Hebrew University of Jerusalem
Mt. Scopus, Jerusalem, ISRAEL
Explaining the Bootstrap
IBEE Computer Code
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.
the code for ibee.1 - ibee.6 and the functions, as one long
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
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.
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.
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 for the first lecture.
references.pdf Additional references for the course.
notes.2.pdf Notes for the second lecture.
glim.formulas.pdf Some formulas we'll be using.
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.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.
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.
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.
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.
aic.examples.R Code to compare models using AIC.
aic.examples.pdf The results.
notes.8.pdf Notes for the eighth lecture.
notes.9.pdf Notes for the ninth lecture.
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.
The last day to hand in the paper is now 4 March 2018.
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.
notes.12.pdf Notes for the twelfth lecture.
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.