StatDA (version 1.7.4)

KrigeLegend: Krige

Description

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

Usage

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

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.

Examples

# NOT RUN {
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)
# }