Analyzing the large numbers of variables in biomedical and satellite imagery / / Phillip I. Good

• Record Nr.: UNINA9910825098303321
• Autore: Good Phillip I
• Pubbl/distr/stampa: Hoboken, N.J., : Wiley, c2011
• ISBN: 1-283-13877-8; 0-470-93725-4; 9786613138774; 0-470-93727-0; 1-118-00214-8
• Edizione: [1st ed.]
• Descrizione fisica: xii, 185 p. : ill
• Disciplina: 006.3/12
• Soggetti: Data mining; Mathematical statistics; Biomedical engineering - Data processing; Remote sensing - Data processing; Functions of several complex variables; R (Computer program language)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and indexes.
• Nota di contenuto: Machine generated contents note: ; 1. Very Large Arrays -- ; 1.1. Applications -- ; 1.2. Problems -- ; 1.3. Solutions -- ; 2. Permutation Tests -- ; 2.1. Two-Sample Comparison -- ; 2.1.1. Blocks -- ; 2.2. k-Sample Comparison -- ; 2.3. Computing The p-Value -- ; 2.3.1. Monte Carlo Method -- ; 2.3.2. An R Program -- ; 2.4. Multiple-Variable Comparisons -- ; 2.4.1. Euclidean Distance Matrix Analysis -- ; 2.4.2. Hotelling's T2 -- ; 2.4.3. Mantel's U -- ; 2.4.4. Combining Univariate Tests -- ; 2.4.5. Gene Set Enrichment Analysis -- ; 2.5. Categorical Data -- ; 2.6. Software -- ; 2.7. Summary -- ; 3. Applying the Permutation Test -- ; 3.1. Which Variables Should Be Included? -- ; 3.2. Single-Value Test Statistics -- ; 3.2.1. Categorical Data -- ; 3.2.2. A Multivariate Comparison Based on a Summary Statistic -- ; 3.2.3. A Multivariate Comparison Based on Variants of Hotelling's T2; ; 3.2.4. Adjusting for Covariates -- ; 3.2.5. Pre-Post Comparisons -- ; 3.2.6. Choosing a Statistic: Time-Course Microarrays -- ; 3.3. Recommended Approaches -- ; 3.4. To Learn More -- ; 4. Biological Background -- ; 4.1. Medical Imaging -- ; 4.1.1. Ultrasound -- ; 4.1.2. EEG/MEG -- ; 4.1.3. Magnetic Resonance Imaging -- ; 4.1.3.1. MRI -- ; 4.1.3.2. fMRI -- ; 4.1.4. Positron Emission Tomography -- ; 4.2. Microarrays -- ; 4.3. To Learn More -- ; 5. Multiple Tests -- ; 5.1. Reducing the Number of Hypotheses to Be Tested -- ; 5.1.1. Normalization -- ; 5.1.2. Selection Methods -- ; 5.1.2.1. Univariate Statistics -- ; 5.1.2.2. Which Statistic? -- ; 5.1.2.3. Heuristic Methods -- ; 5.1.2.4. Which Method? -- ; 5.2. Controlling the Over All Error Rate -- ; 5.2.1. An Example: Analyzing Data from Microarrays -- ; 5.3. Controlling the False Discovery Rate -- ; 5.3.1. An Example: Analyzing Time-Course Data from Microarrays -- ; 5.4. Gene Set Enrichment Analysis; ; 5.5. Software for Performing Multiple Simultaneous Tests -- ; 5.5.1. AFNI -- ; 5.5.2. Cyber-T -- ; 5.5.3. dChip -- ; 5.5.4. ExactFDR -- ; 5.5.5. GESS -- ; 5.5.6. HaploView -- ; 5.5.7. MatLab -- ; 5.5.8. R -- ; 5.5.9. SAM -- ; 5.5.10. ParaSam -- ; 5.6. Summary -- ; 5.7. To Learn More -- ; 6. The Bootstrap -- ; 6.1. Samples and Populations -- ; 6.2. Precision of an Estimate -- ; 6.2.1. R Code -- ; 6.2.2. Applying the Bootstrap -- ; 6.2.3. Bootstrap Reproducibility Index -- ; 6.2.4. Estimation in Regression Models -- ; 6.3. Confidence Intervals -- ; 6.3.1. Testing for Equivalence -- ; 6.3.2. Parametric Bootstrap -- ; 6.3.3. Blocked Bootstrap -- ; 6.3.4. Balanced Bootstrap -- ; 6.3.5. Adjusted Bootstrap -- ; 6.3.6. Which Test? -- ; 6.4. Determining Sample Size -- ; 6.4.1. Establish a Threshold -- ; 6.5. Validation -- ; 6.5.1. Cluster Analysis -- ; 6.5.2. Correspondence Analysis -- ; 6.6. Building a Model -- ; 6.7. How Large Should The Samples Be?; ; 6.8. Summary -- ; 6.9. To Learn More -- ; 7. Classification Methods -- ; 7.1. Nearest Neighbor Methods -- ; 7.2. Discriminant Analysis -- ; 7.3. Logistic Regression -- ; 7.4. Principal Components -- ; 7.5. Naive Bayes Classifier -- ; 7.6. Heuristic Methods -- ; 7.7. Decision Trees -- ; 7.7.1. A Worked-Through Example -- ; 7.8. Which Algorithm Is Best for Your Application? -- ; 7.8.1. Some Further Comparisons -- ; 7.8.2. Validation Versus Cross-validation -- ; 7.9. Improving Diagnostic Effectiveness -- ; 7.9.1. Boosting -- ; 7.9.2. Ensemble Methods -- ; 7.9.3. Random Forests -- ; 7.10. Software for Decision Trees -- ; 7.11. Summary -- ; 8. Applying Decision Trees -- ; 8.1. Photographs -- ; 8.2. Ultrasound -- ; 8.3. MRI Images -- ; 8.4. EEGs and EMGs -- ; 8.5. Misclassification Costs -- ; 8.6. Receiver Operating Characteristic -- ; 8.7. When the Categories Are As Yet Undefined -- ; 8.7.1. Unsupervised Principal Components Applied to fMRI; ; 8.7.2. Supervised Principal Components Applied to Microarrays -- ; 8.8. Ensemble Methods -- ; 8.9. Maximally Diversified Multiple Trees -- ; 8.10. Putting It All Together -- ; 8.11. Summary -- ; 8.12. To Learn More -- Glossary of Biomedical Terminology -- Glossary of Statistical Terminology -- Appendix: An R Primer -- ; R1. Getting Started -- ; R1.1. R Functions -- ; R1.2. Vector Arithmetic -- ; R2. Store and Retrieve Data -- ; R2.1. Storing and Retrieving Files from Within R -- ; R2.2. The Tabular Format -- ; R2.3. Comma Separated Format -- ; R3. Resampling -- ; R3.1. The While Command -- ; R4. Expanding R's Capabilities -- ; R4.1. Downloading Libraries of R Functions -- ; R4.2. Programming Your Own Functions.