Modern applied biostatistical methods using S-Plus / / Steve Selvin

• Autore: Selvin S.
• Pubblicazione: New York ; , : Oxford University Press, , 2023
• Edizione: 1st ed.
• Descrizione fisica: xiv, 461 p. : ill
• Disciplina: 570/.1/5195
• Soggetto topico: Biometry; Biology - Data processing; S (Computer program language)
• Note generali: Includes index.; Previously issued in print: 1998.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Contents -- 1. S-language -- In the beginning -- Three data types-and some input conventions -- Reading values into SPLUS -- S-tools-a beginning set -- S-arithmetic -- More S-tools-intermediate set -- S-tools for statistics -- Statistical distributions in SPLUS -- Arrays and tables -- Matrix algebra tools -- Some additional S-tools -- Four S-code examples -- The .Data file -- Addendum: Built-in editors -- Problem set I -- 2. Descriptive Techniques -- Description of descriptive statistics -- Basic statistical measures -- Histogram smoothing-density estimation -- Stem-and-leaf display -- Comparison of groups-t-test -- Comparison of groups-boxplots -- Comparison of data to a theoretical distribution-quantile plots -- Comparison of groups-qqplots -- xy-plot -- Three-dimensional plots-perspective plots -- Three-dimensional plots-contour plots -- Three-dimensional plots-rotation -- Smoothing -- Two-dimensional smoothing of spatial data -- Clusters as a description of data -- Additivity-"sweeping" an array -- Example-geographic calculations using S-functions -- Estimation of the center of a two-dimensional distribution -- Addendum: S-geometry -- Problem set II -- 3. Simulation: Random Values -- Random uniform values -- An example -- Sampling without and with replacement -- Random sample from a discrete probability distribution-acceptance/rejection sampling -- Random sample from a discrete probability distribution-inverse transform method -- Binomial probability distribution -- Hypergeometric probability distribution -- Poisson probability distribution -- Geometric probability distribution -- Random samples from a continuous distribution -- Inverse transform method -- Simulating values from the normal distribution -- Four other statistical distributions -- Simulating minimum and maximum values -- Butler's method. ; Random values over a complex region -- Multivariate normal variables -- Problem set III -- 4. General Linear Models -- Simplest case-univariate linear regression -- Multivariable case -- Multivariable linear model -- A closer look at residual values -- Predict-pointwise confidence intervals -- Formulas for glm( ) -- Polynomial regression -- Discriminant analysis -- Linear logistic model -- Categorical data-bivariate linear logistic model -- Multivariable data-linear logistic model -- Goodness-of-fit -- Poisson model -- Multivariable Poisson model -- Problem set IV -- 5. Estimation -- Estimation: Maximum Likelihood -- Estimator properties -- Maximum likelihood estimator -- Scoring to find maximum likelihood estimates -- Multiparameter estimation -- Generalized scoring -- Estimation: Bootstrap -- Background -- General outline -- Sample mean from a normal population -- Confidence limits -- An example-relative risk -- Median -- Simple linear regression -- Jackknife estimation -- Bias estimation -- Two-sample test-bootstrap approach -- Two-sample test-randomization approach -- Estimation: Least Squares -- Least squares properties -- Non-linear least squares estimation -- Problem set V -- 6. Analysis of Tabular Data -- Two by two tables -- Matched pairs-binary response -- Two by k table -- Measures of association-2 x 2 table -- Measures of association-r x c table -- Measures of association-table with ordinal variables -- Loglinear model -- Multidimensional-k-level variables -- High dimensional tables -- Problem set VI -- 7. Analysis of Variance and Some Other S-Functions -- Analysis of variance -- One-way design -- Nested design -- Two-way classification with one observation per cell -- Matched pairs-measured response -- Two-way classification with more than one observation per cell -- Leaps-a model selection technique -- Principal components. ; Canonical correlations -- Problem set VII -- 8. Rates, Life Tables, and Survival -- Rates -- Life tables -- Survival analysis-an introduction -- Nonparametric estimation of a survival curve -- Hazard rate-estimation -- Mean/median survival time -- Proportional hazards model -- Problem set VIII -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- X -- Y -- Z.
• Sommario/riassunto: Statistical analysis typically involves applying theoretically generated techniques to the description and interpretation of collected data. This text combines theory, application and interpretation to create an entire biostatistical process.
• Titolo autorizzato: Modern applied biostatistical methods using S-Plus
• ISBN: 0-19-773778-1; 1-280-75989-5; 9786610759897; 0-19-974773-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910974626203321
• Lo trovi qui: Univ. Federico II
• Serie: Monographs in epidemiology and biostatistics ; ; v.28.; Oxford scholarship online.