aspect (version 1.0-6)

lineals: Linearizing bivariate regressions

Description

This function performs optimal scaling in order to achieve linearizing transformations for each bivariate regression.

Usage

lineals(data, level = "nominal", itmax = 100, eps = 1e-06)

Arguments

data

Data frame or matrix

level

Vector with scale level of the variables ("nominal" or "ordinal"). If all variables have the same scale level, only one value can be provided

itmax

Maximum number of iterations

eps

Convergence criterion

Value

loss

Final value of the loss function

catscores

Resulting category scores (after optimal scaling)

cormat

Correlation matrix based on the scores

cor.rat

Matrix with correlation ratios

indmat

Indicator matrix (dummy coded)

scoremat

Transformed data matrix (i.e with category scores resulting from optimal scaling)

burtmat

Burt matrix

niter

Number of iterations

Details

This function can be used as a preprocessing tool for categorical and ordinal data for subsequent factor analytical techniques such as structural equation models (SEM) using the resulting correlation matrix based on the transformed data. The estimates of the corresponding structural parameters are consistent if all bivariate regressions can be linearized.

References

Mair, P., & De Leeuw, J. (2008). Scaling variables by optimizing correlational and non-correlational aspects in R. Journal of Statistical Software, 32(9), 1-23. 10.18637/jss.v032.i09

de Leeuw, J. (1988). Multivariate analysis with linearizable regressions. Psychometrika, 53, 437-454.

See Also

corAspect

Examples

# NOT RUN {
data(galo)
res.lin <- lineals(galo)
summary(res.lin)
# }