cca                   package:ade4                   R Documentation

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

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

     Performs a Canonical Correspondence Analysis.

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

     cca(sitspe, sitenv, scannf = TRUE, nf = 2)

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

  sitspe: a data frame for correspondence analysis, typically a sites x
          species table

  sitenv: a data frame containing variables, typically a sites x
          environmental variables table

  scannf: a logical value indicating whether the eigenvalues bar plot
          should be displayed

      nf: if scannf FALSE, an integer indicating the number of kept
          axes

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

     returns an object of class 'pcaiv'. See 'pcaiv'

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

     Daniel Chessel chessel@biomserv.univ-lyon1.fr

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

     Ter Braak, C. J. F. (1986) Canonical correspondence analysis : a
     new eigenvector technique for multivariate direct gradient
     analysis. _Ecology_, *67*, 1167-1179.

     Ter Braak, C. J. F. (1987) The analysis of vegetation-environment
     relationships by canonical correspondence analysis. _Vegetatio_,
     *69*, 69-77.

     Chessel, D., Lebreton J. D. and Yoccoz N. (1987) Proprits de
     l'analyse canonique des correspondances. Une utilisation en
     hydrobiologie. _Revue de Statistique Applique_, *35*, 55-72.

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

     'cca' in the package 'vegan'

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

     data(rpjdl)
     millog <- log(rpjdl$mil + 1)
     iv1 <- cca(rpjdl$fau, millog, scan = FALSE)
     plot(iv1)

     # analysis with c1 - as - li -ls
     # projections of inertia axes on PCAIV axes
     s.corcircle(iv1$as)

     # Species positions
     s.label(iv1$c1, 2, 1, clab = 0.5, xlim = c(-4,4))
     # Sites positions at the weighted mean of present species
     s.label(iv1$ls, 2, 1, clab = 0, cpoi = 1, add.p = TRUE)

     # Prediction of the positions by regression on environmental variables
     s.match(iv1$ls, iv1$li, 2, 1, clab = 0.5)

     # analysis with fa - l1 - co -cor
     # canonical weights giving unit variance combinations
     s.arrow(iv1$fa)

     # sites position by environmental variables combinations
     # position of species by averaging
     s.label(iv1$l1, 2, 1, clab = 0, cpoi = 1.5)
     s.label(iv1$co, 2, 1, add.plot = TRUE)

     s.distri(iv1$l1, rpjdl$fau, 2, 1, cell = 0, csta = 0.33)
     s.label(iv1$co, 2, 1, clab = 0.75, add.plot = TRUE)

     # coherence between weights and correlations
     par(mfrow = c(1,2))
     s.corcircle(iv1$cor, 2, 1)
     s.arrow(iv1$fa, 2, 1)
     par(mfrow = c(1,1))
      

