pco                  package:labdsv                  R Documentation

_P_r_i_n_c_i_p_a_l _C_o_o_r_d_i_n_a_t_e_s _A_n_a_l_y_s_i_s

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

     Principal coordinates analysis is an eigenanalysis of  distance or
     metric dissimilarity matrices.

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

     pco(dis, k=2)

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

     dis: the distance or dissimilarity matrix object of class "dist"
          returned from  'dist', 'vegdist', or  'dsvdis'

       k: the number of dimensions to return

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

     pco is simply a wrapper for the 'cmdscale' function of Venebles
     and Ripley to make plotting of the function similar to other
     LabDSV functions

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

     an object of class 'pco' with components: 

  points: the coordinates of samples on eigenvectors

_N_o_t_e:

     Principal Coordinates Analysis was pioneered by Gower (1966) as an
     alternative to PCA better suited to ecological datasets.

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

     of the 'cmdscale' function: Venebles and Ripley 

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

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

     Gower, J.C. (1966)  Some distance properties of latent root and
     vector methods used in multivariate analysis.  Biometrika
     53:325-328.

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

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

     'cmdscale', 'pca',  'nmds', 'cca'

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

             data(bryceveg) # returns a vegetation dataframe
             dis.bc <- dsvdis(bryceveg,'bray/curtis') 
                       # returns an object of class \sQuote{dist}
             veg.pco <- pco(dis.bc,k=4) # returns first 4 dimensions

