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Thank you so much! I have used Box-cox transformation. I need to sure
before submitting my paper for publication. <br>
I used following codes for CSGS of ground water arsenic concentration.<br>
<br>
Zia<br>
<br>
<font face="Courier New, Courier, monospace">data(tala)</font><br>
<br>
<small><font face="Courier New, Courier, monospace">was.bc<-box.cox(tala$was,
0.47) <br>
tala.bc = cbind(tala, was.bc)<br>
<br>
coordinates(tala.bc) <- ~ x + y <br>
coordinates(tala.grid) <-~ x + y <br>
<br>
v.ok.was<-variogram(was.bc~1,data=tala.bc)<br>
plot(v.ok.was, pl=F, pch=20, cex=1, col="Black")<br>
m.ok.was<-vgm(.06,"Exp",4000,0.02)<br>
(m.ok.f.was<-fit.variogram(v.ok.was, m.ok.was))<br>
<br>
# Simulation<br>
#--------------<br>
set.seed(130)<br>
sim.was<-krige(was.bc~1,tala.bc,tala.grid,model=m.ok.f.was, nmax=50,
nsim = 1000)<br>
<br>
# Back-transformation<br>
#-----------------<br>
for(i in 1:length(sim.was@data)){sim.was@data[[i]] <-
(((sim.was[[i]]*0.47+1)^k.was))}<br>
sim.data.was<-as.data.frame(sim.was)<br>
summary(sim.data.was)</font></small><br>
<br>
<br>
<br>
<br>
Edzer Pebesma wrote:
<blockquote cite="mid:4B9DDB3A.20207@uni-muenster.de" type="cite">
<pre wrap="">Yes, you can. It is less flexible than the normal score transform -- try
for instance transforming a bimodal distribution -- but has the
advantage that the back-transform is straightforward. How do you for
example use the inverse normal score transformation simulated values
outside the range of the data?
Zia Ahmed wrote:
</pre>
<blockquote type="cite">
<pre wrap="">I have a basic question related to the data transformation CSG
simulation. Can I use Box-cox transformation of the data instead of
normal score transformation before performing CSG simulation?
Suggestion will be appreciated.
Thanks
ZIa
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<pre wrap=""><!---->
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