afc: Simple Correspondence Analysis

Description

This function calculates simple correspondence analysis for a data matrix.

Usage

afc(x, dim = 2, alpha = 1)

Arguments

A frequency matrix or a binary matrix obtained from the original data set of nominal variables.

dim

Number of dimensions for the solution

alpha

Biplot weight for rows and columns. 1 means rows in principal coordinates and columns in standard coordinates, 0 means rows in standard coordinates and columns in principal coordinates.

Value

Title: Title of the statistical technique
Non_Scaled_Data: Original data
Minima: vector with the minimum values for each column of the initial data matrix
Maxima: vector with the maximum values for each column of the initial data matrix
Initial_Transformation: Name of the transformation for the data
Scaled_Data: Scaled data according to the transformation
nrows: Number of rows of the data set
ncols: Number of columns of the data set
dim: Number of dimensions for the solution
CumInertia: Acumulated Inertia
Scale_Factor: Scale factor for the transformation
RowCoordinates: Coordinates for the individuals in the reduced dimension space
ColCoordinates: Coordinates for the variables in the reduced dimension space
RowContributions: Contributions of the dimensions to explain the inertia of each row
ColContributions: Contributions of the dimensions to explain the inertia of each column
Inertia: Inertia for each dimension
Eigenvalues: Eigenvalues

References

BENZECRI, J.P. (1973) L'analyse des Donnees. Vol. 2. L'analyse des correspondences. Dunod. Paris.

Examples

Run this code

  
  data(HairColor)
  G = NominalMatrix2Binary(data.matrix(HairColor))
	mca=afc(G,dim=2)
  mca