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A group of geostatistical techniques to interpolate the value of a random field at an unobserved location from observations of its value at nearby locations.
8
votes
3
answers
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Backtransformation of kriging predictions and variances
This example using the "meuse" data shows how to make variogram and use it to get kriging predictions (and variances) using the popular 'gstat' package of R. … meuse.grid)
m <- vgm(0.59,"Sph", 897, 0.05)
plot(v$dist, v$gamma, ylim=c(0,0.75), xlim=c(0,1600), xaxs="i", yaxs="i")
lines(variogramLine(m, maxdist=1600), lwd=2, lty=2, col=4)
# do the prediction by kriging …
1
vote
0
answers
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Determining covariance of irregularly spaced spatial data in R?
region at two time point 2000 and 2010 with sample size of N_2000 = 51 and N_2010 = 26 (not all the samples are from the same location), mean of mu(C)_2000 = 47 and mu(C)_2010 = 27 (determined by block kriging … of all point observations using the 'gstat' package of R) and variance of V(C)_2000 = 89 and V(C)_2010 = 68 (kriging variance). …