bindata (version 0.9-20)

rmvbin: Multivariate Binary Random Variates

Description

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.

Usage

rmvbin(n, margprob, commonprob=diag(margprob),
       bincorr=diag(length(margprob)),
       sigma=diag(length(margprob)),
       colnames=NULL, simulvals=NULL)

Arguments

n

number of observations.

margprob

margin probabilities that the components are 1.

commonprob

matrix of probabilities that components i and j are simultaneously 1.

bincorr

matrix of binary correlations.

sigma

covariance matrix for the normal distribution.

colnames

vector of column names for the resulting observation matrix.

simulvals

result from simul.commonprob, a default data array is automatically loaded if this argument is omitted.

Details

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.

References

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.

See Also

commonprob2sigma, check.commonprob, simul.commonprob

Examples

# NOT RUN {
## 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 (commonprob2sigma rather slow)
sigma <- commonprob2sigma(m)
rmvbin(10,margprob=diag(m),sigma=sigma)
# }