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"Hotelling's T^2 Ellipse", "topics": [ "simpleEllipse" ] }, { "page": "slplot-pcaRes-method", "title": "Side by side scores and loadings plot", "topics": [ "slplot", "slplot,pcaRes-method" ] }, { "page": "sortFeatures", "title": "Sort the features of NLPCA object", "topics": [ "sortFeatures" ] }, { "page": "summary", "title": "Summary of PCA model", "topics": [ "summary", "summary,pcaRes-method", "summary.pcaRes" ] }, { "page": "svdImpute", "title": "SVDimpute algorithm", "topics": [ "svdImpute" ] }, { "page": "svdPca", "title": "Perform principal component analysis using singular value decomposition", "topics": [ "svdPca" ] }, { "page": "tempFixNas", "title": "Temporary fix for missing values", "topics": [ "tempFixNas" ] }, { "page": "vector2matrices-matrix-method", "title": "Tranform the vectors of weights to matrix structure", "topics": [ "vector2matrices,matrix-method" ] }, { "page": "vector2matrices-nlpcaNet-method", "title": "Tranform the vectors of weights to matrix structure", "topics": [ "vector2matrices,nlpcaNet-method" ] }, { "page": "wasna-pcaRes-method", "title": "Get a matrix with indicating the elements that were missing in the input data. Convenient for estimating imputation performance.", "topics": [ "wasna", "wasna,pcaRes-method" ] }, { "page": "weightsAccount", "title": "Create an object that holds the weights for nlpcaNet. 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