Multivariate generalized linear mixed models using R:
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton, Fla. [u.a.]
CRC Press
2011
|
| Series: | A Chapman & Hall book
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | XXIII, 280 S. |
| ISBN: | 9781439813263 |
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MARC
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| 245 | 1 | 0 | |a Multivariate generalized linear mixed models using R |c Damon M. Berridge ; Robert Crouchley |
| 264 | 1 | |a Boca Raton, Fla. [u.a.] |b CRC Press |c 2011 | |
| 300 | |a XXIII, 280 S. | ||
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Record in the Search Index
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| adam_text | Contents
List of Figures
xi
List of Tables
xiii
List of Applications
xv
List of
Datasets
xvii
Preface
xix
Acknowledgments
xxiii
1
Introduction
1
2
Generalized linear models for continuous/interval scale
data
9
2.1
Introduction
........................ 9
2.2
Continuous/interval scale data
............. 10
2.3
Simple and multiple linear regression models
..... 11
2.4
Checking assumptions in linear regression models
... 12
2.5
Likelihood: multiple linear regression
.......... 13
2.6
Comparing model likelihoods
.............. 14
2.7
Application of a multiple linear regression model
... 15
2.8
Exercises on linear models
................ 17
3
Generalized linear models for other types of data
21
3.1
Binary data
........................ 21
3.1.1
Introduction
.................... 21
3.1.2
Logistic regression
................. 22
3.1.3
Logit and
probit
transformations
........ 23
3.1.4
General logistic regression
............ 24
3.1.5
Likelihood
..................... 24
3.1.6
Example with binary data
............ 24
3.2
Ordinal data
....................... 26
3.2.1
Introduction
.................... 26
3.2.2
The ordered logit model
............. 27
3.2.3
Dichotomization of ordered categories
...... 29
3.2.4
Likelihood
..................... 29
3.2.5
Example with ordered data
........... 30
3.3
Count data
........................ 32
3.3.1
Introduction
.................... 32
3.3.2
Poisson
regression models
............ 33
3.3.3
Likelihood
..................... 34
3.3.4
Example with count data
............. 34
3.4
Exercises
......................... 37
4
Family of generalized linear models
43
4.1
Introduction
........................ 43
4.2
The linear model
..................... 44
4.3
The binary response model
............... 44
4.4
The
Poisson
model
.................... 46
4.5
Likelihood
......................... 46
5
Mixed models for continuous/interval scale data
49
5.1
Introduction
........................ 49
5.2
Linear mixed model
................... 49
5.3
The intraclass correlation coefficient
.......... 51
5.4
Parameter estimation by maximum likelihood
..... 53
5.5
Regression with level-two effects
............ 54
5.6
Two-level random intercept models
........... 55
5.7
General two-level models including random intercepts
56
5.8
Likelihood
......................... 58
5.9
Residuals
......................... 58
5.10
Checking assumptions in mixed models
........ 59
5.11
Comparing model likelihoods
.............. 60
5.12
Application of a two-level linear model
......... 61
5.13
Two-level growth models
................ 66
5.13.1
A two-level repeated measures model
...... 66
5.13.2
A linear growth model
.............. 66
5.13.3
A quadratic growth model
............ 67
5.14
Likelihood
......................... 67
5.15
Example using linear growth models
.......... 68
5.16
Exercises using mixed models for continuous/interval
scale data
......................... 69
6
Mixed models for binary data
75
6.1
Introduction
........................ 75
6.2
The two-level logistic model
............... 75
6.3
General two-level logistic models
............ 77
6.4
Intraclass correlation coefficient
............. 77
6.5
Likelihood
......................... 78
6.6
Example using binary data
............... 78
6.7
Exercises using mixed models for binary data
..... 81
7
Mixed models for ordinal data
85
7.1
Introduction
........................ 85
7.2
The two-level ordered logit model
............ 85
7.3
Likelihood
......................... 86
7.4
Example using mixed models for ordered data
..... 87
7.5
Exercises using mixed models for ordinal data
..... 90
8
Mixed models for count data
93
8.1
Introduction
........................ 93
8.2
The two-level
Poisson
model
............... 93
8.3
Likelihood
......................... 94
8.4
Example using mixed models for count data
...... 95
8.5
Exercises using mixed models for count data
..... 97
9
Family of two-level generalized linear models
99
9.1
Introduction
........................ 99
9.2
The mixed linear model
. . ............... 100
9.3
The mixed binary response model
........... 100
9.4
The mixed
Poisson
model
................ 102
9.5
Likelihood
......................... 102
10
Three-level generalized linear models
105
10.1
Introduction
........................ 105
10.2
Three-level random intercept models
.......... 105
10.3
Three-level generalized linear models
.......... 106
10.4
Linear models
....................... 107
10.5
Binary response models
................. 108
10.6
Likelihood
......................... 108
10.7
Example using three-level generalized linear models
. 109
10.8
Exercises using three-level generalized linear mixed
models
........................... 112
11
Models for multivariate data
115
11.1
Introduction
........................ 115
11.2
Multivariate two-level generalized linear model
.... 116
11.3
Bivariate Poisson
model: example
............ 117
11.4
Bivariate ordered response model: example
...... 121
11.5
Bivariate linear-probit model: example
......... 126
11.6
Multivariate two-level generalized linear model
likelihood
......................... 131
11.7
Exercises using multivariate generalized linear mixed
models
........................... 131
12
Models for duration and event history data
135
12.1
Introduction
........................ 135
12.1.1
Left censoring
................... 135
12.1.2
Right censoring
.................. 135
12.1.3
Time-varying explanatory variables
....... 136
12.1.4
Competing risks
.................. 136
12.2
Duration data in discrete time
............. 137
12.2.1
Single-level models for duration data
...... 137
12.2.2
Two-level models for duration data
....... 139
12.2.3
Three-level models for duration data
...... 140
12.3
Renewal data
....................... 143
12.3.1
Introduction
.................... 143
12.3.2
Example: renewal models
............. 145
12.4
Competing risk data
................... 147
12.4.1
Introduction
.................... 147
12.4.2
Likelihood
..................... 148
12.4.3
Example: competing risk data
.......... 150
12.5
Exercises using renewal and competing risks models
. 153
13
Stayers, non-susceptibles and endpoints
157
13.1
Introduction
........................ 157
13.2
Mover-stayer model
................... 157
13.3
Likelihood incorporating the mover-stayer model
. . . 160
13.4
Example
1:
stayers within count data
......... 161
13.5
Example
2:
stayers within binary data
......... 164
13.6
Exercises: stayers
..................... 166
14
Handling initial conditions/state dependence in binary
data
169
14.1
Introduction to key issues: heterogeneity, state
dependence and non-stationarity
............ 169
14.2
Example
.......................... 170
14.3
Random effects models
.................. 171
14.4
Initial conditions problem
................ 172
14.5 Initial
treatment
..................... 173
14.6
Example: depression data
................ 174
14.7
Classical conditional analysis
.............. 174
14.8
Classical conditional model: example
.......... 175
14.9
Conditioning on initial response but allowing random
effect
ito
j
to be dependent on
z j ............
176
14.10
Wooldridge conditional model: example
........ 177
14.11
Modelling the initial conditions
............. 178
14.12
Same random effect in the initial response and
subsequent response models with a common
scale parameter
...................... 179
14.13
Joint analysis with a common random effect: example
180
14.14
Same random effect in models of the initial response
and subsequent responses but with different
scale parameters
..................... 181
14.15
Joint analysis with a common random effect (different
scale parameters): example
............... 182
14.16
Different random effects in models of the initial response
and subsequent responses
................ 183
14.17
Different random effects: example
............ 184
14.18
Embedding the Wooldridge approach in joint models for
the initial response and subsequent responses
..... 185
14.19
Joint model incorporating the Wooldridge approach: ex¬
ample
........................... 187
14.20
Other link functions
................... 187
14.21
Exercises using models incorporating initial conditions/
state dependence in binary data
............ 188
15
Incidental parameters: an empirical comparison of fixed
effects and random effects models
195
15.1
Introduction
........................ 195
15.2
Fixed effects treatment of the two-level linear model
. 197
15.3
Dummy variable specification of the fixed effects
model
........................... 199
15.4
Empirical comparison of two-level fixed effects and
random effects estimators
................ 200
15.5
Implicit fixed effects estimator
............. 204
15.6
Random effects models
.................. 204
15.7
Comparing two-level fixed effects and random effects
models
........................... 208
15.8
Fixed effects treatment of the three-level linear
model
........................... 208
15.9
Exercises
comparing fixed effects and random effects
. 209
A SabreR installation, SabreR commands, quadrature,
estimation, endogenous effects
215
A.I SabreR installation
.................... 215
A.2 SabreR commands
.................... 215
A.
2.1
The arguments of the SabreR object
...... 215
A.2.
2
The anatomy of a SabreR command file
.... 216
A.3 Quadrature
........................ 218
A.3.1 Standard Gaussian quadrature
.......... 218
A.3.
2
Performance of Gaussian quadrature
...... 219
A.3.3 Adaptive quadrature
............... 221
A.4 Estimation
........................ 223
A.4.1 Maximizing the log likelihood of random effects
models
......................■ . 223
A.5 Fixed effects linear models
......·.......... 225
A.6 Endogenous and exogenous variables
.......... 226
В
Introduction to
R
for Sabre
229
B.I Getting started with
R
.................. 229
8.
1.1
Preliminaries
................... 229
В.
1.1.1
Working with
R
in interactive mode
. 229
B.I.
1.2
Basic functions
............. 231
B.I.
1.3
Getting help
............... 232
6.1.1.4
Stopping
R
............... 232
8.
1.2
Creating and manipulating data
......... 232
B.I.
2.1
Vectors and lists
............ 232
B.l.2.2 Vectors
................. 233
B.I.
2.3
Vector operations
............ 234
B.l.2.4 Lists
................... 235
B.I.
2.5
Data frames
............... 236
B.1.3 Session management
............... 237
B.I.
3.1
Managing objects
............ 237
B.I.
3.2
Attaching and detaching objects
. . . 237
B.I.
3.3
Serialization
............... 238
B.l.3.4
R
scripts
................. 238
B.I.
3.5
Batch processing
............ 239
B.1.4
R
packages
..................... 239
6.1.4.1
Loading a package into
R
....... 239
8.
1.4.2
Installing a package for use in
R
... 239
8.
1.4.3
R
and Statistics
............. 240
В.
2
Data preparation for SabreR
.............. 240
В.
2.1
Creation of dummy variables
........... 240
B.2.2 Missing values
................... 243
B.2.3 Creating lagged response covariate data
.... 245
References
249
Author Index
259
Subject Index
263
Multivariate
Generalized Linear Mixed Models Using
R
presents
robust and methodologically sound models for analyzing large and
complex data sets., enabling readers to answer increasingly complex
research questions. The book applies the principles of modeling
to longitudinal data from panel and related studies via the Sabre
software package in R.
The authors first discuss members of the family of generalized linear
models, gradually adding complexity to the modeling framework by
incorporating random effects. After reviewing the generalized linear
model notation, they illustrate a range of random effects models,
including three-level, multivariate, endpoint, event history, and state
dependence models. They estimate the multivariate generalized
linear mixed models
(
M G L M
Ms) using either standard or adaptive
Gaussian quadrature. The authors also compare two-level fixed and
random effects linear models. The appendices contain additional
information on quadrature, model estimation, and endogenous
variables, along with SabreR commands and examples.
In medical and social science research, MGLMMs help disentangle
state dependence from incidental parameters. Focusing on these
sophisticated data analysis techniques, this book explains the
statistical theory and modeling involved in longitudinal studies. Many
examples throughout the text illustrate the analysis of real-world
data sets. Exercises, solutions, and other material are available on a
supporting website.
|
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| building | Verbundindex |
| bvnumber | BV037237002 |
| classification_rvk | QH 234 SK 850 |
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| discipline | Informatik Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV037237002 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T22:54:08Z |
| institution | BVB |
| isbn | 9781439813263 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-021150567 |
| oclc_num | 711828216 |
| open_access_boolean | |
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| owner_facet | DE-M49 DE-BY-TUM DE-91G DE-BY-TUM DE-824 DE-19 DE-BY-UBM DE-473 DE-BY-UBG DE-739 |
| physical | XXIII, 280 S. |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | CRC Press |
| record_format | marc |
| series2 | A Chapman & Hall book |
| spelling | Berridge, Damon M. Verfasser aut Multivariate generalized linear mixed models using R Damon M. Berridge ; Robert Crouchley Boca Raton, Fla. [u.a.] CRC Press 2011 XXIII, 280 S. txt rdacontent n rdamedia nc rdacarrier A Chapman & Hall book Lineares Regressionsmodell (DE-588)4127971-2 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf R Programm (DE-588)4705956-4 s Multivariate Analyse (DE-588)4040708-1 s Lineares Regressionsmodell (DE-588)4127971-2 s b DE-604 Crouchley, Robert Verfasser (DE-588)170719537 aut Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021150567&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021150567&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Berridge, Damon M. Crouchley, Robert Multivariate generalized linear mixed models using R Lineares Regressionsmodell (DE-588)4127971-2 gnd R Programm (DE-588)4705956-4 gnd Multivariate Analyse (DE-588)4040708-1 gnd |
| subject_GND | (DE-588)4127971-2 (DE-588)4705956-4 (DE-588)4040708-1 |
| title | Multivariate generalized linear mixed models using R |
| title_auth | Multivariate generalized linear mixed models using R |
| title_exact_search | Multivariate generalized linear mixed models using R |
| title_full | Multivariate generalized linear mixed models using R Damon M. Berridge ; Robert Crouchley |
| title_fullStr | Multivariate generalized linear mixed models using R Damon M. Berridge ; Robert Crouchley |
| title_full_unstemmed | Multivariate generalized linear mixed models using R Damon M. Berridge ; Robert Crouchley |
| title_short | Multivariate generalized linear mixed models using R |
| title_sort | multivariate generalized linear mixed models using r |
| topic | Lineares Regressionsmodell (DE-588)4127971-2 gnd R Programm (DE-588)4705956-4 gnd Multivariate Analyse (DE-588)4040708-1 gnd |
| topic_facet | Lineares Regressionsmodell R Programm Multivariate Analyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021150567&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021150567&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT berridgedamonm multivariategeneralizedlinearmixedmodelsusingr AT crouchleyrobert multivariategeneralizedlinearmixedmodelsusingr |