# ca: Simple correspondence analysis In ca: Simple, Multiple and Joint Correspondence Analysis

## Description

Computation of simple correspondence analysis.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```ca(obj, ...) ## S3 method for class 'matrix' ca(obj, nd = NA, suprow = NA, supcol = NA, subsetrow = NA, subsetcol = NA, ...) ## S3 method for class 'data.frame' ca(obj, ...) ## S3 method for class 'table' ca(obj, ...) ## S3 method for class 'xtabs' ca(obj, ...) ## S3 method for class 'formula' ca(formula, data, ...) ```

## Arguments

 `obj,formula` The function is generic, accepting various forms of the principal argument for specifying a two-way frequency table. Currently accepted forms are matrices, data frames (coerced to frequency tables), objects of class `"xtabs"` or `"table"` and one-sided formulae of the form `~ F1 + F2`, where `F1` and `F2` are factors. `nd ` Number of dimensions to be included in the output; if NA the maximum possible dimensions are included. `suprow ` Indices of supplementary rows. `supcol ` Indices of supplementary columns. `subsetrow` Row indices of subset. `subsetcol` Column indices of subset. `data ` A data frame against which to preferentially resolve variables in the `formula` `... ` Other arguments passed to the `ca.matrix` method

## Details

The function `ca` computes a simple correspondence analysis based on the singular value decomposition.
The options `suprow` and `supcol` allow supplementary (passive) rows and columns to be specified. Using the options `subsetrow` and/or `subsetcol` result in a subset CA being performed.

## Value

 `sv ` Singular values `nd ` Dimenson of the solution `rownames ` Row names `rowmass ` Row masses `rowdist ` Row chi-square distances to centroid `rowinertia` Row inertias `rowcoord ` Row standard coordinates `rowsup ` Indices of row supplementary points `colnames ` Column names `colmass ` Column masses `coldist ` Column chi-square distances to centroid `colinertia` Column inertias `colcoord ` Column standard coordinates `colsup ` Indices of column supplementary points `N ` The frequency table

## 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/

Greenacre, M. (2007). Correspondence Analysis in Practice. Second Edition. London: Chapman & Hall / CRC. Blasius, J. and Greenacre, M. J. (1994), Computation of correspondence analysis, in Correspondence Analysis in the Social Sciences, pp. 53-75, London: Academic Press.

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.

`svd`, `plot.ca`, `plot3d.ca`, `summary.ca`, `print.ca`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ``` data("author") ca(author) plot(ca(author)) # table method haireye <- margin.table(HairEyeColor, 1:2) haireye.ca <- ca(haireye) haireye.ca plot(haireye.ca) # some plot options plot(haireye.ca, lines=TRUE) plot(haireye.ca, arrows=c(TRUE, FALSE)) ```