escouf                package:pastecs                R Documentation

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

     Calculate equivalent vectors sensu Escoufier, that is, most
     significant variables from a multivariate data frame according to
     a principal component analysis (variables that are most correlated
     with the principal axes). This method is useful mainly for
     physical or chemical data where simply summarizing them with a PCA
     does not always gives easily interpretable principal axes.

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

     escouf(x, level=1, verbose=TRUE)
     summary(esc)
     plot(esc, level=x$level, lhorz=TRUE, lvert=TRUE, lvars=TRUE,
             lcol=2, llty=2, diff=TRUE, dlab="RV' (units not shown)", dcol=4,
             dlty=par("lty"), dpos=0.8, ...)
     lines(esc, level=x$level, lhorz=TRUE, lvert=TRUE, lvars=TRUE,
             lcol=2, llty=2, ...)
     identify(esc, lhorz=TRUE, lvert=TRUE, lvars=TRUE, lcol=2,
             llty=2, ...)
     extract(esc, n=NULL, level=e$level)

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

       x: A data frame containing the variables to sort according to
          the Escoufier's method 

   level: The level of correlation at which to stop calculation. By
          default `level=1', the highest value, and all variables are
          sorted. Specify a value lower than one to speed up
          calculation. If you specify a too low values you will not be
          able to extract all significant variables (extraction level
          must be lower than calculation level). We advise you keep
          0.95 < level < 1 

 verbose: Print calculation steps. This allows to control the
          percentage of calculation already achieved when computation
          takes a long time (that is, with many variables to sort) 

     esc: An 'escouf' object returned by `escouf'

   lhorz: If `TRUE' then an horizontal line indicating the extraction
          level is drawn 

   lvert: If `TRUE' then a vertical line separate the n extracted
          variables at left from the rest 

   lvars: If `TRUE' then the x-axis labels of the n extracted variables
          at left are printed in a different color to emphasize them 

    lcol: The color to use to draw the lines (`lhorz=TRUE' and
          `lvert=TRUE') and the variables labels (`lvars=TRUE') of the
          n extracted variables. By default, color 2 is used 

    llty: The style used to draw the lines (`lhorz=TRUE' and
          `lvert=TRUE'). By default, lines are dashed 

    diff: If `TRUE' then the RV' curve is also plotted (by default) 

    dlab: The label to use for the RV' curve. By default: `"RV' (units
          not shown)"' 

    dcol: The color to use for the RV' curve (by default, color 4 is
          used) 

    dlty: The style for the RV' curve 

    dpos: The relative horizontal position of the label for the RV'
          curve. The default value of 0.8 means that the label is
          placed at 80% of the horizontal axis.Vertical position of the
          label is automatically determined 

     ...: additional graph parameters 

       n: The number of variables to extract. If a value is given, it
          has the priority on `level' 

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

     An object of type 'escouf' is returned. It has methods `print()',
     `summary()', `plot()', `lines()', `identify()', `extract()'.

_W_A_R_N_I_N_G:

     Since a large number of iterations is done, this function is slow
     with a large number of variables (more than 25-30)!

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

     Frdric Ibanez (ibanez@obs-vlfr.fr), Philippe Grosjean
     (phgrosjean@sciviews.org), Benjamin Planque
     (Benjamin.Planque@ifremer.fr),  Jean-Marc Fromentin
     (Jean.Marc.Fromentin@ifremer.fr)

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

     Cambon, J., 1974. Vecteur quivalent  un autre au sens des
     composantes principales. Application hydrologique. DEA de
     Mathmatiques Appliques, Universit de Montpellier.

     Escoufier, Y., 1970. Echantillonnage dans une population de
     variables alatoires relles. Pub. Inst. Stat. Univ. Paris,
     19:1-47.

     Jabaud, A., 1996. Cadre climatique et hydrobiologique du lac
     Lman. DEA d'Ocanologie Biologique Paris.

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

     `abund'

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

     data(marbio)
     marbio.esc <- escouf(marbio)
     summary(marbio.esc)
     plot(marbio.esc)
     # The x-axis has short labels. For more info., enter: 
     marbio.esc$vr
     # Define a level at which to extract most significant variables
     marbio.esc$level <- 0.90
     # Show it on the graph
     lines(marbio.esc)
     # This can also be done interactively on the plot using:
     # marbio.esc$level <- identify(marbio.esc)
     # Finally, extract most significant variables
     marbio2 <- extract(marbio.esc)
     names(marbio2)

