escouf                package:pastecs                R Documentation

_C_h_o_o_s_e _v_a_r_i_a_b_l_e_s _u_s_i_n_g _t_h_e _E_s_c_o_u_f_i_e_r'_s _e_q_u_i_v_a_l_e_n_t _v_e_c_t_o_r_s _m_e_t_h_o_d

_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)
     ## S3 method for class 'escouf':
     summary(esc)
     ## S3 method for class 'escouf':
     plot(esc, level=x$level, lhorz=TRUE, lvert=TRUE, lvars=TRUE,
             lcol=2, llty=2, diff=TRUE, dlab="RV' (units not shown)", dcol=4,
             dlty=par("lty"), dpos=0.8, ...)
     ## S3 method for class 'escouf':
     lines(esc, level=x$level, lhorz=TRUE, lvert=TRUE, lvars=TRUE,
             lcol=2, llty=2, ...)
     ## S3 method for class 'escouf':
     identify(esc, lhorz=TRUE, lvert=TRUE, lvars=TRUE, lcol=2,
             llty=2, ...)
     ## S3 method for class 'escouf':
     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)

