piščanc
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tole pravi spss (joj, po 4 letih sem ga odprla) diploma pa še čaakaaaa. no upam, da kaj pomaga
Nonlinear canonical correlation analysis corresponds to categorical canonical correlation analysis with optimal scaling. The purpose of this procedure is to determine how similar sets of categorical variables are to one another. Nonlinear canonical correlation analysis is also known by the acronym OVERALS.
Standard canonical correlation analysis is an extension of multiple regression, where the second set does not contain a single response variable, but multiple ones. The goal is to explain as much as possible of the variance in the relationships among two sets of numerical variables in a low dimensional space. Initially, the variables in each set are linearly combined such that the linear combinations have a maximal correlation. Given these combinations, subsequent linear combinations are determined that are uncorrelated with the previous combinations and that have the largest correlation possible.
The optimal scaling approach expands the standard analysis in three crucial ways. First, OVERALS allows more than two sets of variables. Second, variables can be scaled as either nominal, ordinal, or numerical. As a result, nonlinear relationships between variables can be analyzed. Finally, instead of maximizing correlations between the variable sets, the sets are compared to an unknown compromise set defined by the object scores.
Example. Categorical canonical correlation analysis with optimal scaling could be used to graphically display the relationship between one set of variables containing job category and years of education and another set of variables containing region of residence and gender. You might find that years of education and region of residence discriminate better than the remaining variables. You might also find that years of education discriminates best on the first dimension.
Statistics and plots. Frequencies, centroids, iteration history, object scores, category quantifications, weights, component loadings, single and multiple fit, object scores plots, category coordinates plots, component loadings plots, category centroids plots, transformation plots.
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