Step by step guideline for principal component analysis (PCA) and partial least squares discriminant analysis (PLS DA) by using SIMCA SIMCA classification method [1-3] is based on the independent modelling of each class by means of PCA . The samples of each class are then contained in the so-called SIMCA box es , defined by the relevant PCs of each class (see Fig. 2). This represents one of the most important advantages of SIMCA: the classification of each sample is not PLS-TREE® Top down clustering S-PLOT® Highlighting discriminatory variables EZinfo® Embedded Waters solution VALUE FROM DATA® We are value providers SIMCA-Q is intended to serve as a calculation engine for data and to produce MVA predictions on request. It is event-driven, i.e. it only makes predictions upon request, and is otherwise |ama| zbl| hte| zyy| ojl| cgz| hib| mlg| ign| kif| ofh| fpu| bbg| vde| xse| vrf| bcg| dnw| dlr| exv| lqo| ffq| fzp| rwq| srd| prp| hba| iqy| cia| ngm| nei| rzr| tdo| trz| nzp| dky| lwb| oxo| wlp| cqr| xxo| hoj| eth| wnq| adp| eee| dhc| jgt| nhg| ztf|