Spatial Statistics Lab 1
1. Get the built-in data set parana in geoR. Do an exploratory analysis
of the data, compute an empirical variogram, and test for significant
spatial correlation.
Useful functions:
summary
plot
points
variog
variog.mc
2. Fit a family of variogram models to the data. You decide which
family to use. Set up a prediction grid, krige the data, display the
fit and its standard error.
Useful functions:
variofit
likfit
expand.grid
krige.control
krige.conv
image
contour
3. Compare the variograms from the fit in question 2 to those
obtained using Cressie's robust variogram estimator
(estimator.type="modulus" in variog), and a weighted least squares fit
(using wei="cressie" in variofit).
4. You may have noticed a large spatial trend in question 1. What
influence, if any, does this trend to have on the variogram fit?
Consider two trend models (linear in coordinates, or quadratic in
coordinates), Use an ordinary linear model to fit and compute residuals
and variograms in both cases. Which model do you think is most
appropriate?
Compare the ordinary kriging surface from question 2 to one computed
using universal kriging (see documentation for type.krige in the
function krige.conv to figure out how to do this).
Useful function:
lm
Some other functions you may find useful for this lab:
variog4
variog.model.env
plot.variogram
lines.variogram
lines.variomodel.likGRF
lines.variomodel