Cluster Analysis with Missing Values by Multiple Imputation
Consensus clustering using non-negative matrix factorization
Multiple imputation methods for cluster analysis
MclustBootstrap with nboot = 1 and the same output as Mclust
internal function
one fold cross-validation for specifying threshold r
Overimputation diagnostic plot
Introduce missing values using a missing completely at random mechanis...
clusterMI: Cluster Analysis with Missing Values by Multiple Imputation
Tune the number of iterations for variable selection using varselbest
Cluster analysis and pooling after multiple imputation
Diagnostic plot for the number of iterations used in the varselbest fu...
Graphical investigation for the number of datasets generated by multip...
Diagnostic plot for the number of iterations used in sequential imputa...
Tune the number of clusters according to the partition instability
Kfold cross-validation for specifying threshold r
Apply clustering method after multiple imputation
Class "Rcpp_modelobject"
Compute Silhouette index
Variable selection for specifying conditional imputation models
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.