median_impute

Perform median imputation. In case of discrete rating-scale data, a 
discretization step can be carried out afterwards to make sure that the 
imputed values are mapped to the rating scale of the observed values (as 
the median of a given column may lie in between two answer categories in 
case of an even number of observed values). This is done by randomly 
sampling from the largest answer category smaller than the median and the 
smallest answer category larger than the median (for each missing cell).

multivariate

Collection of methods for rating matrix completion, which is a statistical framework for recommender systems. Another relevant application is the imputation of rating-scale survey data in the social and behavioral sciences. Note that matrix completion and imputation are synonymous terms used in different streams of the literature. The main functionality implements robust matrix completion for discrete rating-scale data with a low-rank constraint on a latent continuous matrix (Archimbaud, Alfons, and Wilms (2025) <doi:10.48550/arXiv.2412.20802>). In addition, the package provides wrapper functions for 'softImpute' (Mazumder, Hastie, and Tibshirani, 2010, <https://www.jmlr.org/papers/v11/mazumder10a.html>; Hastie, Mazumder, Lee, Zadeh, 2015, <https://www.jmlr.org/papers/v16/hastie15a.html>) for easy tuning of the regularization parameter, as well as benchmark methods such as median imputation and mode imputation.

Andreas Alfons

RMCLab

Lab for Matrix Completion and Imputation of Discrete Rating Data

Aurore Archimbaud

median_impute function

<dl><dt>X</dt>
<dd>a matrix or data frame with missing values.</dd>
<dt>discretize</dt>
<dd>a logical indicating whether to include a discretization 
step after median imputation (defaults to <code>TRUE</code>). In case of 
discrete rating-scale data, this can be used to ensure that the imputed 
values are mapped to the discrete rating scale of the observed values.</dd>
<dt>values</dt>
<dd>an optional numeric vector giving the possible values of 
discrete ratings. This is ignored if <code>discretize</code> is <code>FALSE</code>. 
Currently, the possible values are assumed to be the same for all columns. 
If <code>NULL</code>, the unique values of the observed parts of <code>X</code> are 
used.</dd></dl>

Arguments

Author

Median imputation — median_impute

<dl>

<dt>X</dt>
<dd>a matrix or data frame with missing values.</dd>


<dt>discretize</dt>
<dd>a logical indicating whether to include a discretization 
step after median imputation (defaults to <code>TRUE</code>). In case of 
discrete rating-scale data, this can be used to ensure that the imputed 
values are mapped to the discrete rating scale of the observed values.</dd>


<dt>values</dt>
<dd>an optional numeric vector giving the possible values of 
discrete ratings. This is ignored if <code>discretize</code> is <code>FALSE</code>. 
Currently, the possible values are assumed to be the same for all columns. 
If <code>NULL</code>, the unique values of the observed parts of <code>X</code> are 
used.</dd>

</dl>

median_impute: Median imputation

Description

Usage

Value

Arguments

Author

See Also

Examples