# Factominer r

Rcmdr Plugin for the 'FactoMineR' package.

In this article, we present FactoMineR an R package dedicated to multivariate data analysis. The main features of this package is the possibility The FactoMineR package is dedicated to principal components methods to explore, sum-up and visualize data. Dimensionality reduction methods include PCA, FactoMineR: An R package for multivariate analysis. S Lê, J Josse, F Husson. Journal of statistical software 25 (1), 1-18, 2008. 4273, 2008.

23.12.2020

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Details. The function first built a hierarchical tree. Then the sum of the within-cluster inertia are calculated for each partition. The suggested partition is the one with the higher relative loss of inertia (i(clusters n+1)/i(cluster n)). How to perform PCA with FactoMineR (a package of the R software)?Taking into account supplementary qualitative and/or quantitative variables, examinig the qu R plot.MCA -- FactoMineR. Draw the Multiple Correspondence Analysis (MCA) graphs. FactoMineR::plot.MCA is located in package FactoMineR.Please install and load :exclamation: This is a read-only mirror of the CRAN R package repository.

## In this article, we present FactoMineR an R package dedicated to multivariate data analysis. The main features of this package is the possibility to take into account different types of variables

0 Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple R Development Page Contributed R Packages .

### Sensory groups data: Rmarkdown – the script with the outputs · FactoMineR FactoshinyMFAMultiple Factor AnalysisR · All you need to know to analyse a survey

In FactoMineR: Multivariate Exploratory Data Analysis and Data Mining. Description Usage Arguments Value Author(s) References See Also Examples. Description.

Load the data set as a text file by clicking here. Presentation of
Package ‘FactoMineR’ March 29, 2013 Version 1.24 Date 2013-03-12 Title Multivariate Exploratory Data Analysis and Data Mining with R Author Francois Husson, Julie Josse, Sebastien Le, Jeremy Mazet Maintainer Francois Husson

Automatic Reporting with FactoInvestigate The package FactoInvestigate can propose you an automatic interpretation of your results obtained with PCA, CA or MCA. See the section Automatic reporting to have a description of this package. Jul 13, 2017 · Here is a course with videos that present Principal Component Analysis in a French way. Three videos present a course on PCA, highlighting the way to interpret the data. Then you will find videos presenting the way to implement in FactoMineR, to deal with missing values in PCA thanks to Extracting Principal Components in FactoMiner R. Ask Question Asked 5 years, 1 month ago.

Comment améliorer les graphiques, comment gérer les libellés pour avoir des graphiques :exclamation: This is a read-only mirror of the CRAN R package repository. FactoMineR — Multivariate Exploratory Data Analysis and Data Mining. We’ll use i) the FactoMineR package (Sebastien Le, et al., 2008) to compute PCA, (M)CA, FAMD, MFA and HCPC; ii) and the factoextra package for extracting and visualizing the results. FactoMineR is a great and my favorite package for computing principal component methods in R. It’s very easy to use and very well documented. Arguments x.

As it seems I can only display ether the variables or the individuals with the built in ploting dev R code. The function FAMD() [FactoMiner package] can be used to compute FAMD. A simplified format is : FAMD (base, ncp = 5, sup.var = NULL, ind.sup = NULL, graph = TRUE) base: a data frame with n rows (individuals) and p columns (variables). Hierarchical classification on principle components. Hierarchical Clustering on Principal Components .

FactoMineR — Multivariate Exploratory Data Analysis and Data Mining. We’ll use i) the FactoMineR package (Sebastien Le, et al., 2008) to compute PCA, (M)CA, FAMD, MFA and HCPC; ii) and the factoextra package for extracting and visualizing the results.

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### R code The function FAMD () [ FactoMiner package] can be used to compute FAMD. A simplified format is : FAMD (base, ncp = 5, sup.var = NULL, ind.sup = NULL, graph = TRUE)

Abstract. In this article, we present FactoMineR an R package dedicated to multivariate data analysis.

## The FactoMineR package is dedicated to principal components methods to explore, sum-up and visualize data. Dimensionality reduction methods include PCA,

The main features of this package is the possibility to take into account different types of variables (quantitative or categorical), different types of structure on the data (a partition on the variables, a hierarchy on the variables, a partition on the individuals) and … 7/13/2017 5/10/2017 7/13/2017 row.sup.

a list of matrices containing all the results for the supplementary row points (coordinates, square cosine) quanti.sup. if quanti.sup is not NULL, a matrix containing the results for the supplementary continuous variables (coordinates, square cosine) quali.sup. 1/17/2020 R Development Page Contributed R Packages . Below is a list of all packages provided by project FactoMineR.. Important note for package binaries: R-Forge provides these binaries only for the most recent version of R, but not for older versions. In order to successfully install the packages provided on R-Forge, you have to switch to the most recent version of R or, alternatively, install from Downloadable! In this article, we present FactoMineR an R package dedicated to multivariate data analysis.