06442nam 2200649 a 450 991020883060332120230725024416.01-283-13877-80-470-93725-497866131387740-470-93727-01-118-00214-8(CKB)4330000000000530(MiAaPQ)EBC706906(MiAaPQ)EBC4030238(Au-PeEL)EBL706906(CaPaEBR)ebr10452145(CaONFJC)MIL313877(OCoLC)708036686(EXLCZ)99433000000000053020100805d2011 uy 0engurcn|||||||||rdacontentrdamediardacarrierAnalyzing the large numbers of variables in biomedical and satellite imagery[electronic resource] /Phillip I. GoodHoboken, N.J. Wileyc2011xii, 185 p. ill0-470-92714-3 Includes bibliographical references and indexes.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 T23.2.4.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 Analysis5.5.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 fMRI8.7.2.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.Data miningMathematical statisticsBiomedical engineeringData processingRemote sensingData processingFunctions of several complex variablesR (Computer program language)Data mining.Mathematical statistics.Biomedical engineeringData processing.Remote sensingData processing.Functions of several complex variables.R (Computer program language).006.3/12Good Phillip I102489MiAaPQMiAaPQMiAaPQBOOK9910208830603321Analyzing the large numbers of variables in biomedical and satellite imagery2118399UNINA