Methods for dealing with sparse geometry binary predicate lists

```
# S3 method for sgbp
print(x, ..., n = 10, max_nb = 10)
```# S3 method for sgbp
t(x)

# S3 method for sgbp
as.matrix(x, ...)

# S3 method for sgbp
dim(x)

# S3 method for sgbp
Ops(e1, e2)

# S3 method for sgbp
as.data.frame(x, ...)

- x
object of class

`sgbp`

- ...
ignored

- n
integer; maximum number of items to print

- max_nb
integer; maximum number of neighbours to print for each item

- e1
object of class

`sgbp`

- e2
object of class

`sgbp`

`sgbp`

are sparse matrices, stored as a list with integer vectors holding the ordered `TRUE`

indices of each row. This means that for a dense, \(m \times n\) matrix `Q`

and a list `L`

, if `Q[i,j]`

is `TRUE`

then \(j\) is an element of `L[[i]]`

. Reversed: when \(k\) is the value of `L[[i]][j]`

, then `Q[i,k]`

is `TRUE`

.

`==`

compares only the dimension and index values, not the attributes of two `sgbp`

object; use `identical`

to check for equality of everything.