RaschSampler (version 0.8.0)

rsampler: Sampling Binary Matrices

Description

The function implements an MCMC algorithm for sampling of binary matrices with fixed margins complying to the Rasch model. Its stationary distribution is uniform. The algorithm also allows for square matrices with fixed diagonal.

Usage

rsampler(inpmat, controls = rsctrl())

Arguments

inpmat

A binary (data) matrix with $n$ rows and $k$ columns.

controls

An object of class RSctr. If not specified, the default parameters as returned by function rsctrl are used.

Value

• A list of class RSmpl with components
• n
  number of rows of the input matrix
• k
  number of columns of the input matrix
• inpmat
  the input matrix
• tfixed
  TRUE, if diagonals of inpmat are fixed
• burn_in
  length of the burn in process
• n_eff
  number of generated matrices (effective matrices)
• step
  controls the number number of void matrices generated in the the burn in process and when effective matrices are generated (see note in rsctrl).
• seed
  starting value for the random number generator
• n_tot
  number of matrices in outvec, n_tot = n_eff + 1
• outvec
  vector of encoded random matrices
• ier
  error code

Details

rsampler is a wrapper function for a Fortran routine to generate binary random matrices based on an input matrix. On output the generated binary matrices are integer encoded. For further processing of the generated matrices use the function rstats.

References

Verhelst, N. D. (2006) An efficient MCMC-algorithm to sample binary matrices with fixed marginals (under revision)

See Also

rsctrl, rstats

Examples

data(xmpl)
ctr<-rsctrl(burn_in=10, n_eff=5, step=10, seed=0, tfixed=FALSE)
res<-rsampler(xmpl,ctr)
summary(res)