Computational statistics with R:
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Amsterdam [u.a.]
Elsevier
2014
|
| Series: | Handbook of statistics
32 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturangaben |
| Physical Description: | XVII, 394 S. Ill., graph. Darst., Kt. |
| ISBN: | 9780444634313 |
Staff View
MARC
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| 245 | 1 | 0 | |a Computational statistics with R |c ed. by Marepalli B. Rao ; C. R. Rao |
| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier |c 2014 | |
| 300 | |a XVII, 394 S. |b Ill., graph. Darst., Kt. | ||
| 336 | |b txt |2 rdacontent | ||
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Record in the Search Index
| _version_ | 1804153106157535232 |
|---|---|
| adam_text | Contents
Contributors
xiii
Preface
xv
1.
Introduction to
R
1
Chai
tra
H. N
agaraja
1
Introduction
1
2
Setting Up
R
4
2.1
Installing and Starting
R
4
2.2
Memory
10
2.3
Saving Your Code and Workspace
1 1
2.4
R
Packages
13
3
Basic
R
Objects and Commands
14
3.1
Numbers, Character Strings, and Logicals
14
3.2
Scatars, Vectors, Matrices, and Arrays
15
3.3
Data Frames and Lists
17
3.4
Strings and Factors
18
4
Writing Programs
19
4.1
Conditional Statements
19
4.2
i f /e
I
se
Statements
20
4.3
for Loops
20
4.4
whi I e Loops
21
4.5
Functions
23
4.6
Debugging and Efficiency
27
5
Input and Output
31
6
Data Processing
32
7
Exploratory Data Analysis
35
8
Statistical Inference and Modeling
36
8.1
Hypothesis Testing
36
8.2
Regression
37
9
Simulation
42
10
Numerical Techniques
45
11
Annotated References
47
Set Up
47
Text Editors
47
Introductory Resources and Books
48
Contents
VI
2.
R
Graphics 49
Deepayan Sarkar
49
1
Introduction
1.1
Origins 49
1.2
Principles or Data Graphics 51
2
Traditional Graphics 51
2.1
The pi ot(
)
Function 54
2.2
Other Common High-Level Functions
56
2.3
Visualizations for Time Series Data
62
2.4
Customizing Plots Using Low-Level Functions
63
2.5
Limitations of Traditional Graphics
66
3
Grid Graphics
69
3.1
Viewports
70
3.2
Units and Primitives
70
3.3
First Atlempt
71
4
Lattice
74
4.1
Overview
74
4.2
Common High-Level Functions
78
4.3
Bar Charts and Dot Plots for Tabular Data
79
4.4
Scatterplots and Custom Displays
83
4.5
The trellis Object
84
5
ggplot
85
6
Further Reading
88
References
91
3.
Graphics Miscellanea
93
Pa/ash Mallick and Marepalli B. Rao
1
Introduction
93
2
The
P
lot
( )
Command
93
2.1
Features that Can Be Included in a Scatter Plot
94
3
Scatter Plots
96
3.1
Regression Analysis with Scatter Plots
96
3.2
Multiple Regression Analysis with Scatterplot Matrices
102
3.3
Scatterplot Matrices of Data Segregated by a Categorical
Variable
T
05
4
Time Series Plots
106
4.1
Three Graphs in a Single Frame
107
4.2
Two Different Time Series Data Sets in a Single Plot
109
5
Pie Charts - - -j
6
Special Box Plots
1
-
3
7
xy Plots 1
λ 6
8
Curves ^ ^
g
9
LOWESS 122
10
Sunflower Plots
^25
11
Violin Plots l27
12
Bean Plots 12g
13
Bubble Charts 130
Contents
vu
14 3D
Surface
Plot
130
15
Chernoff Faces
—
Graphical Presentation of Multivariate Data
133
16
Maps
137
16.1
Drawing Common Maps
137
16.2
Creating a Choropleth Map
139
References
142
4.
Matrix Algebra Topics in Statistics and Economics
Using
R
143
Hrishikesh D. Vinod
1
Introduction
143
2
Basic Matrix Manipulations in
R
144
3
Descriptive Statistics
146
3.1
Outlier Detection and Normality Tests
148
3.2
Multivariate Normality Tests
148
4
Matrix Transformations,
Invariance,
and Equivariance
148
Affine
Transformations Defined
149
Desirable
Invariance
and Equivariance
149
4.1
Data Standardization
149
4.2
Limitations of the Usual Standardization
151
4.3
Mahalanobis Distance and Outlier Detection
153
5
Payoff Matrices in Decision Analysis
1 54
6
Matrix Algebra in Regression Models
1 56
6.1
Matrix QR Decomposition
157
6.2
Collinearity and Singular Value Decomposition
158
6.3
Heteroscedastic and Autocorrelated Errors
159
7
Correlation Matrices and Generalizations
1 60
Bounds on the Cross-Correlation
160
7.1
New Asymmetric Generalized Correlation Matrix
161
8
Matrices for Population Dynamics
165
9
Multivariate Components Analysis
1 68
9.1
Projection Matrix: Generalized Canonical Correlations
168
9.2
Invariant Coordinate Selection
169
10
Sparse Matrices
172
References
175
5.
Sample Size Calculations with R: Level
1 1 77
Marepalli B. Rao and Subramanyam
Kasala
1
Introduction
1 77
1.1
Goals
178
1.2
Why Did We Choose R?
1 78
2
General Ideas on Sample Size Calculations
1 78
2.1
Example
179
2.2
FAQ and Pointers
180
2.3
Signal-to-Noise Ratio
181
2.4
Some Features of the Normal Distribution
181
viii Contents
3
Single-Sample Problems 184
3.1
Quantitative 184
3.2
Testing of Hypotheses Environment
184
3.3
Specifications
185
3.4
Formula for Sample Size
186
3.5
Comments 190
3.6
The Other Type of One-Sided Alternative
190
3.7
The Case of Two-Sided Alternative
190
3.8
Comments
Ί94
3.9
One-Sided Alternative
194
3.10
Two-Sided Alternative
194
3.1 1
The Case When the Population Standard Deviation
σ
Is
Unknown
194
3.12
The Case of One-Sided Alternative
194
3.13
Specifications
195
3.14
Comments
198
3.15
One-Sample Problem: One-Sided Alternative:
σ
Is Known
198
3.16
One-Sample Problem: One-Sided Alternative:
σ
Is Unknown
198
3.17
R
Code
199
3.18
One-Sample Problem
201
3.19
Specifications
202
3.20
Example
202
3.21
An Alternative Approach
202
3.22
Example
202
3.23
Specifications
204
4
Two-Sample Problems: Quantitative Responses
204
4.1
Scenario
1 205
4.2
Specifications
205
4.3
Scenario
2 206
4.4
One-Sided Alternative
206
4.5
Specifications
206
4.6
Scenario
3 207
4.7
One-Sided Alternative
207
4.8
Specifications
207
4.9
Illustration
207
4.10
Two-Sided Alternative
208
4.1 1
Specifications
208
4.12
An Illustration
208
4.1 3
Scenario
4 211
4.14
Estimation Perspective
211
4.1 5
Scenario
1 211
4.16
Specifications
211
4.1 7
Example
212
4.18
Scenario
2 212
4.19
Specifications
212
4.20
Example
2ι ί.
4.21
Scenario
3 213
4.22
Paired f-Test
213
4.23
Specifications
214
Contents ix
5 Multisample Problem —Quantitative
Responses—
Analysis
of Variance
2ЛЅ
5.1
Specifications
215
5.2
Examples
216
5.3
Structure of the Data
216
5.4
Specifications
217
5.5
Specifications
217
5.6
Some Guidelines from the Social Sciences and Psychology
218
5.7
Comments
220
References
220
6.
Sample Size Calculations with R: Level
2 221
Marepalli B. Rao and
Hansen Bannerman-Thompson
1
Single Proportions
221
1.1
Problem
221
2
Two-Sample Proportions
232
2.1
Traditional
Tesi
233
2.2
Arcsine Square Root Transformation
234
3
Effect Sizes
237
3.1
The Case of Proportions
237
3.2
The Case of f-Test
237
3.3
The Case of Correlation
238
3.4
Analysis of Variance
238
4
Multisample Proportions
239
4.1
Testing Equality of Several Population Proportions
239
5
McNemar TEST
242
6
Correlations
244
7
Hazard Ratio in Survival Analysis
247
7.1
A Pilot Study
249
8
Multiple Regression
25 7
References
255
7.
Binomial Regression in
R
257
John Muschelli, Joshua Betz, and Ravi
Varadhan
1
Binomial Regression in the Generalized Linear Model
258
2
Standard Logistic Regression
259
3
Assumptions Involved in the Standard Logistic Regression Model
261
4
Residuals
261
4.1
Interpreting Residuals
263
4.2
Influential Points
266
5
Overdispersion
268
5.1
Estimation Using Quasi
I i kel i
hood
269
5.2
Adding Explanatory Terms to the Model
271
6
Hypothesis Testing and Inference
273
7
Mode) Performance
275
7.1
ROC Curves/Sensitivity/Specificity/Accuracy
275
Contents
7.2
Area Under the Curve 277
7.3
Selecting a Cut Point 28°
8
Modeľmg
Repeated (Longitudinal) Binary Measures
281
8.1
Generalized Estimating Equations
282
8.2
Generalized Linear Mixed Models
285
9
Model Selection 289
9.1
Penalized Logistic Regression: The qlmnet Package
292
9.2
Phoneme Data 293
9.3
Fitting glmnet Models
293
9.4
Visualizing the glmnet Model
294
9.5
Choosing A in glmnet Using Cross-
Val ¡dation
296
10
Machine Learning Methods
299
10.1
Splitting the Data in Train and Test Samples
299
Ю.2
Recursive Partitioning
(гра
r t)
300
10.3
Random Forests
300
10.4
Generalized Boosted Regression Modeling
302
10.5
Comparison of Results
303
11
Concluding Remarks
305
References
306
8.
Computing Tolerance Intervals and Regions Using
R
309
Derek S. Young
1
Introduction
309
1.1
Formal Definition
310
2
Tolerance Intervals for Continuous Distributions
311
2.1
Tolerance intervals for the Normal Distribution
311
2.2
Tolerance Intervals for the Exponential Distribution
315
2.3
Tolerance Intervals for the Weibull Distribution
316
3
Tolerance Intervals for Discrete Distributions
317
3.1
Tolerance Intervals for the Binomial Distribution
318
3.2
Tolerance Intervals for the
Poisson
Distribution
319
3.3
Tolerance intervals for the Negative Binomial Distribution
320
4
Nonparametric Tolerance Intervals
321
5
Regression Tolerance Intervals
324
5.1
Linear Regression Tolerance Intervals
324
5.2
Nonlinear Regression Tolerance Intervals
327
5.3
Nonparametric Regression Tolerance Intervals
328
6
Multivariate Tolerance Regions
331
7
Final Remarks
334
References
335
9.
Modeling the Probability of Second Cancer in
Controlled Clinical Trials
339
Kao-Tai Tsai and Karl E. Peace
1
Introduction
33g
2
Difficulties in Second Cancer Research
340
Contents xi
3
Current Knowledge of Second Malignancy
340
4
Clinical Trial Database
342
4.1
Laboratory Test Data Analysis
343
4.2
Medical History and Concomitant Medicines
345
4.3
Efficacy Data Consideration
347
5
Integrated Analysis
347
6
Assessing Model Adequacy
353
7
Summary
355
References
356
10.
Bayesian Networks
357
Marepalli B. Rao and C. R. Rao
1
Introduction
357
2
Joint and Conditional Distributions
358
3
Generalities and Issues
362
4
Graph Theory
365
5
A Case Study
367
Model Selection
372
6
Network Model Fitting
378
7
Learning Algorithm
383
References
385
Index
387
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| id | DE-604.BV042427253 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T01:21:20Z |
| institution | BVB |
| isbn | 9780444634313 |
| language | English |
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| owner_facet | DE-210 DE-739 DE-706 DE-384 DE-473 DE-BY-UBG DE-703 |
| physical | XVII, 394 S. Ill., graph. Darst., Kt. |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Elsevier |
| record_format | marc |
| series | Handbook of statistics |
| series2 | Handbook of statistics |
| spelling | Computational statistics with R ed. by Marepalli B. Rao ; C. R. Rao Amsterdam [u.a.] Elsevier 2014 XVII, 394 S. Ill., graph. Darst., Kt. txt rdacontent n rdamedia nc rdacarrier Handbook of statistics 32 Literaturangaben R Programm (DE-588)4705956-4 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Statistik (DE-588)4056995-0 s R Programm (DE-588)4705956-4 s DE-604 Rao, Marepalli B. Sonstige (DE-588)1068118873 oth Rao, Calyampudi Radhakrishna 1920-2023 Sonstige (DE-588)119285924 oth Handbook of statistics 32 (DE-604)BV000002510 32 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027862606&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Computational statistics with R Handbook of statistics R Programm (DE-588)4705956-4 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4056995-0 |
| title | Computational statistics with R |
| title_auth | Computational statistics with R |
| title_exact_search | Computational statistics with R |
| title_full | Computational statistics with R ed. by Marepalli B. Rao ; C. R. Rao |
| title_fullStr | Computational statistics with R ed. by Marepalli B. Rao ; C. R. Rao |
| title_full_unstemmed | Computational statistics with R ed. by Marepalli B. Rao ; C. R. Rao |
| title_short | Computational statistics with R |
| title_sort | computational statistics with r |
| topic | R Programm (DE-588)4705956-4 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | R Programm Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027862606&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000002510 |
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