computeContClass: Rowwise Contigency Tables
In scrime: Analysis of High-Dimensional Categorical Data Such as SNP Data

Description Usage Arguments Value Author(s) References See Also Examples

Generates a matrix containing a contingency table for each row of a matrix and a vector of class labels.

1	computeContClass(data, cl, n.cat)

`data`	a numeric matrix consisting of integers between 1 and `n.cat`. Each row of data is assumed to represent a variable, and each column to represent an observation. Missing values are not allowed. All variables must comprise the same number of levels.
`cl`	a numeric vector of length `ncol(data)` specifying the class labels of the observations represented by the columns of data. `cl` must consist of integers between 1 and n.cl, where n.cl is the number of classes.
`n.cat`	an integer giving the number of levels the variables can take. If not specified, `n.cat` will be determined automatically. It is highly recommended not to specify `n.cat`.

A list composed of the following two matrices:

`mat.obs`	a matrix consisting of m rows and `n.cat` * n.cl columns, where m is the number of variables, i.e.\ the number of rows of `data`. Each row of data shows the contingency table of `cl` and the corresponding row of `data`.
`mat.exp`	a matrix of the same size as `mat.obs` containing the numbers of observations expected under the null hypothesis of equal distribution in all classes that correspond to the respective entries in `mat.obs`.

Holger Schwender, holger.schwender@udo.edu

Schwender, H.\ (2007). A Note on the Simultaneous Computation of Thousands of Pearson's ChiSquare-Statistics. Technical Report, SFB 475, Deparment of Statistics, University of Dortmund.

computeContCells, rowChisqStats

## Not run: 
# Generate an example data set consisting of 10 rows (variables)
# and 200 columns (observations) by randomly drawing integers 
# between 1 and 3, and a vector of class labels of length 200
# indicating that the first 100 observation belong to class 1
# and the other 100 to class 2. 

mat <- matrix(sample(3, 2000, TRUE), 10)
cl <- rep(1:2, e = 100)

# Applying computeContClass to this data set

out <- computeContClass(mat, cl)

# generates the observed numbers of observations

out$mat.obs

# and the corresponding expected numbers of observations.

out$mat.exp


## End(Not run)