concareaExampleKola          package:StatDA          R Documentation

_C_o_n_c_e_n_t_r_a_t_i_o_n _A_r_e_a _P_l_o_t _f_o_r _K_o_l_a _d_a_t_a _e_x_a_m_p_l_e

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

     Displays a concentration area plot example for the Kola data. This
     procedure ist useful for determining if mulitple populations that
     are spatially dependent are present in a data set. For a more
     general function see concarea.

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

     concareaExampleKola(x, y, z, zname = deparse(substitute(z)),
     caname = deparse(substitute(z)), borders="bordersKola", logx = FALSE, ifjit = FALSE,
     ifrev = FALSE, ngrid = 100, ncp = 0, xlim = NULL, xcoord = "Easting",
     ycoord = "Northing", ifbw = FALSE, x.logfinetick = c(2, 5, 10),
     y.logfinetick = c(2, 5, 10))

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

       x: name of the x-axis spatial coordinate, the eastings 

       y: name of the y-axis spatial coordinate, the northings 

       z: name of the variable to be processed and plotted 

   zname: a title for the x-axes of the qp-plot and concentration area
          plot. 

  caname: a title for the image of interpolated data. 

 borders: either NULL or character string with the name of the list
          with list elements x and y for x- and y-coordinates of map
          borders 

    logx: if it is required to make a logarithmis data transformation
          for the interpolation 

   ifrev: if FALSE the empirical function ist plotted from highest
          value to lowest 

   ngrid: default value is 100

    xlim: the range for the x-axis 

  xcoord: a title for the x-axis, defaults to "Easting" 

  ycoord: a title for the y-axis, defaults to "Northing" 

    ifbw: if the plot is drawn in black and white 

x.logfinetick: how fine are the tick marks on log-scale on x-axis 

y.logfinetick: how fine are the tick marks on log-scale on y-axis 

   ifjit: default value is FALSE

     ncp: default value is 0

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

     The function assumes that the area is proportional to the count of
     grid points. To be a reasonable model the data points should be
     'evenly' spread over the plane. The interpolated grid size ist
     computed as (max(x) - min(x))/ngrid, with a default value of 100
     for ngrid. Akima's interpolation function is used to obtain a
     linear interpolation between the spatial data values.

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

     An example concentration area plot for Kola is created.

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

     Peter Filzmoser <P.Filzmoser@tuwien.ac.at>

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

     C. Reimann, P. Filzmoser, R.G. Garrett, and R. Dutter: Statistical
     Data Analysis Explained. Applied Environmental Statistics with R.
     John Wiley and Sons Inc. To appear.

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

     'qpplot.das', 'concarea', 'caplot'

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

     data(ohorizon)
     data(kola.background)
     data(bordersKola)

     Cu=ohorizon[,"Cu"]
     X=ohorizon[,"XCOO"]
     Y=ohorizon[,"YCOO"]

     par(mfrow=c(2,2),mar=c(1.5,1.5,1.5,1.5))
     concareaExampleKola(X,Y,Cu,log=TRUE,zname="Cu in O-horizon [mg/kg]",
        x.logfinetick=c(2,5,10),y.logfinetick=c(10))
             

