Function reference • factoextra

Visualizing Dimension Reduction Analysis Outputs Visualization of PCA, CA, MCA, FAMD, MFA and HMFA.
`get_eig()` `get_eigenvalue()` `fviz_eig()` `fviz_screeplot()`	Extract and visualize the eigenvalues/variances of dimensions
`fviz_pca()` `fviz_pca_ind()` `fviz_pca_var()` `fviz_pca_biplot()`	Visualize Principal Component Analysis
`fviz_ca_row()` `fviz_ca_col()` `fviz_ca_biplot()` `fviz_ca()`	Visualize Correspondence Analysis
`fviz_mca_ind()` `fviz_mca_var()` `fviz_mca_biplot()` `fviz_mca()`	Visualize Multiple Correspondence Analysis
`fviz_famd_ind()` `fviz_famd_var()` `fviz_famd()`	Visualize Factor Analysis of Mixed Data
`fviz_mfa_ind()` `fviz_mfa_quali_biplot()` `fviz_mfa_var()` `fviz_mfa_axes()` `fviz_mfa()`	Visualize Multiple Factor Analysis
`fviz_hmfa_ind()` `fviz_hmfa_var()` `fviz_hmfa_quali_biplot()` `fviz_hmfa()`	Visualize Hierarchical Multiple Factor Analysis
`fviz_cos2()`	Visualize the quality of representation of rows/columns
`fviz_contrib()` `fviz_pca_contrib()`	Visualize the contributions of row/column elements
Extracting Data from Dimension Reduction Analysis Outputs Extracting data from the output of PCA, CA, MCA, FAMD, MFA and HMFA.
`facto_summarize()`	Subset and summarize the output of factor analyses
`get_pca()` `get_pca_ind()` `get_pca_var()`	Extract the results for individuals/variables - PCA
`get_ca()` `get_ca_col()` `get_ca_row()`	Extract the results for rows/columns - CA
`get_mca()` `get_mca_var()` `get_mca_ind()`	Extract the results for individuals/variables - MCA
`get_famd()` `get_famd_ind()` `get_famd_var()`	Extract the results for individuals and variables - FAMD
`get_mfa()` `get_mfa_ind()` `get_mfa_var()` `get_mfa_partial_axes()`	Extract the results for individuals/variables/group/partial axes - MFA
`get_hmfa()` `get_hmfa_ind()` `get_hmfa_var()` `get_hmfa_partial()`	Extract the results for individuals/variables/group/partial axes - HMFA
Clustering Computing and visualizing clustering
`get_dist()` `fviz_dist()`	Enhanced Distance Matrix Computation and Visualization
`get_clust_tendency()`	Assessing Clustering Tendency
`fviz_nbclust()` `fviz_gap_stat()`	Dertermining and Visualizing the Optimal Number of Clusters
`fviz_dend()`	Enhanced Visualization of Dendrogram
`fviz_cluster()`	Visualize Clustering Results
`fviz_silhouette()`	Visualize Silhouette Information from Clustering
`hcut()`	Computes Hierarchical Clustering and Cut the Tree
`hkmeans()` `print(<hkmeans>)` `hkmeans_tree()`	Hierarchical k-means clustering
`eclust()`	Visual enhancement of clustering analysis
`fviz_mclust()` `fviz_mclust_bic()`	Plot Model-Based Clustering Results using ggplot2
Data Data sets included in factoextra and used in examples.
`decathlon2`	Athletes' performance in decathlon
`housetasks`	House tasks contingency table
`poison`	Poison
`multishapes`	A dataset containing clusters of multiple shapes
Others
`fviz_add()`	Add supplementary data to a plot
`print(<factoextra>)`	Print method for an object of class factoextra

Reference

Visualizing Dimension Reduction Analysis Outputs

Extracting Data from Dimension Reduction Analysis Outputs

Clustering

Data

Others

Contents