hier.cfa {cfa}R Documentation

Hierachical analysis of configuration frequencies

Description

Recursively eliminates variables to generate subtables which are subjected to a CFA and calculates the overall chi squared.

Usage

hier.cfa(configmatrix, cntvector)

Arguments

configmatrix Dataframe with the variables to be analyzed
cntvector Vector of counts (1 if the data are not aggregated)

Details

The hierarchical CFA is used to eliminate unnecessary variables. The higher the overall chi squared, the better the information from the original table is preserved. Given a constant number of variables in the subtables, the subtable with the highest chi squared is the "best". Sometimes it makes sense to accept a somewhat lower chi squared if the number of variables is lower because of better interpretability.

Value

Row names Configuration
Overall chi squared Overall chi squared for this subtable
order Number of variables used

WARNING

The program is implemented in R itself rather than a library and therefore slow. It repeatedly calls a simplified (and faster) version of cfa. With increasing number of variables in the configuration the running time will incease expontentially

Author(s)

Stefan Funke <funke@attglobal.net>

References

Krauth J., Lienert G. A. (1973, Reprint 1995) Die Konfigurationsfrequenzanalyse (KFA) und ihre Anwendung in Psychologie und Medizin Beltz Psychologie Verlagsunion

Eye, A. von (1990) Introduction to configural frequency analysis. The search for types and anti-types in cross-classification. Cambride 1990

See Also

cfa, boot.cfa

Examples

library(cfa)
data(cfadat)
hier.cfa(cfadat[c("gender","married","children")],cfadat["count"])