qlcMatrix (version 0.9.2)

cosNominal: Associations-measures for sparsely encoded nominal variables

Description

Nominal variables can be encoded as a combination of a sparse incidence and index matrix. Various functions to compute variations of assocSparse and cosSparse for such data are described here.

Usage

cosCol(X, colGroupX, Y = NULL, colGroupY = NULL, norm = norm2 )
assocCol(X, colGroupX, Y = NULL, colGroupY = NULL, method = res, sparse = TRUE)
cosRow(X, rowGroup, Y = NULL, norm = norm2 , weight = NULL)
assocRow(X, rowGroup, Y = NULL, method = res)

Value

When Y = NULL, then all methods return symmetric similarity matrices in the form dsCMatrix, only specifying the upper triangle. The only exception is when sparse=T is chose, then the result will be in the form dsyMatrix.

When a second matrix Y is specified, the result will be of the kind dgCMatrix or dgeMatrix, respectively.

Details

The approaches assoc and cos are described in detail in assocSparse and cosSparse, respectively. Those methods are extended here in case either the columns (.col) or the rows (.row) form groups. Specifically, this occurs with sparse encoding of nominal variables (see splitTable). In such encoding, the different values of a nominal variable are encoded in separate columns. However, these columns cannot be treated independently, but have to be treated as groups.

The .col methods should be used when similarities between the different values of nominal variables are to be computed. The .row methods should be used when similarities between the observations of nominal variables are to be computed.

Note that the calculations of the assoc functionsreally only makes sense for binary data (i.e. matrices with only ones and zeros). Currently, all input is coerced to such data by as(X, "nMatrix")*1, meaning that all values that are not one or zero are turned into one (including negative values!).

See Also

sim.att, sim.obs for convenient shortcuts around these methods.

Examples

# convenience functions are easiest to use
# first a simple example using the farms-dataset from MASS
library(MASS)

# to investigate the relation between the individual values
# This is similar to Multiple Correspondence Analysis (see mca in MASS)
f <- splitTable(farms)
s <- assocCol(f$OV,f$AV)
rownames(s) <- f$values
plot(hclust(as.dist(-s)))