[R-sig-Geo] Basic questions about Bayesian Spatio-temporal Analysis-INLA

[Quiner, Claire]

Greetings,
I am in the process of trying to teach myself how to perform a Bayesian spatio-temporal analysis using INLA in R. I am reading papers and following a number of tutorials but there is one, somewhat basic thing that I can't seem to figure out from my readings.
I have a series of raster stacks of a variety of climatic data, each layer of a stack represents the value from a week in a year. These data will become the prior distributions in my analysis, as I understand it. I was originally under the impression that INLA would read these raster files but I see that the program actually requires tabular data. I can easily transform these raster stacks, getting summary values over each county, by week. However, it is unclear to me what part of the analysis that I should do that in. I would like to prepare a correlation matrix to address multicollinearity, followed by PCA to further eliminate redundant variables. My understanding is that both of these analyses can be done on either raster files or from tabular data. For these preliminary analyses to Bayesian analysis, should I opt for spatial data? Also, how do I handle the temporal nature of this data, which will obviously be correlated, but still may be necessary to maintain?
Any advice would be appreciated.
Thank you,

Claire


	[[alternative HTML version deleted]]