event2dichot               package:sna               R Documentation

_C_o_n_v_e_r_t _a_n _O_b_s_e_r_v_e_d _E_v_e_n_t _M_a_t_r_i_x _t_o _a _D_i_c_h_o_t_o_m_o_u_s _m_a_t_r_i_x

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

     Given a valued adjacency matrix (possibly derived from observed
     interaction ``events''), `event2dichot' returns a dichotomous
     adjacency matrix.

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

     event2dichot(m, method="quantile", thresh=0.5, leq=FALSE)

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

       m: An adjacency matrix 

  method: One of ``quantile,'' ``rquantile,'' ``cquantile,'' ``mean,''
          ``rmean,'' ``cmean,'' ``absolute,'' ``rank,'' ``rrank,'' or
          ``crank''  

  thresh: Dichotomization thresholds for ranks or quantiles 

     leq: Boolean indicating whether values less than or equal to the
          threshold should be taken as existing edges; the alternative
          is to use values strictly greater than the threshold 

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

     The methods used for choosing dichotomization thresholds are as
     follows:

        1.  quantile: specified quantile over the distribution of all
           edge values

        2.  rquantile: specified quantile by row

        3.  cquantile: specified quantile by column

        4.  mean: grand mean

        5.  rmean: row mean

        6.  cmean: column mean

        7.  absolute: the value of `thresh' itself

        8.  rank: specified rank over the distribution of all edge
           values

        9.  rrank: specified rank by row

        10.  crank: specified rank by column

     Note that when a quantile, rank, or value is said to be
     ``specified,'' this refers to the value of `thresh'.

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

     The dichotomized data matrix

_A_u_t_h_o_r(_s):

     Carter T. Butts ctb@andrew.cmu.edu

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

     ~put references to the literature/web site here ~

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

     #Draw a matrix of normal values
     n<-matrix(rnorm(25),nrow=5,ncol=5)

     #Dichotomize by the mean value
     event2dichot(n,"mean")

     #Dichotomize by the 0.95 quantile
     event2dichot(n,"quantile",0.95)

