Abstract
Homogeneity analysis combines the idea of maximizing the correlations between variables of a multivariate data set with that of optimal scaling. In this article we present
methodological and practical issues of the R package homals which performs homogeneity analysis and various extensions. By setting rank constraints nonlinear principal component analysis can be performed. The variables can be partitioned into sets such that homogeneity analysis is extended to nonlinear canonical correlation analysis or to predictive
models which emulate discriminant analysis and regression models. For each model the scale level of the variables can be taken into account by setting level constraints. All
algorithms allow for missing values.
methodological and practical issues of the R package homals which performs homogeneity analysis and various extensions. By setting rank constraints nonlinear principal component analysis can be performed. The variables can be partitioned into sets such that homogeneity analysis is extended to nonlinear canonical correlation analysis or to predictive
models which emulate discriminant analysis and regression models. For each model the scale level of the variables can be taken into account by setting level constraints. All
algorithms allow for missing values.
| Original language | English |
|---|---|
| Pages (from-to) | 1 - 21 |
| Journal | Journal of Statistical Software |
| Volume | 31 |
| Issue number | 4 |
| Publication status | Published - 1 Oct 2009 |