# big_randomSVD: Randomized partial SVD In bigstatsr: Statistical Tools for Filebacked Big Matrices

## Description

An algorithm for partial SVD (or PCA) of a Filebacked Big Matrix based on the algorithm in RSpectra (by Yixuan Qiu and Jiali Mei).
This algorithm is linear in time in all dimensions and is very memory-efficient. Thus, it can be used on very large big.matrices.

## Usage

  1 2 3 4 5 6 7 8 9 10 11 12 big_randomSVD( X, fun.scaling = big_scale(center = FALSE, scale = FALSE), ind.row = rows_along(X), ind.col = cols_along(X), k = 10, tol = 1e-04, verbose = FALSE, ncores = 1, fun.prod = big_prodVec, fun.cprod = big_cprodVec ) 

## Arguments

 X An object of class FBM. fun.scaling A function that returns a named list of mean and sd for every column, to scale each of their elements such as followed: \frac{X_{i,j} - mean_j}{sd_j}. Default doesn't use any scaling. ind.row An optional vector of the row indices that are used. If not specified, all rows are used. Don't use negative indices. ind.col An optional vector of the column indices that are used. If not specified, all columns are used. Don't use negative indices. k Number of singular vectors/values to compute. Default is 10. This algorithm should be used to compute only a few singular vectors/values. tol Precision parameter of svds. Default is 1e-4. verbose Should some progress be printed? Default is FALSE. ncores Number of cores used. Default doesn't use parallelism. You may use nb_cores. fun.prod Function that takes 6 arguments (in this order): a matrix-like object X, a vector x, a vector of row indices ind.row of X, a vector of column indices ind.col of X, a vector of column centers (corresponding to ind.col), a vector of column scales (corresponding to ind.col), and compute the product of X (subsetted and scaled) with x. fun.cprod Same as fun.prod, but for the transpose of X.

## Value

A named list (an S3 class "big_SVD") of

• d, the singular values,

• u, the left singular vectors,

• v, the right singular vectors,

• niter, the number of the iteration of the algorithm,

• nops, number of Matrix-Vector multiplications used,

• center, the centering vector,

• scale, the scaling vector.

Note that to obtain the Principal Components, you must use predict on the result. See examples.

## Note

The idea of using this Implicitly Restarted Arnoldi Method algorithm comes from G. Abraham, Y. Qiu, and M. Inouye, FlashPCA2: principal component analysis of biobank-scale genotype datasets, bioRxiv: doi: 10.1101/094714.
It proved to be faster than our implementation of the "blanczos" algorithm in Rokhlin, V., Szlam, A., & Tygert, M. (2010). A Randomized Algorithm for Principal Component Analysis. SIAM Journal on Matrix Analysis and Applications, 31(3), 1100-1124. doi: 10.1137/080736417.

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 set.seed(1) X <- big_attachExtdata() K <- 10 # Using only half of the data for "training" n <- nrow(X) ind <- sort(sample(n, n/2)) test <- big_randomSVD(X, fun.scaling = big_scale(), ind.row = ind, k = K) str(test) pca <- prcomp(X[ind, ], center = TRUE, scale. = TRUE) # same scaling all.equal(test$center, pca$center) all.equal(test$scale, pca$scale) # use this function to predict scores class(test) scores <- predict(test) # scores and loadings are the same or opposite plot(scores, pca$x[, 1:K]) plot(test$v, pca$rotation[, 1:K]) plot(test$u) plot(test, type = "scores") # projecting on new data ind2 <- setdiff(rows_along(X), ind) scores.test2 <- predict(test, X, ind.row = ind2) scores.test3 <- predict(pca, X[-ind, ]) plot(scores.test2, scores.test3[, 1:K])