Using R for data management, statistical analysis, and graphics:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton [u.a.]
CRC Press
2011
|
| Series: | A Chapman & Hall book
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XXII, 275 S. Ill., graph. Darst. |
| ISBN: | 9781439827550 |
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MARC
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| 008 | 100922s2011 xxuad|| |||| 00||| eng d | ||
| 010 | |a 2010021409 | ||
| 020 | |a 9781439827550 |c pbk. : alk. paper |9 978-1-4398-2755-0 | ||
| 035 | |a (OCoLC)700637637 | ||
| 035 | |a (DE-599)BVBBV036681695 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
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| 084 | |a SK 850 |0 (DE-625)143263: |2 rvk | ||
| 084 | |a ST 601 |0 (DE-625)143682: |2 rvk | ||
| 100 | 1 | |a Horton, Nicholas J. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Using R for data management, statistical analysis, and graphics |c Nicholas J. Horton ; Ken Kleinman |
| 264 | 1 | |a Boca Raton [u.a.] |b CRC Press |c 2011 | |
| 300 | |a XXII, 275 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a A Chapman & Hall book | |
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a R (Computer program language) | |
| 650 | 4 | |a Open-source software | |
| 650 | 4 | |a Database management | |
| 650 | 4 | |a Mathematical statistics |x Data processing | |
| 650 | 0 | 7 | |a R |g Programm |0 (DE-588)4705956-4 |2 gnd |9 rswk-swf |
| 655 | 7 | |0 (DE-588)4151278-9 |a Einführung |2 gnd-content | |
| 689 | 0 | 0 | |a R |g Programm |0 (DE-588)4705956-4 |D s |
| 689 | 0 | |C b |5 DE-604 | |
| 700 | 1 | |a Kleinman, Ken |e Verfasser |0 (DE-588)130212288 |4 aut | |
| 856 | 4 | 2 | |m Digitalisierung UB Bamberg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020600551&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-020600551 | |
Record in the Search Index
| _version_ | 1816231407450587136 |
|---|---|
| adam_text |
Contents
List of Tables
xvii
List of Figures
xix
Preface
xxi
1
Introduction to
R
1
1.1
Installation
. 2
1.1.1
Installation under Windows
. 2
1.1.2
Installation under Mac OS X
. 3
1.1.3
Installation under Linux
. 4
1.2
Running
R
and sample session
. 4
1.2.1
Replicating examples from the book and sourcing
commands
. 4
1.2.2
Batch mode
. 6
1.3
Using the
R
Commander graphical interface
. 7
1.4
Learning
R
and getting help
. 8
1.5
Fundamental structures: Objects, classes, and related concepts
. 10
1.5.1
Objects and vectors
. 10
1.5.2
Indexing
. 10
1.5.3
Operators
. 11
1.5.4
Lists
. 11
1.5.5
Matrices
. 12
1.5.6 Dataframes. 13
1.5.7
Attributes and classes
. 15
1.5.8
Options
. 15
1.6
Built-in and user-defined functions
. 15
1.6.1
Calling functions
. 15
1.6.2
Writing functions
. 16
1.6.3
The apply family of functions
. 18
1.7
Add-ons: Libraries and packages
. 18
1.7.1
Introduction to libraries and packages
. 18
1.7.2
CRAN
task views
. 19
vi
CONTENTS
1.7.3
Installed libraries and packages
. 20
1.7.4
Recommended packages
. 21
1.7.5
Packages referenced in the book
. 22
1.7.6
Datasets
available with
R
. 24
1.8
Support and bugs
. 24
2
Data management
25
2.1
Input
. 25
2.1.1
Native
dataset
. 25
2.1.2
Fixed format text files
. 26
2.1.3
Other fixed files
. 26
2.1.4
Reading more complex text files
. 26
2.1.5
Comma-separated value (CSV) files
. 27
2.1.6
Reading
datasets
in other formats
. 27
2.1.7
URL
. 28
2.1.8
XML (extensible markup language)
. 28
2.1.9
Data entry
. 29
2.2
Output
. 29
2.2.1
Save a native
dataset
. 29
2.2.2
Creating files for use by other packages
. 29
2.2.3
Creating
datasets
in text format
. 30
2.2.4
Creating HTML formatted output
.
30
2.2.5
Creating XML
datasets
and output
.
30
2.2.6
Displaying objects
. 31
2.2.7
Displaying formatted output
. 31
2.2.8
Number of digits to display
. 32
2.2.9
Automating reproducible analysis and output
. 32
2.3
Structure and
meta-data
. 32
2.3.1
Access variables from
a dataset
. 32
2.3.2
Names of variables and their types
. 33
2.3.3
Rename variables in
a dataset
. 33
2.3.4
Add comment to
a dataset
or variable
. 33
2.4
Derived variables and data manipulation
. 34
2.4.1
Create string variables from numeric variables
. 34
2.4.2
Create numeric variables from string variables
. 34
2.4.3
Concatenate vectors
. 34
2.4.4
Extract characters from string variables
. 34
2.4.5
Length of string variables
. 35
2.4.6
Concatenate string variables
. 35
2.4.7
Find strings within string variables
.
35
2.4.8
Find approximate strings
. 36
2.4.9
Replace strings within string variables
.· · · · · 36
2.4.10
Split string into multiple strings
.· · · ·· ·
36
2.4.11
Remove spaces around string variables
. · · · · ■
36
2.4.12
Upper to lower case
: . . .--.
W.
.'. . . . . · ■ ■ 37
CONTENTS
vii
2.4.13
Create categorical
variables
from continuous
variables
. 37
2.4.14
Recode a
categorical variable
. 37
2.4.15
Create a categorical variable using logic
. 38
2.4.16
Formatting values of variables
. 38
2.4.17
Label variables
. 39
2.4.18
Account for missing values
. 39
2.4.19
Observation number
. 41
2.4.20
Unique values
. 41
2.4.21
Duplicated values
. 41
2.4.22
Lagged variable
. 41
2.4.23
SQL
. 42
2.5
Merging, combining, and subsctting
datasets
. 42
2.5.1
Subsetting observations
. 42
2.5.2
Random sample of
a
dataset
. 42
2.5.3
Convert from wide to long (tall) format
. 43
2.5.4
Convert from long (tall) to wide format
. 44
2.5.5
Concatenate
datasets
. 44
2.5.6
Sort
datasets
. 44
2.5.7
Merge
datasets
. 44
2.5.8
Drop variables in
a
dataset
. 46
2.6
Date and time variables
. 46
2.6.1
Create date variable
. 46
2.6.2
Extract weekday
. 46
2.6.3
Extract month
. 47
2.6.4
Extract year
. 47
2.6.5
Extract quarter
. 47
2.6.6
Create time variable
. 47
2.7
Interactions with the operating system
. 47
2.7.1
Timing commands
. 47
2.7.2
Suspend execution for a time interval
. 48
2.7.3
Execute command in operating system
. 48
2.7.4
Command history
. 48
2.7.5
Find working directory
. 48
2.7.6
Change working directory
. 49
2.7.7
List and access files
. 49
2.8
Mathematical functions
. 49
2.8.1
Basic functions
. 49
2.8.2
Trigonometric functions
. 50
2.8.3
Special functions
. 50
2.8.4
Integer functions
. 50
2.8.5
Comparisons of floating point variables
. 51
2.8.6
Complex numbers
. 51
2.8.7
Derivative
. 51
2.8.8
Optimization problems
. 51
viii CONTENTS
2.9 Matrix
operations
. 52
2.9.1
Create matrix from vector
. 52
2.9.2
Create matrix from vectors
. 52
2.9.3
Transpose matrix
. 52
2.9.4
Find dimension of a matrix
. 53
2.9.5
Matrix multiplication
. 53
2.9.6
Component-wise multiplication
. 53
2.9.7
Invert matrix
. 53
2.9.8
Create submatrix
. 53
2.9.9
Create a diagonal matrix
. 53
2.9.10
Create vector of diagonal elements
. 54
2.9.11
Create vector from a matrix
. 54
2.9.12
Calculate determinant
. 54
2.9.13
Find eigenvalues and eigenvectors
. 54
2.9.14
Calculate singular value decomposition
. 54
2.10
Probability distributions and random number generation
. 55
2.10.1
Probability density function
. 55
2.10.2
Cumulative density function
. 55
2.10.3
Quantiles of a probability density function
. 56
2.10.4
Uniform random variables
. 56
2.10.5
Multinomial random variables
. 57
2.10.6
Normal random variables
. 57
2.10.7
Multivariate normal random variables
. 57
2.10.8
Truncated multivariate normal random variables
. 58
2.10.9
Exponential random variables
. 58
2.10.10
Other random variables
. 58
2.10.11
Setting the random number seed
. 59
2.11
Control flow and programming
. 59
2.11.1
Looping
. 59
2.11.2
Error recovery
. 59
2.11.3
Assertions
. 60
2.11.4
Conditional execution
. 60
2.11.5
Sequence of values or patterns
. 60
2.11.6
Grid of values
. 62
2.11.7
Reference a variable using a character vector
. 62
2.11.8
Perform an action repeatedly over a set of
variables
. 62
2.12
Further resources
. 63
2.13
HELP examples
. 63
2.13.1
Data input
. 63
2.13.2
Consistency checking
. 65
2.13.3
Data output
. 65
2.13.4
Data display
. 65
2.13.5
Derived variables and data manipulation
. 66
2.13.6
Sorting and subsetting
datasets
. 70
CONTENTS ix
2.13.7
Probability
distributions
. 71
3
Common statistical procedures
73
3.1
Summary statistics
. 73
3.1.1
Means and other summary statistics
. 73
3.1.2
Means by group
. 73
3.1.3
Trimmed mean
. 74
3.1.4
Five-number summary
. 74
3.1.5
Quantiles
. 74
3.1.
G
Centering, normalizing, and scaling
. 75
3.1.7
Mean and
95%
confidence interval
. 75
3.1.8
Maximum likelihood estimation of distributional
parameters
. 75
3.1.9
Bootstrapping a sample statistic
. 75
3.1.10
Proportion and
95%
confidence interval
.
7G
3.1.11
Tests of normality
. 77
3.2
Contingency tables
. 77
3.2.1
Display counts for a single variable
. 77
3.2.2
Display cross-classification table
. 77
3.2.3
Pearson chi-square statistic
. 78
3.2.4
Cochran-Mantel-Haenszel test
. 78
3.2.5
Fisher's exact test
. 78
3.2.6
McNemaťs
test
. 78
3.3
Divariate
statistics
. 79
3.3.1
Epidemiologie
statistics
. 79
3.3.2
Test characteristics
. 79
3.3.3
Correlation
. 80
3.3.4
Kappa (agreement)
. 80
3.4
Two sample tests for continuous variables
. 81
3.4.1
Student's t-test
. 81
3.4.2
Nonparametric tests
. 81
3.4.3
Permutation test
. 81
3.4.4
Logrank test
. 82
3.5
Further resources
. 82
3.6
HELP examples
. 82
3.6.1
Summary statistics and exploratory data analysis
. 82
3.6.2
Divariate
relationships
. 83
3.6.3
Contingency tables
. 86
3.6.4
Two sample tests of continuous variables
. 88
3.6.5
Survival analvsis: Logrank test
. 90
CONTENTS
Linear
regression and
ANOVA 93
4.1 Model
fitting
.93
4.1.1 Linear
regression .
93
4.1.2 Linear
regression with categorical covariates
.94
4.1.3
Parameterization of categorical covariates
.94
4.1.4
Linear regression with no intercept
.95
4.1.5
Linear regression with interactions
.95
4.1.6
Linear models stratified by each value of a grouping
variable
.96
4.1.7
One-way analysis of variance
.96
4.1.8
Two-way (or more) analysis of variance
.97
4.2
Model comparison and selection
.97
4.2.1
Compare two models
.97
4.2.2
Log-likelihood
.97
4.2.3
Akaiké
Information Criterion (AIC)
.97
4.2.4
Bayesian Information Criterion
(BIC)
.98
4.3
Tests, contrasts, and linear functions
.98
4.3.1
Joint null hypotheses: Several parameters equal
0 . 98
4.3.2
Joint null hypotheses: Sum of parameters
.98
4.3.3
Tests of equality of parameters
.99
4.3.4
Multiple comparisons
.99
4.3.5
Linear combinations of parameters
.99
4.4
Alodel diagnostics
.100
4.4.1
Predicted values
.100
4.4.2
Residuals
.100
4.4.3
Standardized residuals
.100
4.4.4
Leverage
.101
4.4.5
Cook's
D
.101
4.4.6
DFFITS
.101
4.4.7
Diagnostic plots
.102
4.4.8
Heteroscedasticity tests
.102
4.5
Model parameters and results
.102
4.5.1
Parameter estimates
.102
4.5.2
Standard errors of parameter estimates
.102
4.5.3
Confidence limits for parameter estimates
.103
4.5.4
Confidence limits for the mean
.103
4.5.5
Prediction limits
.103
4.5.6
Plot confidence limits for a particular covariate
vector
.103
4.5.7
Plot prediction limits for a new observation
.104
4.5.8
Plot predicted lines for several values of a predictor
. . . 104
4.5.9
Design and information matrix
.104
4.5.10
Covariance matrix of the predictors
.105
4.6
Further resources
.105
4.7
HELP examples
.105
CONTENTS xi
4.7.1 Scatterplot
with smooth fit
.IOC
4.7.2
Regression with prediction intervals
.
10G
4.7.3
Linear regression with interaction
.108
4.7.4
Regression diagnostics
.
Ill
4.7.5
Fitting regression model separately for each value; of
another variable
.112
4.7.6
Two-way ANOVA
.
Ill
4.7.7
Multiple comparisons
.118
4.7.8
Contrasts
.118
5
Regression generalizations
121
5.1
Generalized linear models
.121
5.1.1
Logistic regression model
.121
5.1.2
Exact logistic regression
.122
5.1.3
Poisson
model
.122
5.1.4
Goodness of fit for count models
.123
5.1.5
Zero-inflated
Poisson
model
.123
5.1.6
Negative binomial model
.123
5.1.7
Zero-inflated negative binomial model
.124
5.1.8
Log-linear model
.124
5.1.9
Ordered multinomial model
.124
5.1.10
Generalized (nominal outcome) multinomial logit
. . . .125
5.1.11
Conditional logistic regression model
.125
5.2
Models for correlated data
.125
5.2.1
Linear models with correlated outcomes
.125
5.2.2
Linear mixed models with random intercepts
.125
5.2.3
Linear mixed models with random slopes
.126
5.2.4
More complex random coefficient models
.126
5.2.5
Multilevel models
.127
5.2.6
Generalized linear mixed models
.127
5.2.7
Generalized estimating equations
.127
5.2.8
Time-series model
.127
5.3
Survival analysis
.128
■5.3.1
Proportional hazards (Cox) regression model
.128
5.3.2
Proportional hazards (Cox) model with frailty
.128
5.4
Further generalizations to regression models
.129
5.4.1
Nonlinear least squares model
.129
5.4.2
Generalized additive model
.129
5.4.3
Robust regression model
.129
5.4.4
LASSO model selection
.130
5.4.5
Quantile regression model
.130
5.4.6
Ridge regression model
.130
5.4.7
Bayesian methods
.131
5.4.8
Complex survey design
.131
5.5
Multivariate statistics
.133
xii CONTENTS
5.5.1 Cronbach's
alpha.
133
5.5.2
Factor analysis
.133
5.5.3
Principal component analysis
.133
5.5.4
Recursive partitioning
.134
5.5.5
Linear discriminant analysis
.134
5.5.6
Hierarchical clustering
.134
5.6
Further resources
.135
5.7
HELP examples
.135
5.7.1
Logistic regression
.136
5.7.2
Poisson
regression
.138
5.7.3
Zero-inflated
Poisson
regression
.139
5.7.4
Negative binomial regression
.140
5.7.5
LASSO model selection
.140
5.7.6
Quantile regression
.142
5.7.7
Ordinal logit
.143
5.7.8
Multinomial logit
.144
5.7.9
Generalized additive model
.145
5.7.10
Reshaping
dataset
for longitudinal regression
.146
5.7.11
Linear model for correlated data
.148
5.7.12
Linear mixed (random slope) model
.151
5.7.13
Generalized estimating equations
.152
5.7.14
Generalized linear mixed model
.153
5.7.15
Cox proportional hazards model
.154
5.7.16
Bayesian
Poisson
regression
.155
5.7.17
Cronbach's alpha
.156
5.7.18
Factor analysis
.157
5.7.19
Principal component analysis
.158
5.7.20
Recursive partitioning
.159
5.7.21
Linear discriminant analysis
.161
5.7.22
Hierarchical clustering
.162
6
Graphics
165
6.1
A compendium of useful plots
.166
6.1.1
Scatterplot
.166
6.1.2
Scatterplot with multiple
y
values
.166
6.1.3
Bubble plot
.167
6.1.4
Sunflower plot
.167
6.1.5
Matrix of scatterplots
.167
6.1.6
Conditioning plot
.168
6.1.7
Barplot
.168
6.1.8
Dotplot
.168
6.1.9
Histogram
.168
6.1.10
Stem-and-leaf plot
.169
6.1.11
Boxplot
.169
6.1.12
Side-by-side boxplots
.169
CONTENTS xiii
6.1.13
Interaction
plots
.170
6.1.14
Plots for categorical data
.170
6.1.15 3-D
plots
.170
6.1.16
Circular plot
.170
6.1.17
Receiver operating characteristic (ROC) curve
.171
6.1.18
Kaplan-Meier plot
.171
6.1.19
Plot an arbitrary function
.172
6.1.20
Empirical cumulative probability density plot
.172
6.1.21
Empirical probability density plot
.172
6.1.22
Normal quantile-quantile plot
.172
6.2
Adding elements
.173
6.2.1
Plot symbols
.173
6.2.2
Add ¡joints to an existing graphic
.173
6.2.3
Jitter points
.174
6.2.4
Arbitrary straight line
.174
6.2.5
OLS line fit to points
.174
6.2.6
Smoothed line
.174
6.2.7
Add grid
.175
6.2.8
Normal density
.175
6.2.9
Marginal rug plot
.175
6.2.10
Titles
.175
6.2.11
Footnotes
.176
6.2.12
Text
.176
6.2.13
Mathematical symbols
.176
6.2.14
Arrows and shapes
.176
6.2.15
Legend
.177
6.2.16
Identifying and locating points
.177
6.3
Options and parameters
.177
6.3.1
Graph size
.177
6.3.2
Point and text size
.177
6.3.3
Box around plots
.178
6.3.4
Size of margins
.178
6.3.5
Graphical settings
.178
6.3.6
Multiple plots per page
.178
6.3.7
Axis range and style
.179
6.3.8
Axis labels, values, and tick marks
.179
6.3.9
Line styles
.179
6.3.10
Line widths
.179
6.3.11
Colors
.180
6.3.12
Log scale
.180
6.3.13
Omit axes
.180
6.4
Saving graphs
.180
6.4.1
PDF
.180
6.4.2
Postscript
.181
6.4.3
JPEG
.181
XIV
CONTENTS
6.4.4
WMF
.181
6.4.5
BMP
.181
6.4.6
TIFF
.181
6.4.7
PNG
.182
6.4.8
Closing
a graphie
device
.182
6.5
Further resources
.182
6.6
HELP examples
.182
6.6.1
Scatterplot with multiple axes
.182
6.6.2
Bubble plot
.183
6.6.3
Conditioning plot
.184
6.6.4
Multiple plots
.185
6.6.5
Dotplot
.187
6.6.6
Kaplan-Meier plot
.188
6.6.7
ROC curve
.190
6.6.8
Pairs plot
.190
6.6.9
Visualize correlation matrix
.192
Advanced applications
7.1
Power and sample size calculations
.197
7.1.1
Analytic power calculation
.197
7.1.2
Simulation-based power calculations
.198
7.2
Simulations and data generation
.200
7.2.1
Simulate data from a logistic regression
.200
7.2.2
Simulate data from generalized linear mixed model
. . ■ 201
7.2.3
Generate correlated binary data
.202
7.2.4
Simulate data from a Cox model
.203
7.3
Data management and related tasks
.204
7.3.1
Finding two closest values in a vector
.204
7.3.2
Calculate and plot a running average
.205
7.3.3
Tabulate binomial probabilities
.206
7.3.4
Sampling from a pathological distribution
.208
7.4
Read geocoded data and draw maps
.211
7.4.1
Read variable format files and plot maps
.211
7.4.2
Read input files
.211
7.4.3
Plotting maps
.212
7.5
Data scraping and visualization
.214
7.5.1
Scraping data from HTML files
.214
7.5.2
Reading data with two lines per observation
.214
7.5.3
Plotting time series data
.216
7.6
Account for missing data
.217
7.7
Propensity score modeling
.219
7.8
Empirical problem solving
.223
7.8.1
Diploma (or hat-check) problem
.223
7.8.2
Knapsack problem (constrained optimization)
.224
7.9
Further resources
.226
CONTENTS xv
Appendix
The HELP study
dataset
227
A.I Background on the HELP study
.227
A.
2
Road map to analyses of the HELP
dataset
.227
A.3 Detailed description of the
dataset
.229
Bibliography
2.33
Indices
239
Subject index
.241
R
index
.
2ÍÍ3 |
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| spellingShingle | Horton, Nicholas J. Kleinman, Ken Using R for data management, statistical analysis, and graphics Datenverarbeitung R (Computer program language) Open-source software Database management Mathematical statistics Data processing R Programm (DE-588)4705956-4 gnd |
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| title | Using R for data management, statistical analysis, and graphics |
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