lineals: Linearizing bivariate regressions

In aspect: A General Framework for Multivariate Analysis with Optimal Scaling

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

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.

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

Author(s)

Jan de Leeuw, Patrick Mair

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. doi: 10.18637/jss.v032.i09

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

See Also

corAspect

Examples

data(galo)
res.lin <- lineals(galo)
summary(res.lin)

aspect documentation built on May 6, 2022, 9:06 a.m.