burn_in: the number of sampled matrices to come close to a stationary distribution. The default is burn_in = 100. (The actual number is 2 * burn_in * step.)
n_eff: the number of effective matrices, i.e., the number of matrices to be generated by the sampling function rsampler. n_eff must be positive and not larger than 8191 ((213)−1). The default is n_eff = 100.
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 step = 16.
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 rsampler
(dependent on the system's clock) and its value is returned in the output. If seed is not equal to zero, its value is used as the seed of the random number generator. In that case its value is unaltered at output. The default is seed = 0.
tfixed: logical, -- specifies if in case of a quadratic input matrix the diagonal is considered fixed (see note below). The default is tfixed = FALSE.
Returns
A list of class RSctr with components burn_in, n_eff, step, seed, tfixed.,
Note
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.
See Also
rsampler
Examples
ctr <- rsctrl(n_eff =1, seed =987654321)# specify new controlssummary(ctr)## Not run:# incorrect specifications will lead to an errorctr2 <- rsctrl(step =-3, n_eff =10000)## End(Not run)