Multivariate Description. What Technique? Response variable(s)... Predictors(s) No Predictors(s) Yes... is one distribution summary regression models...

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Multivariate Description. What Technique? Response variable(s)... Predictors(s) No Predictors(s) Yes... is one distribution summary regression models...
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Presentation transcript:

Multivariate Description

What Technique? Response variable(s)... Predictors(s) No Predictors(s) Yes... is one distribution summary regression models... are many indirect gradient analysis (PCA, CA, DCA, MDS) cluster analysis direct gradient analysis constrained cluster analysis discriminant analysis (CVA)

Raw Data

Linear Regression

Two Regressions

Principal Components

Gulls Variables

Scree Plot

Output > gulls.pca2$loadings Loadings: Comp.1 Comp.2 Comp.3 Comp.4 Weight Wing Bill H.and.B > summary(gulls.pca2) Importance of components: Comp.1 Comp.2 Comp.3 Standard deviation Proportion of Variance Cumulative Proportion

Bi-Plot

Male or Female?

Linear Discriminant > gulls.lda <- lda(Sex ~ Wing + Weight + H.and.B + Bill, gulls) lda(Sex ~ Wing + Weight + H.and.B + Bill, data = gulls) Prior probabilities of groups: Group means: Wing Weight H.and.B Bill Coefficients of linear discriminants: LD1 Wing Weight H.and.B Bill

Discriminating

Relationship between PCA and LDA

CVA

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