spatcontrol

This package aims at exploring the determinants of spatial data. It focuses on complex spatial patterns: influence of known barriers on the dispersion/spatial autocorrelation, multiple spatial scales, etc...

Installation:

Simply source spatcontrol.R to use the spatial analysis functions:

source("spatcontrol.R")

It can ask for a bunch of R packages to be installed.

In addition, substantial computation time can be saved when using large datasets by using the attached C code:

R CMD SHLIB spatcontrol.c

More systematic use can be done by using the sec_launch.sh command:

chmod +x sec_launch.sh

Then you should use the example out of spatcontrol.c in a lower nivel than spatcontrol, calling it in the code (eg:mycode.R):

source("spatcontrol/spatcontrol.R,chdir=TRUE)

Then you can run a backed up simulation using: spatcontrol/sec_launch.sh mycode.R

Main functionalities and examples:

Functions are in spatcontrol.R. Example of the use of the main functionalities are given the in the example_* files:

• how to generate spatially autocorrelated data in example_generation.R with gen.map
• how to compute structured autocorrelograms to examine the impact of known barriers on presence absence data in example_structuredMI.R.
• fit a gaussian field while accounting for known barriers, cofactors and observers quality in the fit in example_fit_GMRF.R The parameters for the priors are configured in parameters_extrapol.r

Other utility functions can be found in spatcontrol.R, organized into chapters:

• General purpose functions (data management, sparse matrices handling, plotting)
• Functions specific to the structured autocorrelograms
• Map generation
• Functions specific to the GMRF

Credits:

This package is maintained by Corentin Barbu currently at University of Pennsylvania in MZ Levy lab.

The spatcontrol package is under development at the github repository: https://github.com/cbarbu/spatcontrol

Participation is welcome through forking and pull request in GitHub. The code will at some point be shared in part or fully as a CRAN package.

Todo:

-Set more reasonable priors on io mean, io variance -Remove intercept from calculations carefully -Figure out where/what muPrior is doing (see parameters_extrapol.R) - and try to remove it (along with removing intercept)