fCFA {cfa}R Documentation

Stepwise CFA approaches

Description

These CFA methods detect and eliminate stepwise types/antitypes cells by specifying an appropriate contrast in the design matrix. The procedures stop when model fit is achieved. Functional CFA (fCFA) uses a residual criterion, Kieser-Victor CFA (kvCFA) a LR-criterion.

Usage

fCFA(m.i, X, tabdim, alpha = 0.05)
kvCFA(m.i, X, tabdim, alpha = 0.05)

Arguments

m.i Vector of observed frequencies.
X Design Matrix of the base model.
tabdim Vector of table dimensions.
alpha Significance level.

Value

resstep Log-linear solution for each step: The first list element indicates the current iteration, the second the design matrix, the third the expected frequencies, the fourth is a vector composed of the deviance, Chi-squared-value, df, and p-value.
dev.val Deviance values for each step.
chisq.val Chi-squared values for each step.
df.val Degrees of freedom for each step.
p.val P-values for each step.
struMat Design vectors that blank out cells (strutural part of the model).

Author(s)

Patrick Mair, Alexander von Eye

References

von Eye, A., and Mair, P. (2007). A functional approach to configural frequency analysis. Austrian Journal of Statistics, in press.

Kieser, M., and Victor, N. (1999). Configural frequency analysis (CFA) revisited: A new look at an old approach. Biometrical Journal, 41, 967-983.

Examples


#Functional CFA for a internet terminal usage data set by Wurzer (An application of configural frequency analysis: Evaluation of the
#usage of internet terminals, 2005, p.82)
dd <- data.frame(a1=gl(3,4),b1=gl(2,2,12),c1=gl(2,1,12))
X <- model.matrix(~a1+b1+c1,dd,contrasts=list(a1="contr.sum",b1="contr.sum",c1="contr.sum"))
ofreq <- c(121,13,44,37,158,69,100,79,24,0,26,3)
tabdim <- c(3,2,2)

res1 <- fCFA(ofreq, X, tabdim=tabdim)
res1
summary(res1)

#Kieser-Vector CFA for Children's temperament data from von Eye (Configural Frequency Analysis, 2002, p. 192) 
dd <- data.frame(a1=gl(3,9),b1=gl(3,3,27),c1=gl(3,1,27))
X <- model.matrix(~a1+b1+c1,dd,contrasts=list(a1="contr.sum",b1="contr.sum",c1="contr.sum"))
ofreq <- c(3,2,4,23,23,6,39,33,9,11,29,13,19,36,19,21,26,18,13,30,41,12,14,23,8,6,7)
tabdim <- c(3,3,3)

res2 <- kvCFA(ofreq, X, tabdim=tabdim)
res2
summary(res2)

[Package cfa version 0.8-1 Index]