Spatial correlograms are great to examine patterns of spatial autocorrelation in your data. They show how correlated are pairs of spatial observations when you increase the distance (lag) between them – they are plots of some index of autocorrelation (Moran’s I or Geary’s c) against distance. Although correlograms are not as fundamental as variograms (a keystone concept of geostatistics), they are very useful as an exploratory and descriptive tool.
I use ncf as my default package in R to evaluate patterns where the spacial analysis does what I want for my scenarios. In picking up a good college for my children for example something like the analysis below can give clues in betting on more favorable odds than just pure word of mouth.
With such a basis of knowledge, you can imagine my surprise when this morning I discovered that we have a bug for over 15 years in the statistical analysis we use for brain scans! The disturbing bit is
The researchers used published fMRI results, and along the way they swipe the fMRI community for their “lamentable archiving and data-sharing practices” that prevent most of the discipline’s body of work being re-analysed. ®
Simply put: some science told the rest of us that when your brain was farting was because of some pretty picture generated by some smart math. The math was right, the code to implement that math, was wrong and you took remedy or actions to prevent that gas-thought syndrome of yours when in reality the problem was shown elsewhere, in your brain scan.
Now…what if all of the sudden this theory because more probable?!