As I see, there is no way of scaling the biplot axes with the variance of the components as in e.g. R biplot.prcomp (see here). This is really important, since this way, the features length and the angles between them relates to the variance and covariance of the variables, and this way, so much information became visible on the biplot.
E.g. in iris dataset, 'sepal length' and 'petal length' are correlated (0.87), however, they are almost orthogonal in the biplot provided by the pca library. Also, scaling the observations make it possible to map the observations onto the variables, giving qualitive and quantitive insight into the PCA.
As I see, there is no way of scaling the biplot axes with the variance of the components as in e.g. R biplot.prcomp (see here). This is really important, since this way, the features length and the angles between them relates to the variance and covariance of the variables, and this way, so much information became visible on the biplot.
E.g. in iris dataset, 'sepal length' and 'petal length' are correlated (0.87), however, they are almost orthogonal in the biplot provided by the
pcalibrary. Also, scaling the observations make it possible to map the observations onto the variables, giving qualitive and quantitive insight into the PCA.