Variogram fit with RPanel

September 21, 2011
By

(This article was first published on R Video tutorial for Spatial Statistics, and kindly contributed to R-bloggers)

During the UseR 2011 conference I saw lots of examples of the use of RPanel to create a GUI in R. Yesterday, because I was a bit bored of the work I was doing I started thinking about this and I decided to try this package.

My objective was to create a new panel with all the main setting for fitting a variogram model to an omnidirectional variogram with gstat.

This is the script:

library(rpanel)
library(gstat)

data<-read.table("data.txt",sep="",header=T)
coordinates(data)=~Lat+Lon

##Variogram Fitting
variogram.plot <- function(panel) {
with(panel, {
variogram<-variogram(Oxigen~1,data,cutoff=Cutoff)
vgm.var<-vgm(psill=Sill,model=Model,range=Range,nugget=Nugget)
#fit<-fit.variogram(variogram,vgm.var)
plot(variogram$dist,variogram$gamma,xlab=”Distance”,ylab=”Semivariance”)
lines(variogramLine(vgm.var,maxdist=Range))
})
panel
}

var.panel <- rp.control("Variogram",Sill=20,Range=250,Nugget=0,Model="Mat",Cutoff=250)
rp.listbox(var.panel,Model,c(“Mat”,”Sph”,”Gau”))
rp.slider(var.panel, Cutoff, 0,500,showvalue=T)
rp.slider(var.panel, Sill, 0,500,showvalue=T)
rp.slider(var.panel, Range, 0,1000,showvalue=T)
rp.slider(var.panel, Nugget, 0,15,showvalue=T)
rp.button(var.panel, title = “Fit”, action = variogram.plot)

At this address you can find a zip file with a sample dataset that you can use to try this script, however if you know a bit of gstat you can start customizing it straigth away:

This is the screenshot from my R Console:

I also tried to embed the variogram plot into the panel in order to execute the script in batch mode, this is the result:


if(print(require(tcltk))==FALSE){install.packages(“tcltk”,repos=”http://cran.r-project.org”)}
if(print(require(tcltk))==TRUE){require(tcltk)}

if(print(require(rpanel))==FALSE){install.packages(“rpanel”,repos=”http://cran.r-project.org”)}
if(print(require(rpanel))==TRUE){require(rpanel)}

if(print(require(gstat))==FALSE){install.packages(“gstat”,repos=”http://cran.r-project.org”)}
if(print(require(gstat))==TRUE){require(gstat)}

data<-read.table(tclvalue(tkgetOpenFile()),sep="",header=T)
coordinates(data)=~Lat+Lon

grid <- spsample(data, cellsize = 10, type = "regular")
gridded(grid) <- TRUE
##Variogram Fitting
variogram.plot <- function(panel) {
with(panel, {
variogram<-variogram(Oxigen~1,data,cutoff=Cutoff)
vgm.var<-vgm(psill=Sill,model=Model,range=Range,nugget=Nugget)
#fit<-fit.variogram(variogram,vgm.var)
plot(variogram$dist,variogram$gamma,xlab=”Distance”,ylab=”Gamma”)
lines(variogramLine(vgm.var,maxdist=Range*2))
})
panel
}

replot.smooth <- function(object) {
rp.tkrreplot(var.panel, plot)
object
}

var.panel <- rp.control("Variogram",Sill=20,Range=250,Nugget=0,Model="Mat",Cutoff=250,size=c(800,600))
rp.listbox(var.panel,Model,c(“Mat”,”Sph”,”Gau”))
rp.slider(var.panel, Cutoff, 0,sqrt(areaSpatialGrid(grid)),showvalue=T)
rp.slider(var.panel, Sill, 0,var(data$Oxigen)*2,showvalue=T)
rp.slider(var.panel, Range, 0,sqrt(areaSpatialGrid(grid)),showvalue=T)
rp.slider(var.panel, Nugget, 0,var(data$Oxigen),showvalue=T)
rp.button(var.panel, title = “Fit”,action=replot.smooth)
rp.tkrplot(var.panel, plot,variogram.plot)
rp.block(var.panel)

It probably need some more work in order to be perfect but at the moment I’m not interested in a perfect automatic script.
If someone has time to spend on it and is able to made it perfectly automatic for every data file, please share the script with the community.

By the way, I also implemented a line from the tcltk package for the selection of the data file interactively.
This is the resulting panel:

To leave a comment for the author, please follow the link and comment on their blog: R Video tutorial for Spatial Statistics.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Sponsors

Mango solutions



RStudio homepage



Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training



http://www.eoda.de









ODSC

CRC R books series













Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)