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Motivation ANOVA Simultaneous Component Analysis is a common tool for the analysis of multivariate results produced by designed experiments. Meaningful relationships between the experimental design and measured variables in the dataset are often established by significance testing, with perforation tests being the preferred go-to method. We show that VASCA is more effective than both ASCA and the widely used false discovery rate control system; the former is used as a benchmark for random selection based on multiple significance tests. In comparison to the common partial least squares discriminant analysis technique and its sparse counterpart, we'll continue to demonstrate the use of VASCA for exploratory data analysis. We'll continue to illustrate the effectiveness of VASCA for exploratory data analysis. Avatar and implementation The code for VASCA is available in the MEDA Toolbox at https://github. com/josecamachop/MEDA-Toolbox.
Source link: https://europepmc.org/article/MED/36495189
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