Fit a statistical factor model using Principal Component Analysis (PCA)
statistical.factor.model(R, k = 1, ...)#'
N x k matrix of factor loadings (i.e. betas)
m x k matrix of factor realizations
m x N matrix of model residuals representing idiosyncratic risk factors
Where N is the number of assets, k is the number of factors, and m is the number of observations.
xts of asset returns
number of factors to use
additional arguments passed to prcomp
The statistical factor model is fitted using prcomp. The factor
loadings, factor realizations, and residuals are computed and returned
given the number of factors used for the model.