Various parameters that control aspects of the random generation of binary matrices.

1 |

`burn_in` |
the number of sampled matrices to
come close to a stationary distribution.
The default is |

`n_eff` |
the number of effective matrices, i.e., the number of matrices to be generated by the sampling function |

`step` |
controls the number number of void matrices generated in the the burn in
process and when effective matrices are generated (see note
below). The default is |

`seed` |
is the indicator for the seed of the random number generator.
Its value must be in the range 0 and 2147483646 (2**31-2).
If the value of seed equals zero, a seed is generated
by the sampling function |

`tfixed` |
logical, – specifies if in case of a quadratic input
matrix the diagonal is considered fixed (see note below).
The default is |

A list of class `RSctr`

with components
`burn_in`

, `n_eff`

, `step`

,
`seed`

, `tfixed`

.,

If one of the components is incorrectly specified
the error function `rserror`

is called and some informations are printed. The ouput object
will not be defined.

The specification of `step`

controls the sampling algorithm as follows:
If , e.g., `burn_in = 10`

, `n_eff = 5`

, and `step = 2`

,
then during the burn in period `step * burn_in = 2 * 10`

matrices are generated. After that, `n_eff * step = 5 * 2`

matrices
are generated and every second matrix of these last ten is returned from
`link{rsampler}`

.

`tfixed`

has no effect if the input matrix is not quadratic,
i.e., all matrix elements are considered free (unrestricted).
If the input matrix is quadratic, and `tfixed = TRUE`

,
the main diagonal of the matrix is considered as fixed.
On return from `link{rsampler}`

all diagonal elements
of the generated matrices are set to zero.
This specification applies, e.g.,
to analyzing square incidence matrices
representing binary asymmetric relation
in social network theory.

The summary method (`summary.RSctr`

) prints
the current definitions.

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