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

lossFinal value of the loss function
catscoresResulting category scores (after optimal scaling)
cormatCorrelation matrix based on the scores
cor.ratMatrix with correlation ratios
indmatIndicator matrix (dummy coded)
scorematTransformed data matrix (i.e with category scores resulting from optimal scaling)
burtmatBurt matrix
niterNumber 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, Volume 32, Issue 9..

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

Examples

Run this code

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

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