

rmvbin(bindata)                              R Documentation

_M_u_l_t_i_v_a_r_i_a_t_e _B_i_n_a_r_y _R_a_n_d_o_m _V_a_r_i_a_t_e_s

_D_e_s_c_r_i_p_t_i_o_n_:

     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 dis-
     tribution, or covariance matrix of the normal distribu-
     tion. 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.

_U_s_a_g_e_:

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

_A_u_t_h_o_r_(_s_)_:

     Friedrich Leisch

_R_e_f_e_r_e_n_c_e_s_:

     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 Man-
     agement Science'', Vienna University of Economics,
     <URL: http://www.wu-wien.ac.at/am>

_S_e_e _A_l_s_o_:

     `commonprob2sigma',`check.commonprob', `simul.common-
     prob'

_E_x_a_m_p_l_e_s_:

     # 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)

