cfa (version 0.10-1)

bcfa: Bootstrap-CFA

Description

The bootstrap-CFA tries to replicate the pattern of significant configurations by re-sampling.

Usage

bcfa(configs, cnts, runs=100, sig.item="sig.z",...)

Value

cnt.antitype

Number of antiypes

cnt.type

Number of types

pct.types

Number of types in percent

cnt.sig

Number of significant results

pct.cnt.sig

Number of significant results in percent

Arguments

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. cnts must be a vector.

runs

Number of samples to be drawn.

sig.item

Indicator of significance in the result table (sig.z,sig.chisq,sig.perli,sig.zl, sig.zl.corr). Do not forget to set the proper parameters for the CFA if sig.perli,sig.zl or sig.zl.corr are to be used!

...

Parameters to be to relayed to the CFA

Author

Stefan Funke <[e-mail]>

Details

Takes 'runs' samples and does as many CFAs while counting how many times this configuration was considered to be significant.

Repeated-measures CFAs (mcfa) are not provided.

This is a heuristic method rather than a strict test of significance since there is no adjustment for multiple testing whatsoever. The advantage is a more reliable picture compared to splitting the original data, doing a CFA, and checking if the configurations re-appear in a CFA with the other half of the data.

References

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

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])
counts<-trunc(runif(250)*10)
bcfa(configs,counts,runs=25)