Hierachical analysis of configuration frequencies
Recursively eliminates one variable in the configuration to generate all possible sub-tables and performs a global chi-squared-test on them
hcfa(configs, cnts)
configs |
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. If it is set to NA, a count of one is assumed for every
row. This allows untabulated data to be processed. |
The hierarchical CFA assists in the selection of configuration variables by showing which variables contribute the most to the variability. If eliminating a variable does not markedly decrease the global chi squared the variable is likely to be redundant, provided there are no extraneous reasons for retaining it.
The output is in decreasing order of chi squared so the most useful combinations of variables come first.
chisq |
Global chi squared |
df |
Degrees of freedom for this subtable |
order |
Order (number of configuration variables) |
The p for the test of significance ist provided by the print method
Stefan Funke <s.funke@t-online.de>
Lautsch, E., von Weber S. (1995) Methoden und Anwendungen der Konfigurationsfrequenzanalyse in Psychologie und Medizin, Beltz Psychologie Verlagsunion
# 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])
counts<-trunc(runif(250)*10)
hcfa(configs,counts)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.