peav: Percentage Explained Additional Variance
In nsprcomp: Non-Negative and Sparse PCA

Description Usage Arguments Details Note See Also

peav computes the percentage of the explained _additional_ variance of each principal component, taking into account the possible non-orthogonality of the pseudo-rotation matrix W.

1	peav(x, w, center = TRUE, scale. = FALSE)

`x`	a numeric data matrix with the observations as rows
`w`	a numeric data matrix with the principal axes as columns
`center`	a logical value indicating whether the empirical mean of `x` should be subtracted. Alternatively, a vector of length equal to the number of columns of `x` can be supplied. The value is passed to `scale`.
`scale.`	a logical value indicating whether the columns of `x` should be scaled to have unit variance before the analysis takes place. The default is `FALSE` for consistency with `prcomp`. Alternatively, a vector of length equal to the number of columns of `x` can be supplied. The value is passed to `scale`.

The explained additional variance is computed using asdev and divided by the total variance of the data to obtain percentages. sum(peav(x, w)) is equal to one if W is an orthonormal basis, e.g. the rotation matrix of a standard PCA.

peav is useful to compare the solutions of various constrained PCA methods w.r.t. standard PCA.

The method produces different results than the "percentage explained variance" (pev) computed by the spca function from the elasticnet package.

asdev, scale

nsprcomp documentation built on May 1, 2019, 7:56 p.m.