#ADD TAG TO XLINE STATA PC#

This corresponds to the eigenvector associated to the second largest eigenvalue And so on … Introduction Robust Covariance Matrix Robust PCA Application ConclusionĬlassical PCA The new variables (PCs) have a variance equal to theircorrespondingeigenvalue Var(Yi)= i for all i=1…p The relative variance explained by each PC isgiven by i / i Introduction Robust Covariance Matrix Robust PCA Application Conclusion Principal component analysis Hence, for the first principal component, the goal is to find a linear transformation Y=1 X1+2 X2+.+ p Xp (= TX)such that tha variance of Y (=Var(TX) =T  ) is maximal The direction of d is given by the eigenvector correponding to the largest eigenvalue of matrix Σ Introduction Robust Covariance Matrix Robust PCA Application ConclusionĬlassical PCA The second vector (orthogonal to the first), is the one that has the second highest variance. Principal component analysis X2 Introduction Robust Covariance Matrix Robust PCA Application Conclusion X1 Introduction Robust Covariance Matrix Robust PCA Application Conclusion the goal of PCA is to construct a new set of p axes in the directions of greatest variability.

Principal component analysis PCA, transforms a set of correlated variables into a smaller set of uncorrelated variables (principal components). Robust PCA in Stata Vincenzo Verardi FUNDP (Namur) and ULB (Brussels), Belgium FNRS AssociateResearcher

Add tag to xline stata