rsampler {eRm}  R Documentation 
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.
rsampler(inpmat, controls = rsctrl())
inpmat 
A binary (data) matrix with 
controls 
An object of class 
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
.
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 

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 
seed 
starting value for the random number generator 
n_tot 
number of matrices in 
outvec 
vector of encoded random matrices 
ier 
error code 
An element of outvec
is a four byte (or 32 bits) integer.
The matrices to be output are stored bitwise (some bits are unused, since a integer is used for every row of a matrix).
So the number of integers per row needed equals (k+31)/32
(integer division), which is one to four in the present implementation since the number of columns and rows must not exceed 128 and 4096, respectively.
The summary method (summary.RSmpl
) prints information on the content of the output object.
Reinhold Hatzinger, Norman Verhelst
Verhelst, N. D. (2008). An Efficient MCMC Algorithm to Sample Binary Matrices with Fixed Marginals. Psychometrika, 73 (4)
data(xmpl)
ctr<rsctrl(burn_in=10, n_eff=5, step=10, seed=0, tfixed=FALSE)
res<rsampler(xmpl,ctr)
summary(res)