Man pages for clusterMI
Cluster Analysis with Missing Values by Multiple Imputation
| chooseB | Diagnostic plot for the number of iterations used in the... |
| chooseB.intern | Tune the number of iterations for variable selection using... |
| choosem | Graphical investigation for the number of datasets generated... |
| choosemaxit | Diagnostic plot for the number of iterations used in... |
| choosenbclust | Tune the number of clusters according to the partition... |
| chooser | Kfold cross-validation for specifying threshold r |
| cluster.intern | Apply clustering method after multiple imputation |
| clusterMI | Cluster analysis and pooling after multiple imputation |
| clusterMI-package | clusterMI: Cluster Analysis with Missing Values by Multiple... |
| fastnmf | Consensus clustering using non-negative matrix factorization |
| imputedata | Multiple imputation methods for cluster analysis |
| initfastnmf | initialize fastnmf |
| mclustboot.intern | MclustBootstrap with nboot = 1 and the same output as Mclust |
| myem.mix | internal function |
| onefold.chooser | one fold cross-validation for specifying threshold r |
| overimpute | Overimputation diagnostic plot |
| prodna | Introduce missing values using a missing completely at random... |
| Rcpp_modelobject-class | Class '"Rcpp_modelobject"' |
| Silhouette.intern | Compute Silhouette index |
| varselbest | Variable selection for specifying conditional imputation... |
| wine | Chemical analysis of wines from three different cultivars |
