fviz_nbclust() checks now whether the argument FUNcluster is correctly specified (@robsalasco, #82).fviz_mclust_bic() (@hpsprecher, #84)outlier.pointsize and outlier.labelsize added in fviz_cluster() to customize outliers detected with DBSCAN (@choonghyunryu, #74)pointsize in the function fviz() canbe now a continuous variable.hkmeans() takes other distance metrics (@santsang, #52)get_clust_tendency() updated to return the correct value of hopkins statistics as explained at: https://www.datanovia.com/en/lessons/assessing-clustering-tendency/
invisible works properly in the function fviz_pca_biplot()(@ginolhac, #26).fviz_dend() now works for an object of class diana (@qfazille, #30).fviz_cluster() supports HCPC results (@famuvie, #34).mean.point in the function fviz(). logical value. If TRUE, group mean points are added to the plot.fill.ind and fill.var added in fviz_pca() (@ginolhac, #27 and @Confurious, #42).geom.ind and geom.var in fviz_pca_xxx() and fviz_mca_xxx() functions to have more controls on the individuals/variables geometry in the functions fviz_pca_biplot() and fviz_mca_biplot() (@Confurious, #42).geom.row and geom.col in fviz_ca_xxx() functions to have more controls on the individuals/variables geometry in the function fviz_ca_biplot() (@Confurious, #42).gradient.cols in fviz_pca_biplot()
àxes in fviz_cluster() to specify the dimension to plot.New argument circlesize in the function fviz() to change the size of the variable correlation circle size.
It’s now possible to color individuals using a custom continuous variable (#29). This is done using the argument col.ind.
library(factoextra)
data(iris)
res.pca <- prcomp(iris[, -5], scale = TRUE)
# Visualize and color by a custom continuous variable
fviz_pca_ind(res.pca, col.ind = iris$Sepal.Length,
legend.title = "Sepal.Length")library(FactoMineR)
library(factoextra)
.tbl2.1 <- matrix(c(395, 2456,1758,
147, 153, 916,
694, 327, 1347),byrow=T,3,3)
dimnames(.tbl2.1) <- list(地域=c("オスロ","中部地域","北部地域"),
犯罪=c("強盗", "詐欺","破壊") )
res.CA <- CA(.tbl2.1,graph=FALSE)
fviz_ca_biplot(res.CA,map="simbiplot",title="simbiplot",
font.family = "HiraKakuProN-W3")New function fviz_mclust() for plotting model-based clustering using ggplot2.
New function fviz(): Generic function to create a scatter plot of multivariate analyse outputs, including PCA, CA and MCA, MFA, …
New functions fviz_mfa_var() and fviz_hmfa_var() for plotting MFA and HMFA variables, respectively.
New function get_mfa_var(): Extract the results for variables (quantitatives, qualitatives and groups). Deprecated functions: get_mfa_var_quanti(), get_mfa_var_quali() and get_mfa_group().
New functions added for extracting and visualizing the results of FAMD (factor analysis of mixed data): get_famd_ind(), get_famd_var(), fviz_famd_ind() and fviz_famd_var().
Now fviz_dend() returns a ggplot. It can be used to plot circular dendrograms and phylogenic-like trees. Additionnally, it supports an object of class HCPC (from FactoMineR).
fviz_cluster():
fviz_cluster(): to change the plot main title and axis labels.fviz_pca(). When you use habillage, point shapes change automatically by groups. To avoid this behaviour use for example pointshape = 19 in combination with habillage (@raynamharris, #15).fviz_add().New argument gradient.cols in fviz_*() functions.
Support for the ExPosition package added (epCA, epPCA, epMCA) (#23)
fviz_nbclust() to make sure that x is an object of class data.frame or matrix (Jakub Nowosad, #15).The following arguments are deprecated in fviz_cluster(): title, frame, frame.type, frame.level, frame.alpha. Now, use main, ellipse, ellipse.type, ellipse.level and ellipse.alpha instead.
Now, by default, the function fviz_cluster() doesn’t show cluster mean points for an object of class PAM and CLARA, when the argument show.clust.cent is missing . This is because cluster centers are medoids in the case of PAM and CLARA but not means. However, user can force the function to display the mean points by using the argument show.clust.cent = TRUE.
The argument jitter is deprecated; use repel = TRUE instead, to avoid overlapping of labels.
New argument “sub” in fviz_dend() for adding a subtitle to the dendrogram. If NULL, the method used hierarchical clustering is shown. To remove the subtitle use sub = “”.
fviz_cluster() can handle HCPC object obtained from MCA (Alejandro Juarez-Escario, #13)fviz_ca_biplot() reacts when repel = TRUE usedfacto_summarize(), now the contribution values computed for >=2 axes are in percentage (#22)fviz_ca() and fviz_mca() now work with the latest version of ade4 v1.7-5 (#24)New fviz_mfa function to plot MFA individuals, partial individuals, quantitive variables, categorical variables, groups relationship square and partial axes (@inventionate, #4).
New fviz_hmfa function to plot HMFA individuals, quantitive variables, categorical variables and groups relationship square (@inventionate, #4).
New get_mfa and get_hmfa function (@inventionate, #4).
fviz_ca, fviz_pca, fviz_mca, fviz_mfa and fviz_hmfa ggrepel support (@inventionate, #4).
Updated fviz_summarize, eigenvalue, fviz_contrib and fviz_cos2 functions, to compute FactoMineR MFA and HMFA results (@inventionate, #4).
fviz_cluster() added. This function can be used to visualize the outputs of clustering methods including: kmeans() [stats package]; pam(), clara(), fanny() [cluster package]; dbscan() [fpc package]; Mclust() [mclust package]; HCPC() [FactoMineR package]; hkmeans() [factoextra].
fviz_silhouette() added. Draws the result of cluster silhouette analyses computed using the function silhouette()[cluster package]
fviz_nbclust(): Dertemines and visualize the optimal number of clusters
fviz_gap_stat(): Visualize the gap statistic generated by the function clusGap() [in cluster package]
hcut(): Computes hierarchical clustering and cut the tree into k clusters.
hkmeans(): Hierarchical k-means clustering. Hybrid approach to avoid the initial random selection of cluster centers.
get_clust_tendency(): Assessing clustering tendency
fviz_dend(): Enhanced visualization of dendrogram
eclust(): Visual enhancement of clustering analysis
get_dist() and fviz_dist(): Enhanced Distance Matrix Computation and Visualization
eclust(): Visual enhancement of clustering analysis
fviz_ca_biplot()
get_ca()
fviz_contrib()
facto_summarize()