Mapping the spatial effects

As with glm, you can get the fitted values and residuals from your model using the fitted and residuals functions. bayesx models are a bit more complex, since you have the linear predictor and terms from sx elements, such as the spatially correlated term.

The summary function will show you information for the linear model terms and the smoothing terms in two separate tables. The spatial term is called "sx(i):mrf" - standing for "Markov Random Field".

Bayesian analysis returns samples from a distribution for our S(x) term at each of the London boroughs. The fitted function from bayesx models returns summary statistics for each borough. You'll just look at the mean of that distribution for now.

The model from the BayesX output is available as flu_spatial.

Get a summary of the model and see where the parameter information is. Does the Health Deprivation parameter look significant?
Add a column named spatial to london with the mean of the distribution of the fitted spatial term, and map this.
Add another column, named spatial_resid, with the residuals.
- Use the "mu" column from residuals, as this is based on the rate rather than the number of cases, so can be compared across areas with different populations.
Plot a map of spatial_resid using spplot().
Run a Moran statistic Monte-Carlo test on the residuals - do they show spatial correlation?
- Call moran.mc(), passing the residual vector as the first argument.

Take Hint (-30 XP)

Exercise

Mapping the spatial effects

Instructions