anosim                 package:vegan                 R Documentation

_A_n_a_l_y_s_i_s _o_f _S_i_m_i_l_a_r_i_t_i_e_s

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

     Analysis of similarities (ANOSIM) provides a way to test
     statistically whether there is a significant difference between
     two or more groups of sampling units.

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

     anosim(dis, grouping, permutations=1000, strata)

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

     dis: Dissimilarity matrix.

grouping: Factor for grouping observations.

permutations: Number of permutation to assess the significance of the
          ANOSIM statistic. 

  strata: An integer vector or factor specifying the strata for
          permutation. If supplied, observations are permuted only
          within the specified strata.

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

     Analysis of similarities (ANOSIM) provides a way to test
     statistically whether there is a significant difference between
     two or more groups of sampling units.  Function 'anosim' operates
     directly on a dissimilarity matrix.  A suitable dissimilarity
     matrix is produced by functions 'dist' or 'vegdist'.  The method
     is philosophically allied with NMDS ordination ('isoMDS'), in that
     it uses only the rank order of dissimilarity values.

     If two groups of sampling units are really different in their
     species composition, then compositional dissimilarities between
     the groups ought to be greater than those within the groups.  The
     'anosim' statistic R is based on the difference of mean ranks
     between groups (r_B) and within groups (r_W):


                     R = (r_B - r_W)/(N/(N-1)/4)


     The divisor is chosen so that R will be in the interval -1 ... +1,
     value 0 indicating completely random grouping.

     The statistical significance of observed R is assessed by
     permuting the grouping vector to obtain the empirical distribution
     of R under null-model.

     The function has 'summary' and 'plot' methods.  These both show
     valuable information to assess the validity of the method:  The
     function assumes that all ranked dissimilarities within groups 
     have about equal median and range.  The 'plot' method uses
     'boxplot' with options 'notch=TRUE' and 'varwidth=TRUE'.

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

     The function returs a list of class 'anosim' with following items: 

   call : Function call.

statistic: The value of ANOSIM statistic R

  signif: Significance from permutation.

    perm: Permutation values of R

class.vec: Factor with value 'Between' for dissimilarities between
          classes and class name for corresponding dissimilarity within
          class.

dis.rank: Rank of dissimilarity entry.

dissimilarity: The name of the dissimilarity index: the '"method"'
          entry of the 'dist' object.

_N_o_t_e:

     I don't quite trust this method.  Somebody should study its
     performance carefully.  The function returns a lot of information 
     to ease further scrutiny.

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

     Jari Oksanen, with a help from Peter R. Minchin.

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

     Clarke, K. R. (1993). Non-parametric multivariate analysis of
     changes in community structure. _Australian Journal of Ecology_
     18, 117-143.

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

     'dist' and 'vegdist' for obtaining dissimilarities, and 'rank' for
     ranking real values.  For comparing dissimilarities against
     continuous variables, see 'mantel'.

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

     data(dune)
     data(dune.env)
     dune.dist <- vegdist(dune)
     attach(dune.env)
     dune.ano <- anosim(dune.dist, Management)
     summary(dune.ano)
     plot(dune.ano)

