disana                package:labdsv                R Documentation

_D_i_s_s_i_m_i_l_a_r_i_t_y _A_n_a_l_y_s_i_s

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

     Dissimilarity analysis is a graphical analysis of the 
     distribution of values in a dissimilarity matrix

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

     disana(x)

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

       x: an object of class 'dist' such as returned by  'dist',
          'vegdist' or 'dsvdis'

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

     Calculates three vectors: the minimum, mean,  and maximum
     dissimilarity for each sample in a dissimilarity matrix.   By
     default it produces three plots: the sorted dissimilarity values,
     the sorted min, mean, and maximum dissimilarity for each sample,
     and the mean dissimilarity versus the minimum dissimilarity for
     each sample.   Optionally, you can identify sample plots in the
     last panel with the mouse.

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

     Plots three graphs to the current graphical device, and returns an
     (invisible) list with four components: 

     min: the minimum dissimilarity of each sample to all others

    mean: the mean dissimilarity of each sample to all others

     max: the maximum dissimilarity of each sample to all others

   plots: a vector of samples identified in the last panel

_N_o_t_e:

     Dissimilarity matrices are often large, and difficult to visualize
     directly.  'disana' is designed to highlight aspects of interest
     in these large matrices.

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

     David W. Roberts droberts@montana.edu <URL:
     http://ecology.msu.montana.edu/droberts>

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

     <URL: http://ecology.msu.montana.edu/labdsv/R>

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

         data(bryceveg) # returns a data.frame called veg
         dis.bc <- dsvdis(bryceveg,'bray/curtis')
         ## Not run: disana(dis.bc)

