R: Multiple and joint correspondence analysis

mjca {ca}

R Documentation

Multiple and joint correspondence analysis

Description

Computation of multiple and joint correspondence analysis.

Usage

mjca(obj, nd = 2, lambda = c("adjusted", "indicator", "Burt", "JCA"), 
     supcol = NA, subsetcol = NA, 
     ps = ":", maxit = 50, epsilon = 0.0001, reti = FALSE)

Arguments

`obj`	A response pattern matrix (data frame containing factors), or a frequency table (a table object)
`nd`	Number of dimensions to be included in the output; if NA the maximum possible dimensions are included.
`lambda`	Gives the scaling method. Possible values include `"indicator"`, `"Burt"`, `"adjusted"` and `"JCA"`. Using `lambda = "JCA"` results in a joint correspondence analysis using iterative adjusment of the Burt matrix in the solution space.
`supcol`	Indices of supplementary columns.
`subsetcol`	Indices of subset categories.
`ps`	Separator used for combining variable and category names.
`maxit`	The maximum number of iterations (Joint Correspondence Analysis).
`epsilon`	A convergence criterion (Joint Correspondence Analysis).
`reti`	Logical indicating whether the indicator matrix should be included in the output.

Details

The function mjca computes a multiple or joint correspondence analysis based on the eigenvalue decomposition of the Burt matrix.

Value

`sv`	Eigenvalues (`lambda = "indicator"`) or singular values (`lambda = "Burt"`, `"adjusted"` or `"JCA"`)
`lambda`	Scaling method
`inertia.e`	Percentages of explained inertia
`inertia.t`	Total inertia
`inertia.et`	Total percentage of explained inertia with the `nd`-dimensional solution
`levelnames`	Names of the factor/level combinations, joined using `ps`
`factors`	A matrix containing the names of the factors and the names of the factor levels
`levels.n`	Number of levels in each factor
`nd`	User-specified dimensionality of the solution
`nd.max`	Maximum possible dimensionality of the solution
`rownames`	Row names
`rowmass`	Row masses
`rowdist`	Row chi-square distances to centroid
`rowinertia`	Row inertias
`rowcoord`	Row standard coordinates
`rowpcoord`	Row principal coordinates
`rowctr`	Row contributions
`rowcor`	Row squared correlations
`colnames`	Column names
`colmass`	Column masses
`coldist`	Column chi-square distances to centroid
`colinertia`	Column inertias
`colcoord`	Column standard coordinates
`colpcoord`	Column principal coordinates
`colctr`	column contributions
`colcor`	Column squared correlations
`colsup`	Indices of column supplementary points (of the Burt and Indicator matrix)
`subsetcol`	Indices of subset columns
`Burt`	Burt matrix
`Burt.upd`	The updated Burt matrix (JCA only)
`subinertia`	Inertias of sub-matrices
`JCA.iter`	Vector of length two containing the number of iterations and the epsilon (JCA only)
`indmat`	Indicator matrix if `reti` was set to `TRUE`
`call`	Return of `match.call`

References

Nenadic, O. and Greenacre, M. (2007), Correspondence analysis in R, with two- and three-dimensional graphics: The ca package. Journal of Statistical Software, 20 (3), http://www.jstatsoft.org/v20/i03/
Nenadic, O. and Greenacre, M. (2007), Computation of Multiple Correspondence Analysis, with Code in R, in Multiple Correspondence Analysis and Related Methods (eds. M. Greenacre and J. Blasius), Boca Raton: Chapmann & Hall / CRC, pp. 523-551.
Greenacre, M.J. and Pardo, R. (2006), Subset correspondence analysis: visualizing relationships among a selected set of response categories from a questionnaire survey. Sociological Methods and Research, 35, pp. 193-218.

Examples

 
data("wg93")
mjca(wg93[,1:4])

### Different approaches to multiple correspondence analysis:
# Multiple correspondence analysis based on the indicator matrix:
mjca(wg93[,1:4], lambda = "indicator")

# Multiple correspondence analysis based on the Burt matrix:
mjca(wg93[,1:4], lambda = "Burt")

# "Adjusted" multiple correspondence analysis (default setting):
mjca(wg93[,1:4], lambda = "adjusted")

# Joint correspondence analysis:
mjca(wg93[,1:4], lambda = "JCA")


### Subset analysis and supplementary variables:
# Subset analysis:
mjca(wg93[,1:4], subsetcol = (1:20)[-seq(3,18,5)])

# Supplementary variables:
mjca(wg93, supcol = 5:7)

# Combining supplementary variables and a subset analysis:
mjca(wg93, supcol = 5:7, subsetcol = (1:20)[-seq(3,18,5)]) 

# table input
data(UCBAdmissions)
mjca(UCBAdmissions)
plot(mjca(UCBAdmissions))

[Package ca version 0.58 Index]