rsampler {RaschSampler} | 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 n rows and k columns. |
controls |
An object of class RSctr . If not specified, the default
parameters as returned by function rsctrl are
used. |
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 |
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 |
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 or two in the present application since the number of columns
must not exceed 64 (see rsctrl
).
The summary method (summary.RSmpl
) prints
information on the content of the output object.
Reinhold Hatzinger, Norman Verhelst
Verhelst, N. D. (2006) An efficient MCMC-algorithm to sample binary matrices with fixed marginals (under revision)
data(xmpl) ctr<-rsctrl(burn_in=10, n_eff=5, step=10, seed=0, tfixed=FALSE) res<-rsampler(xmpl,ctr) summary(res)