boot.cfa {cfa}R Documentation

Bootstrap CFA

Description

Check if the configurations detected are stable. This is done by counting the configurations found to be significant in the CFAs performed.

Usage

boot.cfa(configmatrix, cntvector, runs=100, bonferroni=F, sig.limit=0.05)

Arguments

configmatrix Dataframe with the variables to be analyzed
cntvector Vector of counts (1 if the data are not aggregated
runs Number of runs and therefore samples to be generated.
bonferroni Use Bonferroni-adjustment in determining if the configuration is considered significant
sig.limit Limit of p which cause the configuration to be counted as "signifcant"

Details

The repeated use of tests of significance is obviously problematic. The result is therfore to be interpreted in a strictly heuristic way.

Value

Row names Configuration
cnt.antitype Number of cases where n<expected
cnt.type Number of cases where n>expected
pct.types Percentage of cases where n>expected
cnt.sig Number of cases where the p(z) for the configuration was considered significant
pct.cnt.sig Percentage of cases where the p(z) for the configuration was considered significant

WARNING

The program is implemented in R itself rather than a compiled library and therefore slow. It repeatedly calls cfa. In addition the number of runs must be high to give a reliable result. The default of 100 is the absolute mininum. So a run of the program under realistic conditions may take from minutes to hours.

Author(s)

Stefan Funke <funke@attglobal.net>

References

Lautsch E., von Weber S. (1995) Methoden und Anwendungen der Konfigurationsfrequenzanalyse (KFA) Beltz Verlagsunion (1995)

See Also

cfa, hier.cfa

Examples

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