simul.commonprob: Simulate Joint Binary Probabilities

In bindata: Generation of Artificial Binary Data

Simulate Joint Binary Probabilities

Description

Compute common probabilities of binary random variates generated by thresholding normal variates at 0.

Usage

simul.commonprob(margprob, corr=0, method="integrate", n1=10^5, n2=10)

Arguments

margprob	vector of marginal probabilities.
corr	vector of correlation values for normal distribution.
method	either "integrate" or "monte carlo".
n1	number of normal variates if method is "monte carlo".
n2	number of repetitions if method is "monte carlo".

Details

The output of this function is used by rmvbin. For all combinations of marginprob[i], marginprob[j] and corr[k], the probability that both components of a normal random variable with mean qnorm(marginprob[c(i,j)]) and correlation corr[k] are larger than zero is computed.

The probabilities are either computed by numerical integration of the multivariate normal density, or by Monte Carlo simulation.

For normal usage of rmvbin it is not necessary to use this function, one simulation result is provided as variable SimulVals in this package and loaded by default.

Value

simul.commonprob returns an array of dimension c(length(margprob), length(margprob), length(corr)).

Author(s)

Friedrich Leisch

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

rmvbin

Examples

simul.commonprob(seq(0,1,0.5), seq(-1,1,0.5), meth="mo", n1=10^4)

data(SimulVals)

bindata documentation built on May 29, 2024, 8:58 a.m.