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                    Course syllabus.

medfly.docx                   Description of data set on medfly trappings.
medfly.data.tab.txt         Medfly data set; tab-delimited and contains variable names. 

meron.docx                   Description of data set on vegetation growth in Mt Meron.
meron.200.glim.txt       Mt Meron data set.

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

paper.guidelines.pdf      Guidelines for writing the term paper analyzing data from the three sets below.

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.
rat.desc.pdf                   Description of data set on rat-sightings in Madrid.

notes.1.pdf                    Notes for the first lecture.

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

Week 3
glim.formulas.pdf                 Some formulas we'll be using.

medfly.lin.reg.pdf                 Linear regression for log-transformed medfly data.
medfly.explore.R                  R code for preliminary analysis of medfly data.
medfly.preliminary.pdf         Preliminary analysis of medfly data.

meron.explore.R                   R code for preliminary analysis of Meron data.
meron.preliminary.pdf          Preliminary analysis of Meron data.

notes.3.pdf                            Notes for the third lecture.
exercise.1.doc                       An exercise illustrating the Newton-Raphson algorithm for a simple
                                                    example of Poisson regression.
exercise.1.soln.pdf                The solution.

Week 4
meron.glim.fit.R                 Code to fit logistic and probit regressions to Meron data.
meron.glim.fit.pdf              The results.
notes.4.pdf                          Notes for the fourth lecture.

Week 5
meron.interaction.graphs.pdf     Graphs showing the effects of slope x aspect interaction for the Meron data.
notes.5.pdf                                  Notes for the fifth lecture.

Week 6
glim.mismatch.fit.R             Code to analyze the mismatch data as a gamma response.
mismatch.gamma.1.pdf       Results of the analysis.
digamma.pdf                       Graphs with the digamma function.
notes.6.pdf                           Notes for the sixth lecture.

Week 7
more.formulas.pdf            Some more formulas.
wald.score.lr.doc               Graphical comparison of likelihood ratio, Wald and score tests.
hypoth.test.examples.R     Code to test some hypotheses for Meron and mismatch data.
hypoth.test.rslts.pdf           The results.
notes.7.pdf                         Notes for the seventh lecture.

Week 8
aic.examples.R           Code to compare models using AIC.
aic.examples.pdf         The results.
notes.8.pdf                   Notes for the eighth lecture.

Week 9
notes.9.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.
notes.10.pdf                                      Notes for the tenth lecture.

Week 11
    The last day to hand in the paper is now 4 March 2018.

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.11.pdf                                Notes for the eleventh lecture.

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

Week 13
               Room change:    
Next week's lesson will take place in the Stat Dept seminar room, 4412.
creatinine.description.pdf    Description of data set and research questions for creatinine clearance.
creatinine.variables.pdf       Concise description of variables in creatinine data set.
creatinine.article.pdf            An article describing the analysis of the data using a mixed linear model.
creatinine.results.pdf            Results of fitting a logistic regression with random effects to part of the data.