Samuel D. Oman

Department of Statistics
Hebrew University of Jerusalem
Mt. Scopus, Jerusalem, ISRAEL
Phone: +972-2-588-3442 
Fax: +972-2-588-3549

Explaining the Bootstrap

IBEE Computer Code

52542 Generalized Linear Models


   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

Generalized Linear Models

Week 1
syllabus.pdf                   Course syllabus.
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.      Description of survival data from shock research unit.
rat.desc.pdf                   Description of data set on rat-sightings in Madrid.

notes.1.pdf                    Notes for the first lecture.

Week 2
medfly.pdf                     Description of data set on medfly trappings.         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.

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

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

Week 3
medfly.preliminary.pdf         Preliminary analysis of medfly data.
medfly.explore.R                  R code for preliminary analysis of medfly data.
medfly.poisson.rslts.pdf       Results of Poisson regression for medfly data.                   Mean and variance of exponential family distribution in terms of natural parameter
notes.3.pdf                            Notes for the third lecture.

Week 4
meron.preliminary.pdf                  Preliminary analysis of Meron data.                           Code to fit logistic and probit regressions to Meron data.                        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                  Code to analyze the mismatch data as a gamma response.
mismatch.gamma.1.pdf            Results of the analysis.
notes.5.pdf                                Notes for the fifth lecture.

Week 6
more.formulas.pdf            Some more formulas.               Graphical comparison of likelihood ratio, Wald and score tests.
notes.6.pdf                        Notes for the sixth lecture.

Week 7
hypoth.test.examples.R     Code to test some hypotheses for Meron and mismatch data.
hypoth.test.rslts.pdf           The results.
aic.examples.R                   Code to compare models using AIC.
aic.examples.pdf                The results.
notes.7.pdf                         Notes for the seventh lecture.

Week 8                              Lesson cancelled

Week 9
phi.hat.examples.R                     Code to estimate the (over-) dispersion parameter for some examples.
overdispersion.examples.pdf      The results.
medfly.quasi.R                            Code to fit the medfly data using quasi-likelihood.
medfly.quasi.rslts.pdf                 The results.
notes.9a.pdf                                Notes for the ninth lecture.

Week 10

mismatch.lm.diagnostics.R              Code to compute diagnostics for linear regression fit to mismatch data.
mismatch.glm.diagnostics.R            Code to do the same for GLIM fit.
mismatch.diagnostics.rslts.1.pdf      The results.
mismatch.diagnostics.rslts.2.pdf      The results.
meron.logistic.probit.rslts.3             Results of logistic and probit fits to Meron data.
glim.meron.phi.diagnostics.4.R       Code to compute diagnostics for probit fit to Meron data.
meron.probit.diagnostics.rslts.4       The results.
notes.10.pdf                                      Notes for the tenth lecture.

Week 11             A presentation on a method to analyze spatially dependent binary responses.

Week 12
notes.12.pdf                                      Notes for the twelfth lecture.

Options for submitting the (hard-copy) term paper:
        - Give it to me in my office (if I'm there).
        - Put it in my mailbox.
        - Hand it in to Clare.