prodna

<code>prodna</code> generates an incomplete dataset by removing a proportion of observed values

Allows clustering of incomplete observations by addressing missing values using multiple imputation. For achieving this goal, the methodology consists in three steps, following Audigier and Niang 2022 <doi:10.1007/s11634-022-00519-1>. I) Missing data imputation using dedicated models. Four multiple imputation methods are proposed, two are based on joint modelling and two are fully sequential methods, as discussed in Audigier et al. (2021) <doi:10.48550/arXiv.2106.04424>. II) cluster analysis of imputed data sets. Six clustering methods are available (distances-based or model-based), but custom methods can also be easily used. III) Partition pooling. The set of partitions is aggregated using Non-negative Matrix Factorization based method. An associated instability measure is computed by bootstrap (see Fang, Y. and Wang, J., 2012 <doi:10.1016/j.csda.2011.09.003>). Among applications, this instability measure can be used to choose a number of clusters with missing values.
The package also proposes several diagnostic tools to tune the number of imputed data sets, to tune the number of iterations in fully sequential imputation, to check the fit of imputation models, etc.

Vincent Audigier

clusterMI

Cluster Analysis with Missing Values by Multiple Imputation

Hang Joon Kim

prodna function

<dl><dt>X</dt>
<dd>a data frame (or matrix).</dd>
<dt>pct</dt>
<dd>proportion of missing values. By default pct = 0.3.</dd></dl>

Arguments

Introduce missing values using a missing completely at random mechanism — prodna

<dl>

<dt>X</dt>
<dd>a data frame (or matrix).</dd>


<dt>pct</dt>
<dd>proportion of missing values. By default pct = 0.3.</dd>

</dl>

prodna: Introduce missing values using a missing completely at random mechanism

Description

Usage

Value

Arguments

Examples