I am disappointed by the lack of documentation walking users step-by-step through Geostatistical Wizard. Is the correct sequence to follow?

1. Select model(s) for best fit of semivariogram of Var1 - Var1 and then optimize that model(s)

2. Select model(s) for best fit of covariance graph of Var1 - Var2 and then optimize this model, or does this somehow overwrite the model you selected and optimized for Var1 - Var1?

3. Select model(s) for best fit of semivariogram of Var2 - Var2 and then optimize that model(s), or does this somehow overwrite the models you selected and optimized for the other variable combinations above?

4. Proceed to searching neighborhood and cross validation steps

Can you reoptimize for a different model if the semivariogram looks horrible after your first optimization, or did the first optimization already distort the data such that you should close out of Geostatistical Wizard and start again from scratch?

How can you fit a model to a negative correlation between the two variables? All the model options create curves of positive correlations only, unless there is a setting for this that I am missing?

Eric Krause or anyone else knowledgeable about Geostatistical Wizard, could you help me? Thanks in advance for any help that you can provide.

The optimize button does not do a complete optimization. For example, the choice of semivariogram model is not optimized. The default model is "Stable," and the optimize model button will find the range, nugget, and sill that minimize the RMS for the Stable model. If you change, for example, to K-Bessel, pressing the optimize model button will find different optimal parameters for the K-Bessel model, which may or may not be better than the optimal Stable parameters. But again, the model with the lowest RMS is not necessarily the best model; there are lots of other diagnostics that you should pay attention to. If those other diagnostics do not look good, the model may need some manual changes.

If you want to manually specify the parameters for the three models, you change them directly with the parameters on the right side of the wizard. Changing Var1 - Var1 to Var2 - Var2, for example, just changes which graph you are looking at, but you can control the parameters of all three models no matter which one you are currently looking at.

You don't need to restart the Wizard after each change. Hitting the optimize model button will set all parameters to their optimal values, no matter what their current values happen to be. If you want to get back to the original default values (before you pressed optimize), you either need to restart or click Back a couple times, then Next a couple times, and they will revert to their non-optimal defaults.

With regards to Figure 11 in your link, you're looking at a covariance view of the semivariogram. You can get your graph to look like this by changing the "Variable" setting on the top-right of the wizard to "Covariance". This just just a different view of the same thing. Instead of making a graph of squared differences (the semivariogram), it makes a graph of covariances, and the idea is that points that are close together are more correlated (ie, have a larger covariance) than points that are further apart. After a particular distance (the range), the covariance becomes 0, which means that points that are further apart than the range are considered spatially independent. You'll also notice that the blue covariance curve never goes below zero, which is what a true "negative covariance model" would look like.

The general idea is that the semivariogram plots how different points are, and the covariance view plots how similar they are. So, points that are farther away will have larger semivariances, but they will have lower covariances.