CA {MVar}

Correspondence Analysis (CA).

Description

Performs simple correspondence analysis (CA) and multiple (MCA) in a data set.

Usage

CA(data, typdata = "f", typmatrix = "I")

Arguments

data

Data to be analyzed (contingency table).

typdata

"f" for frequency data (default),
"c" for qualitative data.

typmatrix

Matrix used for calculations when typdata = "c".
"I" for indicator matrix (default),
"B" for Burt's matrix.

Value

depdata

Verify if the rows and columns are dependent, or independent by the chi-square test, at the 5% significance level.

typdata

Data type: "F" frequency or "C" qualitative.

numcood

Number of principal components.

mtxP

Matrix of the relative frequency.

vtrR

Vector with sums of the rows.

vtrC

Vector with sums of the columns.

mtxPR

Matrix with profile of the rows.

mtxPC

Matrix with profile of the columns

mtxZ

Matrix Z.

mtxU

Matrix with the eigenvectors U.

mtxV

Matrix with the eigenvectors V.

mtxL

Matrix with eigenvalues.

mtxX

Matrix with the principal coordinates of the rows.

mtxY

Matrix with the principal coordinates of the columns.

mtxAutvlr

Matrix of the inertias (variances), with the proportions and proportions accumulated.

Author(s)

Paulo Cesar Ossani

Marcelo Angelo Cirillo

References

Mingoti, S. A. Analise de dados atraves de metodos de estatistica multivariada: uma abordagem aplicada. Belo Horizonte: UFMG, 2005. 297 p.

Rencher, A. C. Methods of multivariate analysis. 2th. ed. New York: J.Wiley, 2002. 708 p.

See Also

Plot.CA

Examples

data(DataFreq) # frequency data set

data <- DataFreq[,2:ncol(DataFreq)]

rownames(data) <- as.character(t(DataFreq[1:nrow(DataFreq),1]))

res <- CA(data = data, "f") # performs CA

print("Is there dependency between rows and columns?"); res$depdata

print("Number of principal coordinates:"); res$numcood

print("Principal coordinates of the rows:"); round(res$mtxX,2)

print("Principal coordinates of the columns:"); round(res$mtxY,2)

print("Inertia of the principal components:"); round(res$mtxAutvlr,2)