Interactive and dynamic graphics for data analysis: with R and GGobi
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer
2007
|
| Schriftenreihe: | Use R!
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVII, 188 S. Ill., zahlr. graph. Darst. |
| ISBN: | 9780387717616 9780387717623 |
Internformat
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| 100 | 1 | |a Cook, Dianne |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Interactive and dynamic graphics for data analysis |b with R and GGobi |c Dianne Cook ; Deborah F. Swayne |
| 246 | 1 | 3 | |a Use R |
| 264 | 1 | |a New York, NY |b Springer |c 2007 | |
| 300 | |a XVII, 188 S. |b Ill., zahlr. graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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| 689 | 1 | 0 | |a Datenanalyse |0 (DE-588)4123037-1 |D s |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-016396769 | ||
Datensatz im Suchindex
| _version_ | 1804137492425736192 |
|---|---|
| adam_text | Contents
Preface
........................................................
V
Technical
Notes
...............................................
XIII
List of Figures
.................................................
XV
1
Introduction
............................................... 1
1.1
Data visualization: beyond the third dimension
.............. 1
1.2
Statistical data visualization: goals and history
.............. 3
1.3
Getting down to data
.................................... 4
1.4
Getting real: process and caveats
.......................... 8
1.5
Interactive investigation
.................................. 15
2
The Toolbox
............................................... 17
2.1
Introduction
............................................ 17
2.2
Plot types
.............................................. 19
2.2.1
Univariatc plots
................................... 19
2.2.2
Divariate
plots
.................................... 21
2.2.3
Mult
і
variate
plots
................................. 24
2.2.4
Plot arrangement
.................................. 34
2.3
Plot manipulation and enhancement
....................... 35
2.3.1
Brushing
......................................... 35
2.3.2
Identification
..................................... 41
2.3.3
Scaling
........................................... 41
2.3.4
Subset selection
................................... 42
2.3.5
Line segments
..................................... 43
2.3.6
Interactive drawing
................................ 43
2.3.7
Dragging points
................................... 43
2.4
Tools available elsewhere
................................. 44
2.5
Recap
.................................................. 45
Exercises
................................................... 45
X
Contents
3
Missing Values
............................................
^~
3.1
Background
.............................................
48
3.2
Exploring missingness
.................................... ^9
3.2.1
Shadow matrix
....................................
49
3.2.2
Getting started: missings in the margins
............ 52
3.2.3
A limitation
...................................... 53
3.2.4
Tracking missings using the shadow matrix
........... 55
3.3
Imputation
............................................. 55
3.3.1
.Mean values
...................................... 56
3.3.2
Random values
.................................... 56
3.3.3
Multiple imputation
............................... 58
3.4
Recap
.................................................. 61
Exercises
................................................... 62
4
Supervised Classification
.................................. 63
4.1
Background
............................................. 64
4.1.1
Classical multivariate statistics
...................... 65
4.1.2
Data mining
...................................... 66
4.1.3
Studying the fit
................................... 69
4.2
Purely graphics: getting a picture of the class structure
....... 70
4.2.1
Overview of Italian Olive Oils
........................ 70
4.2.2
Building classifiers to predict region
................. 71
4.2.3
Separating the oils by area within each region
......... 73
4.2.4
Taking stock
...................................... 77
4.3
Numerical methods
...................................... 77
4.3.1
Linear discriminant analysis
........................ 77
4.3.2
Trees
............................................ 81
4.3.3
Random forests
................................... 83
4.3.4
Neural networks
................................... 88
4.3.5
Support vector machine
............................ 92
4.3.6
Examining boundaries
............................. 97
4.4
Recap
.................................................. 99
Exercises
............................... 99
5
Cluster Analysis
...........................................103
5.1
Background
.............................................105
5.2
Purely graphics
.........................................107
5.3
Numerical methods
......................................
Ш
5.3.1
Hierarchical algorithms
.............................
Ill
5.3.2
Model-based clustering
.............................113
5.3.3
Self-organizing maps
...............................119
5.3.4
Comparing methods
...............................122
5.4
Characterizing clusters
................................. 125
5.5
Recap
.......................
j9g
Exercises
.................... . 27
Contents
XI
6
Miscellaneous Topics
......................................129
6.1
Inference
...............................................129
6.2
Longitudinal data
.......................................134
6.3
Network data
...........................................139
6.4
Multidimensional scaling
.................................145
Exercises
...................................................151
7
Datasets
...................................................153
7.1
Tips
...................................................153
7.2
Australian Crabs
.........................................154
7.3
Italian Olive Oils
.........................................155
7.4
Flea Beetles
.............................................157
7.5
PRIM7
.................................................157
7.6
Tropical Atmosphere-Ocean Array (TAO)
....................159
7.7
Primary Biliary Cirrhosis (PBC)
.............................161
7.8
Spam
..................................................162
7.9
Wages
..................................................164
7.10
Rat Gene Expression
......................................166
7.11
Arabidopsis Gene Expression
...............................168
7.12
Music
..................................................171
7.13
Cluster Challenge
........................................172
7.14
Adjacent Transposition Graph
..............................172
7.15
Florentine Families
.......................................173
7.16
Morse Code Confusion Rates
...............................174
7.17
Personal Social Network
...................................175
References
.....................................................177
Index
..........................................................185
|
| adam_txt |
Contents
Preface
.
V
Technical
Notes
.
XIII
List of Figures
.
XV
1
Introduction
. 1
1.1
Data visualization: beyond the third dimension
. 1
1.2
Statistical data visualization: goals and history
. 3
1.3
Getting down to data
. 4
1.4
Getting real: process and caveats
. 8
1.5
Interactive investigation
. 15
2
The Toolbox
. 17
2.1
Introduction
. 17
2.2
Plot types
. 19
2.2.1
Univariatc plots
. 19
2.2.2
Divariate
plots
. 21
2.2.3
Mult
і
variate
plots
. 24
2.2.4
Plot arrangement
. 34
2.3
Plot manipulation and enhancement
. 35
2.3.1
Brushing
. 35
2.3.2
Identification
. 41
2.3.3
Scaling
. 41
2.3.4
Subset selection
. 42
2.3.5
Line segments
. 43
2.3.6
Interactive drawing
. 43
2.3.7
Dragging points
. 43
2.4
Tools available elsewhere
. 44
2.5
Recap
. 45
Exercises
. 45
X
Contents
3
Missing Values
.
^~
3.1
Background
.
48
3.2
Exploring missingness
. ^9
3.2.1
Shadow matrix
.
49
3.2.2
Getting started: missings in the "margins"
. 52
3.2.3
A limitation
. 53
3.2.4
Tracking missings using the shadow matrix
. 55
3.3
Imputation
. 55
3.3.1
.Mean values
. 56
3.3.2
Random values
. 56
3.3.3
Multiple imputation
. 58
3.4
Recap
. 61
Exercises
. 62
4
Supervised Classification
. 63
4.1
Background
. 64
4.1.1
Classical multivariate statistics
. 65
4.1.2
Data mining
. 66
4.1.3
Studying the fit
. 69
4.2
Purely graphics: getting a picture of the class structure
. 70
4.2.1
Overview of Italian Olive Oils
. 70
4.2.2
Building classifiers to predict region
. 71
4.2.3
Separating the oils by area within each region
. 73
4.2.4
Taking stock
. 77
4.3
Numerical methods
. 77
4.3.1
Linear discriminant analysis
. 77
4.3.2
Trees
. 81
4.3.3
Random forests
. 83
4.3.4
Neural networks
. 88
4.3.5
Support vector machine
. 92
4.3.6
Examining boundaries
. 97
4.4
Recap
. 99
Exercises
. 99
5
Cluster Analysis
.103
5.1
Background
.105
5.2
Purely graphics
.107
5.3
Numerical methods
.
Ш
5.3.1
Hierarchical algorithms
.
Ill
5.3.2
Model-based clustering
.113
5.3.3
Self-organizing maps
.119
5.3.4
Comparing methods
.122
5.4
Characterizing clusters
. 125
5.5
Recap
.
j9g
Exercises
. . 27
Contents
XI
6
Miscellaneous Topics
.129
6.1
Inference
.129
6.2
Longitudinal data
.134
6.3
Network data
.139
6.4
Multidimensional scaling
.145
Exercises
.151
7
Datasets
.153
7.1
Tips
.153
7.2
Australian Crabs
.154
7.3
Italian Olive Oils
.155
7.4
Flea Beetles
.157
7.5
PRIM7
.157
7.6
Tropical Atmosphere-Ocean Array (TAO)
.159
7.7
Primary Biliary Cirrhosis (PBC)
.161
7.8
Spam
.162
7.9
Wages
.164
7.10
Rat Gene Expression
.166
7.11
Arabidopsis Gene Expression
.168
7.12
Music
.171
7.13
Cluster Challenge
.172
7.14
Adjacent Transposition Graph
.172
7.15
Florentine Families
.173
7.16
Morse Code Confusion Rates
.174
7.17
Personal Social Network
.175
References
.177
Index
.185 |
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| discipline | Informatik Soziologie Mathematik |
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| id | DE-604.BV023210709 |
| illustrated | Illustrated |
| index_date | 2024-07-02T20:11:54Z |
| indexdate | 2024-07-09T21:13:09Z |
| institution | BVB |
| isbn | 9780387717616 9780387717623 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016396769 |
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| owner_facet | DE-703 DE-91G DE-BY-TUM DE-706 DE-19 DE-BY-UBM DE-384 DE-2070s DE-188 DE-11 DE-20 DE-29 |
| physical | XVII, 188 S. Ill., zahlr. graph. Darst. |
| publishDate | 2007 |
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| spelling | Cook, Dianne Verfasser aut Interactive and dynamic graphics for data analysis with R and GGobi Dianne Cook ; Deborah F. Swayne Use R New York, NY Springer 2007 XVII, 188 S. Ill., zahlr. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Use R! R Programm (DE-588)4705956-4 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Visualisierung (DE-588)4188417-6 gnd rswk-swf Visualisierung (DE-588)4188417-6 s Data Mining (DE-588)4428654-5 s DE-604 Datenanalyse (DE-588)4123037-1 s R Programm (DE-588)4705956-4 s DE-188 Swayne, Deborah F. Verfasser aut Erscheint auch als Online-Ausgabe 10.1007/978-0-387-71762-3 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016396769&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Cook, Dianne Swayne, Deborah F. Interactive and dynamic graphics for data analysis with R and GGobi R Programm (DE-588)4705956-4 gnd Datenanalyse (DE-588)4123037-1 gnd Data Mining (DE-588)4428654-5 gnd Visualisierung (DE-588)4188417-6 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4123037-1 (DE-588)4428654-5 (DE-588)4188417-6 |
| title | Interactive and dynamic graphics for data analysis with R and GGobi |
| title_alt | Use R |
| title_auth | Interactive and dynamic graphics for data analysis with R and GGobi |
| title_exact_search | Interactive and dynamic graphics for data analysis with R and GGobi |
| title_exact_search_txtP | Interactive and dynamic graphics for data analysis with R and GGobi |
| title_full | Interactive and dynamic graphics for data analysis with R and GGobi Dianne Cook ; Deborah F. Swayne |
| title_fullStr | Interactive and dynamic graphics for data analysis with R and GGobi Dianne Cook ; Deborah F. Swayne |
| title_full_unstemmed | Interactive and dynamic graphics for data analysis with R and GGobi Dianne Cook ; Deborah F. Swayne |
| title_short | Interactive and dynamic graphics for data analysis |
| title_sort | interactive and dynamic graphics for data analysis with r and ggobi |
| title_sub | with R and GGobi |
| topic | R Programm (DE-588)4705956-4 gnd Datenanalyse (DE-588)4123037-1 gnd Data Mining (DE-588)4428654-5 gnd Visualisierung (DE-588)4188417-6 gnd |
| topic_facet | R Programm Datenanalyse Data Mining Visualisierung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016396769&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT cookdianne interactiveanddynamicgraphicsfordataanalysiswithrandggobi AT swaynedeborahf interactiveanddynamicgraphicsfordataanalysiswithrandggobi AT cookdianne user AT swaynedeborahf user |