rankMatrix {Matrix} | R Documentation

## Rank of a Matrix

### Description

Compute ‘the’ matrix rank, a well-defined functional in theory(*), somewhat ambigous in practice. We provide several methods, the default corresponding to Matlab's definition.

(*) The rank of a n x m matrix A, rk(A) is the maximal number of linearly independent columns (or rows); hence rk(A) <= min(n,m).

### Usage

    rankMatrix(x, tol = NULL,
               method = c("tolNorm2", "qr.R", "qrLINPACK", "qr",
                          "useGrad", "maybeGrad"),
               sval = svd(x, 0, 0)$d, warn.t = TRUE)

### Arguments

`x` | numeric matrix, of dimension n x m, say.
`tol` | nonnegative number specifying a (relative, “scalefree”) tolerance for testing of “practically zero” with specific meaning depending on `method`; by default, `max(dim(x)) * .Machine$double.eps` is according to Matlab's default (for its only method which is our `method="tolNorm2"`).

`method`

a character string specifying the computational method for the rank, can be abbreviated:

`"tolNorm2"`:

  the number of singular values `>= tol * max(sval)`;

`"qrLINPACK"`:

  for a dense matrix, this is the rank of `qr(x, tol, LAPACK=FALSE)` (which is `qr(...)$rank`);
  This ("qr*", dense) version used to be the recommended way to compute a matrix rank for a while in the past.

  For sparse `x`, this is equivalent to `"qr.R"`.

`"qr.R"`:

  this is the rank of triangular matrix R, where `qr()` uses LAPACK or a "sparseQR" method (see `qr-methods`) to compute the decomposition QR. The rank of R is then defined as the number of “non-zero” diagonal entries d_i of R, and “non-zero”s fulfill |d_i| >= tol * max(|d_i|).

`"qr"`:

  is for back compatibility; for dense `x`, it corresponds to `"qrLINPACK"`, whereas for sparse `x`, it uses `"qr.R"`.

  For all the "qr*" methods, singular values `sval` are not used, which may be crucially important for a large sparse matrix `x`, as in that case, when `sval` is not specified, the default, computing `svd()` currently coerces `x` to a dense matrix.

`"useGrad"`:

  considering the “gradient” of the (decreasing) singular values, the index of the smallest gap.

`"maybeGrad"`:

  choosing method `"useGrad"` only when that seems reasonable; otherwise using `"tolNorm2"`.

`sval` | numeric vector of non-increasing singular values of `x`; typically unspecified and computed from `x` when needed, i.e., unless `method = "qr"`.
`warn.t` | logical indicating if `rankMatrix()` should warn when it needs `t(x)` instead of `x`. Currently, for `method = "qr"` only, gives a warning by default because the caller often could have passed `t(x)` directly, more efficiently.

### Value

If `x` is a matrix of all `0`, the rank is zero; otherwise, a positive integer in `1:min(dim(x))` with attributes detailing the method used.

### Note

For large sparse matrices `x`, unless you can specify `sval` yourself, currently `method = "qr"` may be the only feasible one, as the others need `sval` and call `svd()` which currently coerces `x` to a `denseMatrix` which may be very slow or impossible, depending on the matrix dimensions.

Note that in the case of sparse `x`, `method = "qr"`, all non-strictly zero diagonal entries d_i where counted, up to including Matrix version 1.1-0, i.e., that method implicitly used `tol = 0`, see also the seed(42) example below.

### Author(s)

Martin Maechler; for the "*Grad" methods, building on suggestions by Ravi Varadhan.

### See Also

`qr`, `svd`.

### Examples

    rankMatrix(cbind(1, 0, 1:3)) # 2

    (meths <- eval(formals(rankMatrix)$method))

    ## a "border" case:
    H12 <- Hilbert(12)
    rankMatrix(H12, tol = 1e-20) # 12;  but  11  with default method & tol.
    sapply(meths, function(.m.) rankMatrix(H12, method = .m.))
    ## tolNorm2    qr   qr.R  qrLINPACK  useGrad maybeGrad
    ##       11    12     11         12       11        11
    ## The meaning of 'tol' for method="qrLINPACK" and *dense* x is not entirely "scale free"
    rMQL <- function(ex, M) rankMatrix(M, method="qrLINPACK",tol = 10^-ex)
    rMQR <- function(ex, M) rankMatrix(M, method="qr.R",     tol = 10^-ex)
    sapply(5:15, rMQL, M = H12) # result is platform dependent
    ##  7  7  8 10 10 11 11 11 12 12 12  {x86_64}
    sapply(5:15, rMQL, M = 1000 * H12) # not identical unfortunately
    ##  7  7  8 10 11 11 12 12 12 12 12
    sapply(5:15, rMQR, M = H12)
    ##  5  6  7  8  8  9  9 10 10 11 11
    sapply(5:15, rMQR, M = 1000 * H12) # the *same*

    ## "sparse" case:
    M15 <- kronecker(diag(x=c(100,1,10)), Hilbert(5))
    sapply(meths, function(.m.) rankMatrix(M15, method = .m.))
    #--> all 15, but 'useGrad' has 14.

    ## "large" sparse
    n <- 250000; p <- 33; nnz <- 10000
    L <- sparseMatrix(i = sample.int(n, nnz, replace=TRUE),
                      j = sample.int(p, nnz, replace=TRUE), x = rnorm(nnz))
    (st1 <- system.time(r1 <- rankMatrix(L)))                # warning+ ~1.5 sec (2013)
    (st2 <- system.time(r2 <- rankMatrix(L, method = "qr"))) # considerably faster!
    r1[[1]] == print(r2[[1]]) ## -->  ( 33  TRUE )

    ## another sparse-"qr" one, which ``failed'' till 2013-11-23:
    set.seed(42)
    f1 <- factor(sample(50, 1000, replace=TRUE))
    f2 <- factor(sample(50, 1000, replace=TRUE))
    f3 <- factor(sample(50, 1000, replace=TRUE))
    rbind. <- if(getRversion() < "3.2.0") rBind else rbind
    D <- t(do.call(rbind., lapply(list(f1,f2,f3), as, 'sparseMatrix')))
    dim(D); nnzero(D) ## 1000 x 150 // 3000 non-zeros (= 2%)
    stopifnot(rankMatrix(D,           method='qr') == 148,
    	  rankMatrix(crossprod(D),method='qr') == 148)

    ## zero matrix has rank 0 :
    stopifnot(sapply(meths, function(.m.)
                            rankMatrix(matrix(0, 2, 2), method = .m.)) == 0)

[Package Matrix version 1.2-17 Index]