- 277 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
Generalized Estimating Equations
About this book
Generalized Estimating Equations, Second Edition updates the best-selling previous edition, which has been the standard text on the subject since it was published a decade ago. Combining theory and application, the text provides readers with a comprehensive discussion of GEE and related models. Numerous examples are employed throughout the text, al
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Yes, you can access Generalized Estimating Equations by James W. Hardin,Joseph M. Hilbe in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.
Information
R
CODE
FOR
SELECTED
OUTPUT
57
4
1
1
0
1
4
1
0
0
1
10.
A
conditional
fixed-effects
model
does
not,
in
general,
include
a
parameter
for
the
constant.
Show
that
the
conditional
fixed-effects
negative
binomial
model
does
allow
a
constant
in
the
model
and
discuss
why
this
is
so.
11.
What
is
a
pooled
estimator?
How
do
pooled
data
differ
from
panel
data?
12.
What
are
score
residuals?
13.
When
is
it
preference
to
estimate
a
model
using
quadrature
or
simulation?
14.
What
are
the
methods
used
for
estimation
of
GEE
models?
2.7
R
code
for
selected
output
Unconditional
fixed-effects
Poisson
(2.3.2.1)
#
rm(list=ls())
use
only
if
need
to
delete
all
objects
in
memory
load(ships)
#
ships
<-
read.table("c://work/gee2/book/ships.txt",
header=T)
attach(ships)
ships
<-
data.frame(ship,
t,
incident,
op_75_79,
co_65_69,
co_70_74,
co_75_79,
exposure,
mon)
save.image(file="c://work/gee2/book/ships.RData")
poissonufe
<-
glm(incident
~
factor(ship)
+
op_75_79
+
co_70_74
+
co_75_79
+
offset(log(mon)),
data=ships,
family=poisson)
summary(poissonufe)
detach(ships)
Unconditional
fixed-effects
logistic
(2.3.2.1)
#
rm(list=ls())
use
only
if
need
to
delete
all
objects
in
memory
load(wheeze)
#
wheeze
<-
read.table("c://work/gee2/book/wheeze.txt",
header=T)
attach(wheeze)
wheeze
<-
data.frame(case,
t,
wheeze,
kingston,
age,
smoke)
save.image(file="c://work/gee2/book/wheeze.RData")
logitufe
<-
glm(wheeze
~
kingston
+
age
+
smoke
+
factor(case),
data=wheeze,
family=binomial)
summary(logitufe)
detach(wheeze)
Table of contents
- Front Cover
- Contents
- Preface
- Chapter 1: Introduction
- Chapter 2: Model Construction and Estimating Equations
- Chapter 3: Generalized Estimating Equations
- Chapter 4: Residuals, Diagnostics, and Testing
- Chapter 5: Programs and Datasets
- References
- Back Cover