makemultdata            package:mixtools            R Documentation

_P_r_o_d_u_c_e _C_u_t_p_o_i_n_t _M_u_l_t_i_n_o_m_i_a_l _D_a_t_a

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

     Change data into a matrix of multinomial counts using the cutpoint
     method and generate EM algorithm starting values for  a
     k-component mixture of multinomials.

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

     makemultdata(..., cuts)

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

     ...: Either vectors (possibly of different lengths) of raw data 
          or an nxm matrix (or data frame) of data. If '...' are
          vectors of varying length,  then 'makemultdata' will create a
          matrix of size nxm where n is the sample size and m is the
          length of the vector with maximum length.  Those  vectors
          with length less than m will have 'NA's to make the 
          corresponding row in the matrix of length m.  If '...' is a
          matrix (or data frame), then the rows must correspond to the
          sample and the columns the repeated measures.

    cuts: A vector of cutpoints.  This vector is sorted by the
          algorithm.

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

     The (i, j)th entry of the matrix 'y' (for j < p) is equal to the
     number of entries in the ith column of 'x' that are less than or
     equal to 'cuts'[j]. The (i, p)th entry is equal to the number of
     entries greater than 'cuts'[j].

_V_a_l_u_e:

     'makemultdata' returns an object which is a list with components: 

       x: An nxm matrix of the raw data.

       y: An nxp matrix of the discretized data where p is one more
          than the number of cutpoints. Each row is a multinomial
          vector of counts.  In particular, each row should sum to the
          number of repeated measures for that sample.

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

     Elmore, R. T., Hettmansperger, T. P. and Xuan, F. (2004) The Sign
     Statistic, One-Way Layouts and Mixture Models, _Statistical
     Science_ *19(4)*, 579-587.

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

     'compCDF', 'multmixmodel.sel', 'multmixEM'

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

     ## Randomly generated data.

     y<-matrix(rpois(70, 6), 10, 7)
     cuts<-c(2, 5, 7)
     out1<-makemultdata(y, cuts = cuts)
     out1

     ## The sulfur content of the coal seams in Texas.

     A<-c(1.51, 1.92, 1.08, 2.04, 2.14, 1.76, 1.17)
     B<-c(1.69, 0.64, .9, 1.41, 1.01, .84, 1.28, 1.59)
     C<-c(1.56, 1.22, 1.32, 1.39, 1.33, 1.54, 1.04, 2.25, 1.49)
     D<-c(1.3, .75, 1.26, .69, .62, .9, 1.2, .32)
     E<-c(.73, .8, .9, 1.24, .82, .72, .57, 1.18, .54, 1.3)

     out2<-makemultdata(A, B, C, D, E, 
                        cuts = median(c(A, B, C, D, E)))
     out2

     ## The reaction time data.

     data(RTdata)
     out3<-makemultdata(RTdata, cuts = 
                        100*c(5, 10, 12, 14, 16, 20, 25, 30, 40, 50))
     out3$y

