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Returns reduces data after PCA with desired percent of variance retained.
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| pca_var <- function(X, percent) { | |
| #Normalize data before start | |
| X = scale(X) | |
| #Covariance matrix | |
| Sigma = (1 / nrow(X)) * t(X) %*% X | |
| #Singular value decomposition of covariance matrix | |
| Sigma_svd <- svd(Sigma) | |
| #Get minimum number of vectors to retain desired percent of variation | |
| k = which(cumsum(Sigma_svd$d)/sum(Sigma_svd$d) >= percent)[1] | |
| #Take this number of vectors | |
| Ureduce <- Sigma_svd$u[, 1:k] | |
| #Compute reduced version of data | |
| Z = X %*% Ureduce | |
| return(Z) | |
| } |
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