![]() Default is 0.99.Ī numeric value between 0 and 1 indicating the quantile to be used as critical value for outlier detection. Grouping variable: a factor specifying the class for each observation.Ī numeric value between 0 and 1 indicating how much variance should be covered by the robust PCs. The default is na.omit.Īrguments passed to or from other methods. ame) containing the variables in theĪn optional vector used to select rows (observations) of theĪ function which indicates what should happen , subset, na.action )Ī formula with no response variable, referring only to ![]() ) # S3 method for class 'formula' OutlierSign2 ( formula, data. ) # Default S3 method: OutlierSign2 ( x, grouping, qcrit = 0.975, explvar = 0.99, trace = FALSE. ![]() SummarySosDisc-class: Class '"SummarySosDisc"' - summary of '"SosDisc"' objects.SummarySimca-class: Class '"SummarySimca"' - summary of '"Simca"' objects.SPcaGrid-class: Class 'SPcaGrid' - Sparse Robust PCA using PP - GRID search.SPcaGrid: Sparse Robust Principal Components based on Projection.SosDiscRobust-class: Class 'SosDiscRobust' - robust and sparse multigroup.SosDiscRobust: Robust and sparse multigroup classification by the optimal.SosDiscClassic-class: Class 'SosDiscClassic' - sparse multigroup classification by.SosDisc-class: Class '"SosDisc"' - virtual base class for all classic and.Simca-class: Class '"Simca"' - virtual base class for all classic and.RSimca-class: Class '"RSimca" - robust classification in high dimensions.RSimca: Robust classification in high dimensions based on the SIMCA.rcpp_hello_world: Simple function using Rcpp.PredictSosDisc-class: Class '"PredictSosDisc"' - prediction of '"SosDisc"' objects.PredictSimca-class: Class '"PredictSimca"' - prediction of '"Simca"' objects.OutlierSign2-class: Class '"OutlierSign2"' - Outlier identification in high.OutlierSign2: Outlier identification in high dimensions using the SIGN2.OutlierSign1-class: Class '"OutlierSign1"' - Outlier identification in high.OutlierSign1: Outlier identification in high dimensions using the SIGN1.OutlierPCOut-class: Class '"OutlierPCOut"' - Outlier identification in high.OutlierPCOut: Outlier identification in high dimensions using the PCOUT.OutlierPCDist-class: Class '"OutlierPCDist"' - Outlier identification in high.OutlierPCDist: Outlier identification in high dimensions using the PCDIST.OutlierMahdist-class: Class 'OutlierMahdist' - Outlier identification using robust.OutlierMahdist: Outlier identification using robust (mahalanobis) distances.Outlier-class: Class '"Outlier"' - a base class for outlier identification.getWeight-methods: Accessor methods to the essential slots of 'Outlier' and its.CSimca-class: Class '"CSimca"' - classification in high dimensions based on.CSimca: Classification in high dimensions based on the (classical).Cars: Consumer reports car data: dimensions.
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