pcc: Pearson's Contingency Coefficient
In scrime: Analysis of High-Dimensional Categorical Data Such as SNP Data

Description Usage Arguments Value Author(s) See Also Examples

Computes the values of (the corrected) Pearson's contingency coefficient for all pairs of rows of a matrix.

1	pcc(x, dist = FALSE, corrected = TRUE, version = 1)

`x`	a numeric matrix consisting of integers between 1 and n.cat, where n.cat is the maximum number of levels a variable in `x` can take.
`dist`	should the distance based on Pearson's contingency coefficient be computed? For how this distance is computed, see `version`.
`corrected`	should Pearson's contingency coefficient be corrected such that it can take values between 0 and 1? If not corrected, it takes values between and 0 and sqrt((a - 1) / a), where a is the minimum of the numbers of levels that the respective two variables can take. Must be set to `TRUE`, if `dist = TRUE`.
`version`	a numeric value – either 1, 2, or 3 – specifying how the distance is computed. Ignored if `dist = FALSE`. If `1`, sqrt(1 - Cont^2) is computed, where Cont denotes Pearson's contigency coefficient. If 2, 1 - Cont is determined, and if 3, 1 - Cont^2 is returned.

A matrix with nrow(x) columns and rows containing the values of (or distances based on) the (corrected) Pearson's contigency coefficient for all pairs of rows of x.

Holger Schwender, holger.schwender@udo.edu

smc

## Not run: 
# Generate a data set consisting of 10 rows and 200 columns,
# where the values are randomly drawn from the integers 1, 2, and 3.

mat <- matrix(sample(3, 2000, TRUE), 10)

# For each pair of rows of mat, the value of the corrected Pearson's 
# contingency coefficient is then obtained by

out1 <- pcc(mat)
out1

# and the distances based on this coefficient by

out2 <- pcc(mat, dist = TRUE)
out2

# Note that if version is set to 1 (default) in pcc, then

all.equal(sqrt(1 - out1^2), out2)


## End(Not run)