res.hcpc <- HCPC(res kk=Inf
Plots graphs from a HCPC result: tree barplot of inertia gains and first factor map with or without the tree
The proposed methodology is available in the HCPC (Hierarchical Clustering on Principal. Components) function of the FactoMineR package. Keywords: Exploratory
res.hcpc <- HCPC(res.mfa). ##### Example of clustering on categorical data data(tea) res.mca <- MCA(teaquanti.sup=19
13 ago 2014 Perform HCPC with the GUI. Description. Perform HCPC with the GUI. Usage. FactoHCPC(). Author(s). F. Husson. FactoMCA. Perform MCA with the GUI.
20 ene 2015 Factoshiny allows to perform CA PCA
res.hcpc <- HCPC(res consol=FALSE). 12. Page 13. 0. 2. 4. 6. Hierarchical clustering inertia gain. Athens. Lisbon. Madrid. Rome. Re ykja vik. Mosco w. Minsk.
Brings a set of tools to help and automatically realise the description of principal component analy- ses (from 'FactoMineR' functions). HCPC a boolean : if ...
A HCPC object see HCPC for details. axes a two integers vector.Defines the axes of the factor map to plot. choice. A string. "tree" plots the tree. "bar
Lien entre la partition en classes et l'une ou l'autre des questions. > res.hcpc$desc.var. $test.chi2 p.value df
The proposed methodology is available in the HCPC (Hierarchical Clustering on Principal. Components) function of the FactoMineR package.
Bien entendu la fonction HCPC de FactoMineR pourrait être remplacée par tout autre algorithme de classification avec ses propres options (hclust
Nota bene : il existe maintenant un manuel papier dédié à FactoMineR qui explique à HCPC(res
Lien entre la partition en classes et l'une ou l'autre des questions. > res.hcpc$desc.var. $test.chi2 p.value df
res.hcpc$call$t. ## $res. ## **Results for the Principal Component Analysis (PCA)**. ## The analysis was performed on 41 individuals described by 13
Méthodes de classification. K-means. Classification ascendante hiérarchique kmeans. HCPC FactoMineR est disponible sur le site officiel de R (CRAN).
Le package FactoMineR est dédié à l'analyse de données. dances Multiples (MCA) et construction ascendante d'une hiérarchie (HCPC). Des méthodes.
Bien entendu la fonction HCPC de FactoMineR pourrait être remplacée par tout autre algorithme de classification avec ses propres options (hclust
26 sept. 2014 FactoMineR: An R Package for Multivariate Analysis. Journal ... Hierarchical Clustering on Principle Components (HCPC). Description.
Le package FactoMineR et le menu FactoMineR de R Commander ont été installés sur (pratiquement res.hcpc$data.clust[ncol(res.hcpc$data.clust)
The function HCPC () [in FactoMineR package] can be used to compute hierarchical clustering on principal components. res: Either the result of a factor analysis or a data frame. nb.clust: an integer specifying the number of clusters. Possible values are: We start by computing again the principal component analysis (PCA).
Partitioning clustering such as k-means algorithm, used for splitting a data set into several groups. The HCPC ( Hierarchical Clustering on Principal Components) approach allows us to combine the three standard methods used in multivariate data analyses (Husson, Josse, and J. 2010): Partitioning clustering, particularly the k-means method.
FactoMineR: An R package for multivariate analysis. Journal of Statistical Software 25 (1): 1–18. Lucas, A. (2014). amap: Another Multidimensional Analysis Package.