plot.acomp           package:compositions           R Documentation

_D_i_s_p_l_a_y_i_n_g _c_o_m_p_o_s_i_t_i_o_n_s _i_n _t_e_r_n_a_r_y _d_i_a_g_r_a_m_s

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

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

      ## S3 method for class 'acomp':
      plot(x,...,labels=colnames(X),cn=colnames(X),
               aspanel=FALSE,id=FALSE,idlabs=NULL,idcol=2,center=FALSE,
               scale=FALSE,pca=FALSE,col.pca=par("col"),margin="acomp",
               add=FALSE,triangle=!add,col=par("col"))
      ## S3 method for class 'rcomp':
      plot(x,...,labels=colnames(X),cn=colnames(X),
               aspanel=FALSE,id=FALSE,idlabs=NULL,idcol=2,center=FALSE,
               scale=FALSE,pca=FALSE,col.pca=par("col"),margin="rcomp",
               add=FALSE,col=par("col"))
               

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

       x: a dataset of a compositional class

     ...: further graphical parameters passed (see 'par')

  margin: the type of marginalisation to be computed, when displaying
          the individual panels. Possible values are: '"acomp"',
          '"rcomp"' and any of the variable names/column numbers in the
          composition. If one of the columns is selected each panel
          displays a subcomposition given by the row part, the column
          part and the given part. If one of the classes is given the
          corresponding margin 'acompmargin'  or  'rcompmargin' is
          used. 

     add: a logical indicating whether the information should just be
          added to an existing plot. If FALSE a new plot is created

triangle: a logical indicating whether the triangle should be drawn

     col: the color to plot the data

  labels: the names of the parts

      cn: the names of the parts to be used in a single panel. Internal
          use only.

 aspanel: logical indicating that only a single panel should be drawn
          and not the whole plot. Internal use only

      id: logical, if TRUE one can identify the points like with the
          'identify' command. 

  idlabs: a character vector providing the labels to be used with the
          identification, when 'id=TRUE'

   idcol: color of the 'idlabs' labels

  center: a logical indicating whether a the data should be centered
          prior to the plot. Centering is done in the choosen geometry.
          See 'scale'

   scale: a logical indicating whether a the data should be scaled
          prior to the plot. Scaling is done in the choosen geometry.
          See 'scale'

     pca: a logical indicating whether the first principal component
          should be displayed in the plot. Currently, the direction of
          the principal component of the displayed subcomposition is
          displayed as  a line. In a future, the projected principal
          componenent of the whole  dataset should be displayed.

 col.pca: The color to draw the principal component.

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

     The data is displayed in ternary diagrams. Thus, it does not work
     for two-part compositions. Compositions of three parts are
     displayed in a single ternary diagram. For compositions of more
     than three components, the data is arranged in a scatterplot
     matrix through the command 'pairs'. 
      In this case, the third component in each of the panels is chosen
      according to setting of 'margin='. Possible values of 'margin='
     are: '"acomp"', '"rcomp"' and any of the variable names/column
     numbers in the composition. If one of the columns is selected each
     panel displays a subcomposition given by the row part, the column
     part and the given part. If one of the classes is given the
     corresponding margin 'acompmargin'  or  'rcompmargin' is used. 
      Ternary diagrams can be read in multiple ways. Each corner of the
     triangle corresponds to an extreme composition containing only the
     part displayed in that corner. Points on the edges correspond to
     compositions containing only the parts in the adjacent corners.
     The relative amounts are displayed by the distance to the opposite
     corner (so-called barycentric coordinates). The individual
     portions  of any point can be infered by drawing a line through
     the investigated point,  and parallel to the edge opposite to the
     corner of the part of interest.  The portion of this part is
     constant along the line. Thus we can read it  on the sides of the
     ternary diagram, where the line crosses its borders. Note that
     these 'isoPortionLines' remain straight under an  arbitrary
     perturbation.  

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

     K.Gerald v.d. Boogaart <URL: http://www.stat.boogaart.de>, Raimon
     Tolosana-Delgado

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

     Aitchison, J. (1986) _The Statistical Analysis of Compositional
     Data_ Monographs on Statistics and Applied Probability. Chapman &
     Hall Ltd., London (UK). 416p.

     Aitchison, J, C. Barcel'o-Vidal, J.J. Egozcue, V. Pawlowsky-Glahn
     (2002) A consise guide to the algebraic geometric structure of the
     simplex, the sample space for compositional data analysis, _Terra
     Nostra_, Schriften der Alfred Wegener-Stiftung, 03/2003

     Billheimer, D., P. Guttorp, W.F. and Fagan (2001) Statistical
     interpretation of species composition, _Journal of the American
     Statistical Association_, *96* (456), 1205-1214

     Pawlowsky-Glahn, V. and J.J. Egozcue (2001) Geometric approach to
     statistical analysis on the simplex. _SERRA_ *15*(5), 384-398

     <URL: http://ima.udg.es/Activitats/CoDaWork03>

     <URL: http://ima.udg.es/Activitats/CoDaWork05>

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

     'plot.aplus', 'kingTetrahedron' (for 3D-plot),
     'qqnorm.acomp','boxplot.acomp'

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

     data(SimulatedAmounts)
     plot(acomp(sa.lognormals))
     plot(rcomp(sa.lognormals))
     plot(acomp(sa.lognormals5),pca=TRUE)
     plot(rcomp(sa.lognormals5),pca=TRUE)

