In least squares regression, leverages are values that indicate how far the corresponding observations are from the center of the x-space, which is described by the x-values. In PLS, the predictors are replaced by x-scores. Observations with high leverage have x-scores far from zero and have a significant influence on the regression coefficients. Points with high leverage are outliers in the x-space, but are not necessarily outliers in the y-space.
The leverage values in PLS are calculated from the x-score matrix T, which is used to calculate the hat matrix (H) as follows:
The leverage (hii) of the ith observation is the ith diagonal element of the H matrix.
A leverage value greater than 2m / n is considered high and should be examined.
|n||the number of observations|
|m||the number of components|