rmvbin                package: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 distribution, or covariance
     matrix of the normal distribution.

_U_s_a_g_e:

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

_A_r_g_u_m_e_n_t_s:

       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.

_D_e_t_a_i_l_s:

     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.

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

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

     'commonprob2sigma', 'check.commonprob', 'simul.commonprob'

_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 (commonprob2sigma rather slow)
     sigma <- commonprob2sigma(m)
     rmvbin(10,margprob=diag(m),sigma=sigma)

