mcfa {cfa}R Documentation

Repeated measures analysis of configuration frequencies

Description

Determines the frequency of all combinations of variable values (their configurations) over time or with and without treatment in comparison with their expected frequency and displays them in the order of decreasing chi-squared. In addition, a global chi squared is calculated.

Usage

mcfa(configmatrix, cntmatrix, descending=TRUE, sortonchisq=TRUE, ignore.na=FALSE, verbose=FALSE)

Arguments

configmatrix Dataframe with the variables to be analyzed
cntmatrix Matrix with >=2 columns of counts (containing 1 if the data are not aggregated)
descending Output in the order of decreasing chi squared
sortonchisq Sort output on chi squared
ignore.na Ignore (casewise) missing data in the configurations
verbose Long output

Details

Each variable must have at least two different values and may have more (extension of the classical CFA). The configmatrix must consist of at least two variables (columns). Factors and numbers are both accepted (the numbers are internally converted to factors). The cntmatrix must be numeric. Counts should be at least = 5 for the chi squared test to be reliable but when using the CFA as a purely heuristic tool counts of 0 are possible.

Value

A list with class "mcfa" contains the tabular results and the overall parameters

Row names Configuration
n(1)..n(configs) Frequency (count) of this configuration
expected(1)..expected(config) Expected Frequency (count) of this configuration
chi.sq Chi squared for the given configuration
p(chisq) p(chi squared) for the given configuration
Overall chi squared Overall chi squared
p(chi squared) p(overall chi squared)
Degrees of freedom Overall degrees of freedom

WARNING

The program is implemented in R itself rather than a compiled library and therefore slow. In most cases the input is a pre-aggregated table and speed is no problem because the configmatrix is small. There are no hard-coded limits in the program so even large tables can be processed but this will take time and memory.

The outout table can be very wide if the levels of the factors variables are long so options(width=..) may need to be adjusted

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, hier.cfa, boot.cfa

Examples

library(cfa)
data(cfa2dat)
mcfa(cfa2dat[1:3],cfa2dat[4:5],verbose=T)