rpca {pbdML} | R Documentation |
Random PCA
rpca(x, k = 1, q = 3, retx = TRUE, center = TRUE, scale = FALSE)
x |
The input data matrix. |
k |
The number of singular values and/or left/right singular vectors to estimate. |
q |
An integer exponent, say 1, 2, or 3. See the paper for details. |
retx |
Logical; determines if the rotated data should be returned. |
center, scale |
Logical; determines if the data should be centered/scaled first. |
An object of class prcomp
.
Drew Schmidt
Halko, Martinsson, and Tropp. 2011. Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Review 53 217-288.
Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification, chapter 10. John Wiley & Sons.
## Not run: x <- matrix(rnorm(30), 10) rpca(x) ## End(Not run)