Learn R Programming

⚠️There's a newer version (2.2.7) of this package.Take me there.

MVar (version 2.0.4)

Multivariate Analysis

Description

Package for multivariate analysis, having functions that perform simple correspondence analysis (CA) and multiple correspondence analysis (MCA), principal components analysis (PCA), canonical correlation analysis (CCA), factorial analysis (FA), multidimensional scaling (MDS), hierarchical and non-hierarchical cluster analysis, linear regression, multiple factor analysis (MFA) for quantitative, qualitative, frequency (MFACT) and mixed data, projection pursuit (PP), grant tour method and other useful functions for the multivariate analysis.

Copy Link

Version

Install

install.packages('MVar')

Monthly Downloads

441

Version

2.0.4

License

GPL (>= 2)

Maintainer

Paulo Cesar Ossani

Last Published

February 24th, 2019

Functions in MVar (2.0.4)

CoefVar

Coefficient of variation of the data.
DataFreq

Frequency data set.
DataInd

Frequency data set.
Cluster

Cluster Analysis.
CCA

Canonical Correlation Analysis(CCA).
GSVD

Generalized Singular Value Decomposition (GSVD).
DataMix

Mixed data set.
DataCoffee

Frequency data set.
GrandTour

Animation technique Grand Tour.
Plot.PCA

Graphs of the Principal Components Analysis (PCA).
MDS

Multidimensional Scaling (MDS).
MFA

Multiple Factor Analysis (MFA).
DataQuan

Quantitative data set
Plot.CA

Graphs of the simple (CA) and multiple correspondence analysis (MCA).
Plot.PP

Graphics of the Projection Pursuit (PP).
Plot.Regr

Graphs of the linear regression results.
PP_Index

Function to find the Projection Pursuit indexes (PP).
Plot.CCA

Graphs of the Canonical Correlation Analysis (CCA).
DataQuali

Qualitative data set
PP_Optimizer

Optimization function of the Projection Pursuit index (PP).
FA

Factor Analysis (FA).
Plot.FA

Graphs of the Factorial Analysis (FA).
Regr

Linear regression.
MVar

Multivariate Analysis.
NormData

Normalizes the data.
Plot.MFA

Graphics of the Multiple Factor Analysis (MFA).
Biplot

Biplot graph.
CA

Correspondence Analysis (CA).
IM

Indicator matrix.
LocLab

Function for better position of the labels in the graphs.
NormTest

Test of normality of the data.
PCA

Principal Components Analysis (PCA).