Bayesian Prediction with High-order Interactions

Longhai Li, Department of Mathematics and Statistics, University of Saskatchewan

Description

This R package is used in two situations. The first is to predict the next outcome based on the previous states of a discrete sequence. The second is to classify a discrete response based on a number of discrete covariates. In both situations, we use Bayesian logistic regression models that consider the high-order interactions. The time arising from using high-order interactions is reduced greatly by our compression technique that represents a group of original parameters as a single one in MCMC step. In this version, we use log-normal prior for the hyperparameters. When it is used for the second situation --- classification, we consider the full set of interaction patterns up to a specified order.

Copyright

Permission is granted for anyone to copy, use, modify, or distribute these programs and accompanying documents for any purpose, provided this copyright notice is retained and prominently displayed, and note is made of any changes made to these programs. These programs and documents are distributed without any warranty, express or implied. As the programs were written for research purposes only, they have not been tested to the degree that would be advisable in any important application. All use of these programs is entirely at the user's own risk.

If you found that this software is useful in your work, please do not hesitate to cite the software or the papers below.

Documentations and Source Packages

  1. BPHO_1.3-0, released 23/06/2009, Documentation: BPHO_1.3-0.pdf, source package: BPHO_1.3-0.tar.gz

    An important note to version earlier than 1.3

    Because I read the whole Markov chain of 'betas' as needed in prediction into memory as a single static array, it may exceed the stack limit of your system, resulting error "C stack overflow". In unix or linux system, a quick way to solve this problem is to relax the stack limit before starting R, by issuing the following command in terminal:

    ulimit -s unlimited

  2. BPHO_1.2-5, released 07/04/2008, Documentation: BPHO_1.2-5.pdf, source package: BPHO_1.2-5.tar.gz
  3. BPHO_1.2-4, released 02/04/2008, Documentation: BPHO_1.2-4.pdf, source package: BPHO_1.2-4.tar.gz
  4. BPHO_1.2-3, released 22/02/2008, Documentation: BPHO_1.2-3.pdf, source package: BPHO_1.2-3.tar.gz

References

The method used in this software is discussed in details in the following papers:

  • Li, L., and Neal, R. M. (2007+), Compressing Parameters in Bayesian High-order Models with Applications to Sequence Models: abstract

  • Li, L. (2007), Bayesian Classification and Regression with High Dimensional Features: abstract

    Instruction of Installing an R package

    This is an R add-on package. R is a free software environment for statistical computing and graphics, available from http://www.r-project.org/. You need to install R and add this package.

    Install

    Method 1: Install from source

    R instruction for installing from source package can be found by typing ?INSTALL in R console. Simply, you need to do the following:

    Download the add-on R package, say mypkg, and type the following command in Unix console to install it to /my/own/R-packages/:

    $ R CMD INSTALL mypkg -l /my/own/R-packages/

    Method 2: Install from CRAN directly

    Type the following command in R console to install it to /my/own/R-packages/ directly from CRAN:

    > install.packages("mypkg", lib="/my/own/R-packages/")

    Load the library

    Type the following command in R console to load the package

    > library("mypkg", lib.loc="/my/own/R-packages/")

    Instruction of using R

    1. Official R manuals
    2. A short R tutorial with examples (PDF, 19 pages) by myself
    Back to Longhai Li's software packages