Description Usage Arguments Value Slots Warning Extends Creating Objects Methods Author(s) See Also Examples

The `sparse_mat`

class implements sparse matrices, potentially stored out-of-memory. Both compressed-sparse-column (CSC) and compressed-sparse-row (CSR) formats are supported. Non-zero elements are internally represented as key-value pairs.

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## Instance creation
sparse_mat(data, datamode = "double", nrow = 0, ncol = 0,
rowMaj = FALSE, dimnames = NULL, keys = NULL,
tolerance = c(abs=0), combiner = "identity",
chunksize = getOption("matter.default.chunksize"), ...)
# Check if an object is a sparse matrix
is.sparse(x)
# Coerce an object to a sparse matrix
as.sparse(x, ...)
## Additional methods documented below
``` |

`data` |
Either a length-2 'list' with elements 'keys' and 'values' which provide the halves of the key-value pairs of the non-zero elements, or a data matrix that will be used to initialized the sparse matrix. If a list is given, all 'keys' elements must be |

`datamode` |
A 'character' vector giving the storage mode of the data in virtual memory. Allowable values are R numeric and logical types ('logical', 'integer', 'numeric') and their C equivalents. |

`nrow` |
An optional number giving the total number of rows. |

`ncol` |
An optional number giving the total number of columns. |

`keys` |
Either NULL or a vector with length equal to the number of rows (for CSC matrices) or the number of columns (for CSR matrices). If NULL, then the 'key' portion of the key-value pairs that make up the non-zero elements are assumed to be row or column indices. If a vector, then they define the how the non-zero elements are matched to rows or columns. The 'key' portion of each non-zero element is matched against this canonical set of keys using binary search. Allowed types for keys are 'integer', 'numeric', and 'character'. |

`rowMaj` |
Whether the data should be stored using compressed-sparse-row (CSR) representation (as opposed to compressed-sparse-column (CSC) representation). Defaults to 'FALSE', for efficient access to columns. Set to 'TRUE' for more efficient access to rows instead. |

`dimnames` |
The names of the sparse matrix dimensions. |

`tolerance` |
For 'numeric' keys, the tolerance used for floating-point equality when determining key matches. The vector should be named. Use 'absolute' to use absolute differences, and 'relative' to use relative differences. |

`combiner` |
In the case of collisions when matching keys, how the row- or column-vectors should be combined. Acceptable values are "identity", "min", "max", "sum", and "mean". A user-specified function may also be provided. Using "identity" means collisions result in an error. Using "sum" or "mean" results in binning all matches. |

`chunksize` |
The (suggested) maximum number of elements which should be accessed at once by summary functions and linear algebra. Ignored when explicitly subsetting the dataset. |

`x` |
An object to check if it is a sparse matrix or coerce to a sparse matrix. |

`...` |
Additional arguments to be passed to constructor. |

An object of class `sparse_mat`

.

`data`

:A length-2 'list' with elements 'keys' and 'values' which provide the halves of the key-value pairs of the non-zero elements.

`datamode`

:The storage mode of the accessed data when read into R. This should a 'character' vector of length one with value 'integer' or 'numeric'.

`paths`

:A 'character' vector of the paths to the files where the data are stored.

`filemode`

:The read/write mode of the files where the data are stored. This should be 'r' for read-only access, or 'rw' for read/write access.

`chunksize`

:The maximum number of elements which should be loaded into memory at once. Used by methods implementing summary statistics and linear algebra. Ignored when explicitly subsetting the dataset.

`length`

:The length of the data.

`dim`

:Either 'NULL' for vectors, or an integer vector of length one of more giving the maximal indices in each dimension for matrices and arrays.

`names`

:The names of the data elements for vectors.

`dimnames`

:Either 'NULL' or the names for the dimensions. If not 'NULL', then this should be a list of character vectors of the length given by 'dim' for each dimension. This is always 'NULL' for vectors.

`ops`

:Delayed operations to be applied on atoms.

- keys
Either NULL or a vector with length equal to the number of rows (for CSC matrices) or the number of columns (for CSR matrices). If NULL, then the 'key' portion of the key-value pairs that make up the non-zero elements are assumed to be row or column indices. If a vector, then they define the how the non-zero elements are matched to rows or columns. The 'key' portion of each non-zero element is matched against this canonical set of keys using binary search. Allowed types for keys are 'integer', 'numeric', and 'character'.

`tolerance`

:For 'numeric' keys, the tolerance used for floating-point equality when determining key matches. An attribute 'type' gives whether 'absolute' or 'relative' differences should be used for the comparison.

`combiner`

:This is a function determining how the row- or column-vectors should be combined (or not) when key matching collisions occur.

If 'data' is given as a length-2 list of key-value pairs, no checking is performed on the validity of the key-value pairs, as this may be a costly operation if the list is stored in virtual memory. Each element of the 'keys' element must be *sorted* in increasing order, or behavior may be unexpected.

Assigning a new data element to the sparse matrix will always sort the key-value pairs of the row or column into which it was assigned.

`matter`

`sparse_mat`

instances can be created through `sparse_mat()`

.

Standard generic methods:

`x[i, j, ..., drop], x[i, j] <- value`

:Get or set the elements of the sparse matrix. Use

`drop = NULL`

to return a subset of the same class as the object.`cbind(x, ...), rbind(x, ...)`

:Combine sparse matrices by row or column.

`t(x)`

:Transpose a matrix. This is a quick operation which only changes metadata and does not touch the data representation.

Kylie A. Bemis

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