mcfa {cfa}

Two or more-sample CFA

Description

Performs an analysis of configuration frequencies for two or more sets of counts. This function is not designed to be called directly by the user but will only be used internally by cfa(). Both the simple an the multiple cfa are handled by cfa()

Usage

mcfa(cfg, cnts, sorton="chisq", sort.descending=TRUE, format.labels=TRUE)

Arguments

cfg

Contains the configurations. This can be a dataframe or a matrix. The dataframe can contain numbers, characters, factors or booleans. The matrix can consist of numbers, characters or booleans (factors are implicitely re-converted to numerical levels). There must be >=3 columns.

cnts

Contains the counts for the configuration. cnts is a matrix or dataframe with 2 or more columns.

sorton

Determines the sorting order of the output. Can be set to chisq, n, or label.

sort.descending

Sort in descending order

format.labels

Format the labels of the configuration. This makes to output wider but it will increase the readability.

Details

This function is the "engine" cfa() will use. It does the aggregation, summing up, and will calculate chi squared. All tests of significance are left to cfa()

Value

The function returns the following list:

labels

Configuration label

sums

Sums for each configuration and each variable in the configuration

counts

Matrix of observed n of the given configuration

expected

Matrix of expected n for the given configuration

chisq

Chi squared for each configuration

Note

There are no hard-coded limits in the program so even large tables can be processed.

Author(s)

Stefan Funke <[e-mail]>

References

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

Lautsch, E., von Weber S. (1995) Methoden und Anwendungen der Konfigurationsfrequenzanalyse 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, scfa

Examples

 
# library(cfa) if not yet loaded
# Some random configurations:
configs<-cbind(c("A","B")[rbinom(250,1,0.3)+1],c("C","D")[rbinom(250,1,0.1)+1],
          c("E","F")[rbinom(250,1,0.3)+1],c("G","H")[rbinom(250,1,0.1)+1])
counts1<-trunc(runif(250)*10) 
counts2<-trunc(runif(250)*10)
cfa(configs,cbind(counts1,counts2))
# cfa rather than mcfa!