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FactoMineR (version 1.39)

Multivariate Exploratory Data Analysis and Data Mining

Description

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 correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017) .

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Version

Install

install.packages('FactoMineR')

Monthly Downloads

112,895

Version

1.39

License

GPL (>= 2)

Maintainer

Francois Husson

Last Published

November 10th, 2017

Functions in FactoMineR (1.39)

HCPC

Hierarchical Clustering on Principle Components (HCPC)
HMFA

Hierarchical Multiple Factor Analysis
coord.ellipse

Construct confidence ellipses
decathlon

Performance in decathlon (data)
ellipseCA

Draw confidence ellipses in CA
estim_ncp

Estimate the number of components in Principal Component Analysis
plot.DMFA

Draw the Dual Multiple Factor Analysis (DMFA) graphs
plot.FAMD

Draw the Multiple Factor Analysis for Mixt Data graphs
poison

Poison
poison.text

Poison
prefpls

Scatter plot and additional variables with quality of representation contour lines
print.AovSum

Print the AovSum results
print.MCA

Print the Multiple Correspondance Analysis (MCA) results
print.MFA

Print the Multiple Factor Analysis results
tab.disjonctif.prop

Make a disjunctive table when missing values are present
tea

tea (data)
JO

Number of medals in athletism during olympic games per country
MCA

Multiple Correspondence Analysis (MCA)
RegBest

Select variables in multiple linear regression
autoLab

Function to better position the labels on the graphs
descfreq

Description of frequencies
dimdesc

Dimension description
plot.CA

Draw the Correspondence Analysis (CA) graphs
predict.MFA

Predict projection for new rows with Multiple Factor Analysis
predict.PCA

Predict projection for new rows with Principal Component Analysis
print.PCA

Print the Principal Component Analysis (PCA) results
reconst

Reconstruction of the data from the PCA, CA or MFA results
textual

Text mining
wine

Wine
CaGalt

Correspondence Analysis on Generalised Aggregated Lexical Table (CaGalt)
DMFA

Dual Multiple Factor Analysis (DMFA)
MFA

Multiple Factor Analysis (MFA)
PCA

Principal Component Analysis (PCA)
footsize

footsize
geomorphology

geomorphology(data)
milk

milk
plot.CaGalt

Draw the Correspondence Analysis on Generalised Aggregated Lexical Table (CaGalt) graphs
plotMFApartial

Plot an interactive Multiple Factor Analysis (MFA) graph
print.FAMD

Print the Multiple Factor Analysis of mixt Data (FAMD) results
print.GPA

Print the Generalized Procrustes Analysis (GPA) results
summary.CA

Printing summeries of ca objects
plot.PCA

Draw the Principal Component Analysis (PCA) graphs
summary.CaGalt

Printing summaries of CaGalt objects
summary.FAMD

Printing summeries of FAMD objects
summary.MCA

Printing summeries of MCA objects
AovSum

Analysis of variance with the contrasts sum (the sum of the coefficients is 0)
CA

Correspondence Analysis (CA)
coeffRV

Calculate the RV coefficient and test its significance
FAMD

Factor Analysis for Mixed Data
FactoMineR-package

Multivariate Exploratory Data Analysis and Data Mining with R
catdes

Categories description
children

Children (data)
GPA

Generalised Procrustes Analysis
graph.var

Make graph of variables
plot.HCPC

Plots for Hierarchical Classification on Principle Components (HCPC) results
plot.HMFA

Draw the Hierarchical Multiple Factor Analysis (HMFA) graphs
plot.MCA

Draw the Multiple Correspondence Analysis (MCA) graphs
condes

Continuous variable description
health

health (data)
hobbies

hobbies (data)
plot.GPA

Draw the General Procrustes Analysis (GPA) map
plot.MFA

Draw the Multiple Factor Analysis (MFA) graphs
predict.FAMD

Predict projection for new rows with Factor Analysis of Mixed Data
predict.MCA

Predict projection for new rows with Multiple Correspondence Analysis
plotGPApartial

Draw an interactive General Procrustes Analysis (GPA) map
plot.catdes

Plots for description of clusters (catdes)
plotellipses

Draw confidence ellipses around the categories
print.CA

Print the Correspondance Analysis (CA) results
print.CaGalt

Print the Correspondence Analysis on Generalised Aggregated Lexical Table (CaGalt) results
print.HCPC

Print the Hierarchical Clustering on Principal Components (HCPC) results
print.HMFA

Print the Hierarchical Multiple Factor Analysis results
summary.MFA

Printing summaries of MFA objects
summary.PCA

Printing summeries of PCA objects
senso

senso
simule

Simulate by bootstrap
svd.triplet

Singular Value Decomposition of a Matrix
tab.disjonctif

Make a disjonctif table
write.infile

Print in a file
mortality

The cause of mortality in France in 1979 and 2006
poulet

Genomic data for chicken
predict.CA

Predict projection for new rows with Correspondence Analysis