KrigeLegend function

Krige

Krige

Plots Krige maps and Legend based on continuous or percentile scale.

KrigeLegend(X, Y, z, resol = 100, vario, type = "percentile",
whichcol = "gray", qutiles = c(0, 0.05, 0.25, 0.5, 0.75, 0.9, 0.95, 1),borders=NULL,
leg.xpos.min = 780000, leg.xpos.max = 8e+05, leg.ypos.min = 7760000,
leg.ypos.max = 7870000, leg.title = "mg/kg", leg.title.cex = 0.7,
leg.numb.cex = 0.7, leg.round = 2, leg.numb.xshift = 70000, leg.perc.xshift = 40000,
leg.perc.yshift = 20000, tit.xshift = 35000)

Arguments

  • X: X-coordinates
  • Y: Y-coordinates
  • z: values on the coordinates
  • resol: resolution of blocks for Kriging
  • vario: variogram model
  • type: "percentile" for percentile legend, "contin" for continous grey-scale or colour map
  • whichcol: type of colour scheme to use: "gray", "rainbow", "rainbow.trunc", "rainbow.inv", "terrain", "topo"
  • qutiles: considered quantiles if type="percentile" is used
  • 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
  • leg.xpos.min: minimum value of x-position of the legend
  • leg.xpos.max: maximum value of x-position of the legend
  • leg.ypos.min: minimum value of y-position of the legend
  • leg.ypos.max: maximum value of y-position of the legend
  • leg.title: title for legend
  • leg.title.cex: cex for legend title
  • leg.numb.cex: cex for legend number
  • leg.round: round legend to specified digits "pretty"
  • leg.numb.xshift: x-shift of numbers in legend relative to leg.xpos.max
  • leg.perc.xshift: x-shift of "Percentile" in legend relative to leg.xpos.min
  • leg.perc.yshift: y-shift of numbers in legend relative to leg.ypos.max
  • tit.xshift: x-shift of title in legend relative to leg.xpos.max

Returns

No return value, creates a plot.

Details

Based on a variogram model a interpolation of the spatial data is computed. The variogram has to be provided by the user and based on this model the spatial prediction is made. To distinguish between different values every predicted value is plotted in his own scale of the choosen colour.

References

C. Reimann, P. Filzmoser, R.G. Garrett, and R. Dutter: Statistical Data Analysis Explained. Applied Environmental Statistics with R. John Wiley and Sons, Chichester, 2008.

Author(s)

Peter Filzmoser <P.Filzmoser@tuwien.ac.at > http://cstat.tuwien.ac.at/filz/

Examples

data(chorizon)
data(kola.background)
X=chorizon[,"XCOO"]
Y=chorizon[,"YCOO"]
#el=chorizon[,"As"]
#vario.b <- variog(coords=cbind(X,Y), data=el, lambda=0, max.dist=300000)
#data(res.eyefit.As_C_m) #need the data 
#v5=variofit(vario.b,res.eyefit.As_C_m,cov.model="spherical",max.dist=300000)

plot(X,Y,frame.plot=FALSE,xaxt="n",yaxt="n",xlab="",ylab="",type="n")

# to inclrease the resolution, set e.g. resol=100
#data(bordersKola) # x and y coordinates of project boundary
#KrigeLegend(X,Y,el,resol=25,vario=v5,type="percentile",whichcol="gray",
#    qutiles=c(0,0.05,0.25,0.50,0.75,0.90,0.95,1),borders="bordersKola",
#    leg.xpos.min=7.8e5,leg.xpos.max=8.0e5,leg.ypos.min=77.6e5,leg.ypos.max=78.7e5,
#    leg.title="mg/kg", leg.title.cex=0.7, leg.numb.cex=0.7, leg.round=2,
#    leg.numb.xshift=0.7e5,leg.perc.xshift=0.4e5,leg.perc.yshift=0.2e5,tit.xshift=0.35e5)
#
#plotbg(map.col=c("gray","gray","gray","gray"),map.lwd=c(1,1,1,1),add.plot=TRUE)
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