
<dt>score_miss</dt>
<dd>Model predictions for missing rows</dd>
<dt>k</dt>
<dd>Number of candidate donors (default 5)</dd></dl>

Arguments

Midastouch: PMM with covariate-distance-weighted donor selection — midastouch_donors

<dl>

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


<dt>X_obs</dt>
<dd>Predictor matrix for observed rows (n_obs x p)</dd>


<dt>X_miss</dt>
<dd>Predictor matrix for missing rows (n_miss x p)</dd>


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


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


<dt>k</dt>
<dd>Number of candidate donors (default 5)</dd>

</dl>

Midastouch: PMM with covariate-distance-weighted donor selection

midastouch_donors: Midastouch: PMM with covariate-distance-weighted donor selection

Description

Usage

Value

Arguments

Details