plotvario               package:StatDA               R Documentation

_P_l_o_t _E_m_p_i_r_i_c_a_l _V_a_r_i_o_g_r_a_m

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

     Plot sample (empirical) variogram computed using the function
     variog.

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

     plotvario(x, max.dist, vario.col = "all",
     scaled = FALSE, var.lines = FALSE, envelope.obj = NULL,
     pts.range.cex, bin.cloud = FALSE, ...)

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

       x: an object of the class "variogram", typically an output of
          the function variog 

max.dist: maximum distance for the x-axis. The default is the maximum
          distance for which the sample variogram was computed 

vario.col: only used if obj has information on more than one empirical
          variogram. The default "all" indicates that variograms of all
          variables should be plotted. Alternativelly a numerical
          vector can be used to select variables. 

  scaled: If TRUE the variogram values are divided by the sample
          variance. This allows comparison of variograms of variables
          measured in different scales. 

var.lines: If TRUE a horizontal line is drawn at the value of the
          variance of the data (if scaled=FALSE) or at 1 (if
          scaled=TRUE) 

envelope.obj: adds a variogram envelope 

pts.range.cex: optional. A two elements vector with maximum and minimum
          values for the character expansion factor cex. If provided
          the point sizes in binned variogram are proportional to the
          number of pairs of points used to compute each bin. 

bin.cloud: logical. If TRUE and the sample variogram was computed with
          the option keep.cloud=TRUE, boxplots of values at each bin
          are plotted instead of the empirical variograms. 

     ...: other arguments to be passed to the function plot or matplot. 

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

     Computes the same as the function plot.variogram from the library
     geoR.

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

     Variogram plot.

_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:

     'plot.variogram'

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

     data(chorizon)
     X=chorizon[,"XCOO"]/1000
     Y=chorizon[,"YCOO"]/1000
     el=chorizon[,"As"]
     vario.b <- variog(coords=cbind(X,Y), data=el, lambda=0, max.dist=300)
     plotvario(vario.b,xlab="Distance [km]",ylab="Semivariogram",
     cex.lab=1.2,max.dist=300,pch=1,cex=1)

