sparseMatrix-class {Matrix} | R Documentation

## Virtual Class "sparseMatrix" — Mother of Sparse Matrices

### Description

Virtual Mother Class of All Sparse Matrices

### Slots

`Dim`:
  Object of class `"integer"` - the dimensions of the matrix - must be an integer vector with exactly two non-negative values.
`Dimnames`:
  a list of length two - inherited from class `Matrix`, see `Matrix`.

### Extends

Class `"Matrix"`, directly.

### Methods

show
  `(object = "sparseMatrix")`: The `show` method for sparse matrices prints “structural” zeroes as `"."` using `printSpMatrix()` which allows further customization.
print
  `signature(x = "sparseMatrix")`, ....
  The `print` method for sparse matrices by default is the same as `show()` but can be called with extra optional arguments, see `printSpMatrix()`.
summary
  `(object = "sparseMatrix")`: Returns an object of S3 class `"sparseSummary"` which is basically a `data.frame` with columns `(i,j,x)` (or just `(i,j)` for `nsparseMatrix` class objects) with the stored (typically non-zero) entries. The `print` method resembles Matlab's way of printing sparse matrices, and also the MatrixMarket format, see `writeMM`.
determinant
  `(x = "sparseMatrix", logarithm=TRUE)`: `determinant()` methods for sparse matrices typically work via `Cholesky` or `lu` decompositions.
diag
  `(x = "sparseMatrix")`: extracts the diagonal of a sparse matrix.
dim<-
  `signature(x = "sparseMatrix", value = "ANY")`: allows to reshape a sparse matrix to a sparse matrix with the same entries but different dimensions. `value` must be of length two and fulfill `prod(value) == prod(dim(x))`.
coerce
  `signature(from = "factor", to = "sparseMatrix")`: Coercion of a factor to `"sparseMatrix"` produces the matrix of indicator rows stored as an object of class `"dgCMatrix"`. To obtain columns representing the interaction of the factor and a numeric covariate, replace the `"x"` slot of the result by the numeric covariate then take the transpose. Missing values (`NA`) from the factor are translated to columns of all `0`s.

See also `colSums`, `norm`, ... for methods with separate help pages.

### Note

In method selection for multiplication operations (i.e. `%*%` and the two-argument form of `crossprod`) the sparseMatrix class takes precedence in the sense that if one operand is a sparse matrix and the other is any type of dense matrix then the dense matrix is coerced to a `dgeMatrix` and the appropriate sparse matrix method is used.

### Examples

    showClass("sparseMatrix") ## and look at the help() of its subclasses
    M <- Matrix(0, 10000, 100)
    M[1,1] <- M[2,3] <- 3.14
    M  ## show(.) method suppresses printing of the majority of rows

    data(CAex); dim(CAex) # 72 x 72 matrix
    determinant(CAex) # works via sparse lu(.)

    ## factor -> t( <sparse design matrix> ) :
    (fact <- gl(5, 3, 30, labels = LETTERS[1:5]))
    (Xt <- as(fact, "sparseMatrix"))  # indicator rows

    ## missing values --> all-0 columns:
    f.mis <- fact
    i.mis <- c(3:5, 17)
    is.na(f.mis) <- i.mis
    Xt != (X. <- as(f.mis, "sparseMatrix")) # differ only in columns 3:5,17
    stopifnot(all(X.[,i.mis] == 0), all(Xt[,-i.mis] == X.[,-i.mis]))

[Package Matrix version 0.999375-33 Index]