rmvbin {bindata} | R Documentation |
Creates correlated multivariate binary random variables by
thresholding a normal distribution. The correlations of the components
can be specified either as common probabilities, correlation matrix of the
binary distribution, or covariance matrix of the normal
distribution. Hence, only one of the arguments commonprob
,
bincorr
and sigma
may be specified. Default are
uncorrelated components.
n
samples from a multivariate normal distribution with mean and
variance chosen in order to get the desired margin and common
probabilities are sampled. Negative values are converted to 0,
positive values to 1.
rmvbin(n, margprob, commonprob=diag(margprob), bincorr=diag(length(margprob)), sigma=diag(length(margprob)), colnames=NULL, simulvals=NULL)
Friedrich Leisch
Friedrich Leisch, Andreas Weingessel and Kurt Hornik (1998). On the generation of correlated artificial binary data. Working Paper Series, SFB ``Adaptive Information Systems and Modelling in Economics and Management Science'', Vienna University of Economics, http://www.wu-wien.ac.at/am
commonprob2sigma
,check.commonprob
,
simul.commonprob
# uncorrelated columns: rmvbin(10, margprob=c(0.3,0.9)) # correlated columns m <- cbind(c(1/2,1/5,1/6),c(1/5,1/2,1/6),c(1/6,1/6,1/2)) rmvbin(10,commonprob=m) # same as the second example, but faster if the same probabilities are # used repeatedly (coomonprob2sigma rather slow) sigma <- commonprob2sigma(m) rmvbin(10,margprob=diag(m),sigma=sigma)