You computed this over a very restricted (space time) range.
A large scale trend in the data could lead to a strong difference
between semivariances and sample variance; think of evaluating the
variogram of z=x+y, where x and y both range from 0 to 1000, and the
varioram is evaluated up to distance 10.
On 06/14/2012 05:47 PM, Rawlins, Barry G. wrote:
Hello
The variograms function in gstat is returning semi-variance estimates
which appear to be two orders of magnitude lower than what I expect from
calculating the variance of the data in the attached space time object
created with the STFDF function:
library(gstat)
library(spacetime)
breaks<-seq(0.5,12.5,1)
var_all<- variogram(VMC~1,sp_time_var, bounds=breaks, cutoff = 12.5) #
VMC is the numeric data to calculate a sp-time varigram
plot(var_all) # note the low values ranges on the scale<0.15
var(sp_time_var at data$VMC) # variance is 11.5
So I would expect the variance in the upper part of the graph to
approach a value around 11.
Have I mis-specified the variogram function? I know from gstat that one
had to specify a gstat object, but here I thought specifying the
spacetime object was the right way to proceed and that VMC~1 was the
correct syntax?
Any advice much appreciated.
Barry
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