R Code and Tutorials

R is a free, open-source platform for statistical computing. It is powerful and constantly growing. It can be frustrating to learn, but is well worth the effort. See the R site for downloads and documentation.

Lora Murphy and Charlie Canham have assembled two packages of R code to facilitate likelihood analyses.

The Likelihood Package contains a set of functions for maximum likelihood estimation using simulated annealing, a global optimization routine. The implementation of simulated annealing is adapted from Goffe et al. (1994), and allows bounded searches.

Likelihood Package zip
Right-click and choose "Save Target As"
(or choose save from the dialogue box that opens with a regular click)
and download the zip file to your machine. NOTE: Do not unzip the file.

The NeighLikeli Package contains functions for likelihood analyses of neighborhood processes (i.e. Canham and Uriarte 2006), in which the function for the predicted response variable contains terms that sum over the effect of a set of neighbors (whose attributes and locations are contained within the data frame).

NeighLikeli Package zip
Right-click and choose "Save Target As"
(or choose save from the dialogue box that opens with a regular click)
and download the zip file to your machine. NOTE: Do not unzip the file.

Installing the Packages: Download the package zip file to a directory on your hard disk. Do not UnZip the file. In R, use the "Install package(s) from local zip file…" option under the Packages main menu to install the package before first use. Then remember to "load" the package in any work session when you want to use it.

For more details, see the section on Package Installation in a brief introduction to R we put together for the course.

References:

• Canham, C. D. and M. Uriarte. 2006. Analysis of neighborhood dynamics of forest ecosystems using likelihood methods and modeling. Ecological Applications 16:62-73.
• Goffe, W. L., G. D. Ferrier, and J. Rogers. 1994. Global optimization of statistical functions with simulated annealing. Journal of Econometrics 60: 65-99.