evaluation

nrmse

msecov

msecor

Various error measures evaluating the quality of imputations

manip

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

evaluation function

<dl><dt>x</dt>
<dd>matrix or data frame</dd>
<dt>y</dt>
<dd>matrix or data frame of the same size as x</dd>
<dt>m</dt>
<dd>the indicator matrix for missing cells (kept for backward
compatibility; <code>where</code> is the documented name)</dd>
<dt>vartypes</dt>
<dd>a vector of length ncol(x) specifying the variable types
(<code>"numeric"</code> or <code>"factor"</code>). The default <code>"guess"</code> infers the
types from the columns of <code>x</code> (numeric columns become <code>"numeric"</code>,
everything else <code>"factor"</code>).</dd>
<dt>where</dt>
<dd>the indicator matrix for missing cells under its documented
name -- the amputed-cell mask as returned in <code>makeMissing()</code>'s
<code>"where"</code> attribute. Supply either <code>m</code> or <code>where</code>, not
both.</dd></dl>

Arguments

Author

Error performance measures — evaluation

<dl>

<dt>x</dt>
<dd>matrix or data frame</dd>


<dt>y</dt>
<dd>matrix or data frame of the same size as x</dd>


<dt>m</dt>
<dd>the indicator matrix for missing cells (kept for backward
compatibility; <code>where</code> is the documented name)</dd>


<dt>vartypes</dt>
<dd>a vector of length ncol(x) specifying the variable types
(<code>"numeric"</code> or <code>"factor"</code>). The default <code>"guess"</code> infers the
types from the columns of <code>x</code> (numeric columns become <code>"numeric"</code>,
everything else <code>"factor"</code>).</dd>


<dt>where</dt>
<dd>the indicator matrix for missing cells under its documented
name -- the amputed-cell mask as returned in <code>makeMissing()</code>'s
<code>"where"</code> attribute. Supply either <code>m</code> or <code>where</code>, not
both.</dd>

</dl>

Error performance measures

evaluation: Error performance measures

Description

Usage

Value

Arguments

Author

Details

References

Examples