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Last data update: 2014.03.03

R: Forced Classification Analysis

dsFC	R Documentation

Forced Classification Analysis

Description

This program is for forced classification of dual scaling.

Usage

dsFC(X, Crit, dim)

Arguments

`X`	The Initial Data.
`Crit`	The criterion item for forced classification.
`dim`	The maximun number of components to be extracted.

Details

There are three types of outputs: Forced classification of the criterion item (type A); dual scaling of non-criterion items by ignoring the criterion item (type B); dual scaling of non-criterion items after eliminating the influence of the criterion item (type C). These three types correspond to, respectively, dual scaling of data projected onto the subspace of the criterion item, dual scaling of non-criterion items, and dual scaling of data in the complementary space of the criterion item.

Value

`Match`	Match-mismatch tables
`Predict`	Correct prediction percentages
`Proj.Op_A`	Projected options weights
`Proj.Su_A`	Projected subject scores
`Inf_A`	Distribution of information over components
`ItemStat_A`	Item statistics
`Out_A`	Results obtained by forced classification
`Rij_A`	Inter-item correlation
`Norm.Op_A`	Normed options weights
`Norm.Su_A`	Normed subject scores

References

Nishisato (1984). Forced classification: A simple application of a quantification technique. Psychometrika, 49, 25-36.

Examples

  data(singapore)
  dsFC(singapore,2,6)