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).New argument mean.point in the function
fviz(). logical value. If TRUE, group mean points are added
to the plot.
Now, PCA correlation circles have fixed coordinates so they don’t appear as ellipses (@scoavoux, #38.
New argument fill.ind and fill.var
added in fviz_pca() (@ginolhac, #27 and @Confurious,
#42).
New arguments 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).
New arguments 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).
New argument gradient.cols in
fviz_pca_biplot()
New argument 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).
New arguments in fviz_cluster():
fviz_cluster(): to change the plot
main title and axis labels.New argument pointshape in 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).
New argument repel in fviz_add().
New argument gradient.cols in fviz_*() functions.
Support for the ExPosition package added (epCA, epPCA, epMCA) (#23)
Check point added in the function 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
Visualization of Correspondence Analysis outputs from different R packages (FactoMineR, ca, ade4, MASS)
fviz_ca_row()
fviz_ca_col()
fviz_ca_biplot()
Extract results from CA output
get_ca_row()
get_ca_col()
get_ca()
Visualize the cos2 and the contributions of rows/columns. The functions can handle the output of PCA, CA and MCA
fviz_cos2()
fviz_contrib()
Sumarize the results of PCA, CA, MCA
facto_summarize()