Samuel D. Oman


Department of Statistics
Hebrew University of Jerusalem
Mt. Scopus, Jerusalem, ISRAEL
Phone: +972-2-588-3442 
Fax: +972-2-588-3549
Email: oman@mscc.huji.ac.il




Explaining the Bootstrap

IBEE Computer Code

52542 Generalized Linear Models

     IBEE

   The following two files describe and contain the S-plus code used to compute the IBEE
and Independence estimates for the Meron vegetation data in "Analyzing Spatially
Distributed Binary Data Using Independent-Block Estimating Equations" by Oman,
Landsman, Carmel and Kadmon.
    The code may be used for binary responses from an equally-spaced rectangular grid,
and uses an exponential covariance function for the latent normal field (it may be
easily adapted to handle other types of covariance functions).

    The S-plus statements are in a number of programs ( ibee.1 - ibee.6) which are to be
run sequentially.  Before running them, the user must prepare data matrices and define
a number of parameters, as described in the comments of  ibee.1.  The programs use a
number of functions, whose code is also available.

ibee.readme.rtf    Contains a brief description of the programs, the names of the functions,
                            and the calling sequence for the functions.

ibee.progs.rtf    Contains the code for ibee.1 - ibee.6 and the functions, as one long file. 
                        The first line of each program or function is in boldface, and the last line
                        is followed by a blank line, in order to simplify copying and pasting into
                        the Splus window.  The programs and functions have numerous
                       comments.


Generalized Linear Models

Week 1
syllabus.pdf                   Syllabus for the course.
paper.guidelines.pdf      Guidelines for writing the term paper analyzing data from the three sets below.
glim.formulas.pdf          Some formulas we'll be using.

birthwt.desc.pdf            Description of data set on possible causes for low fetal birth weight.
shock.data.desc.pdf      Description of survival data from shock research unit.
cardiac.output.pdf        Article describing changes in heart function with age.
rat.desc.pdf                   Description of data set on rat-sightings in Madrid.

notes.1.pdf                    Notes for the first lecture.


Week 2
medfly.data.tab.txt         Medfly data set; tab-delimited and contains variable names. 
meron.pdf                     Description of data set on vegetation growth in Mt Meron.
meron.200.glim.txt       Mt Meron data set.
meron.preliminary.pdf  Preliminary analysis of Meron data.

mismatch.pdf                 Description of data set on survival time following heart transplant.
mismatch.data.txt          Data on survival time following heart transplant.

references.pdf               Additional references for the course.
notes.2.pdf                    Notes for the second lecture.s

Week 3
exp.family.proof.pdf              Proof of definition of (mean, var) in terms of natural parameters of exponential family
glim.newton.raphson.pdf      Newton-Raphson algorithm for a GLIM        
medfly.pdf                            Description of dataset on Medfly trappings.
input.txt.file.code.R              R code to input data copied from a table in .txt.
medfly.explore.R                  R code for preliminary analysis of medfly data.
medfly.preliminary.pdf          Results of preliminary analysis of medfly data.
medfly.poisson.rslts.pdf       Results of Poisson regression for medfly data.
references.weeks.1-3.pdf    Recommended reading for weeks 1 - 3.
notes.3.pdf                           Notes for the third lecture.

Week 4
meron.glim.fit.R                           Code to fit logistic and probit regressions to Meron data.
meron.glim.fit.pdf                        The results.
meron.interaction.graphs.pdf       Graphs showing the effects of slope x aspect interaction for the Meron data.
notes.4.pdf                                    Notes for the fourth lecture.

Week 5
glim.mismatch.fit.R                  Code to analyze the mismatch data as a gamma response.
mismatch.gamma.1.pdf            Results of the analysis.
convolution.pdf                        The definition of the convolution of two functions.
notes.5.pdf                                Notes for the fifth lecture.