Correspondence Analysis. Abdi & Béra
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چکیده
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منابع مشابه
Multiple Correspondence Analysis
Multiple correspondence analysis (MCA) is an extension of correspondence analysis (CA) which allows one to analyze the pattern of relationships of several categorical dependent variables. As such, it can also be seen as a generalization of principal component analysis when the variables to be analyzed are categorical instead of quantitative. Because MCA has been (re)discovered many times, equiv...
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We present an extension of PLS—called partial least squares correspondence analysis (PLSCA)—tailored for the analysis of nominal data. As the name indicates, PLSCA combines features of PLS (analyzing the information common to two tables) and correspondence analysis (CA, analyzing nominal data). We also present inferential techniques for PLSCA such as bootstrap, permutation, and χ2 omnibus tests...
متن کاملAn ExPosition of multivariate analysis with the singular value decomposition in R
ExPosition is a new comprehensive R package providing crisp graphics and implementing multivariate analysis methods based on the singular value decomposition (svd). The core techniques implemented in ExPosition are: principal components analysis, (metric) multidimensional scaling, correspondence analysis, and several of their recent extensions such as barycentric discriminant analyses (e.g., di...
متن کاملDiscriminant Correspondence Analysis
As thename indicates, discriminant correspondence analysis (DCA) is an extension of discriminant analysis (DA) and correspondence analysis (CA). Like discriminant analysis, the goal of DCA is to categorize observations in pre-defined groups, and like correspondence analysis, it is used with nominal variables. The main idea behind DCA is to represent each group by the sum of its observations and...
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تاریخ انتشار 2014