Data Analysis and Graphics Using R: An Example-based Approach
Join the revolution ignited by the ground-breaking R system! Starting with an introduction to R, covering standard regression methods, then presenting more advanced topics, this book guides users through the practical and powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display and interpretation of data. The many worked examples, taken from real-world research, are accompanied by commentary on what is done and why. A website provides computer code and data sets, allowing readers to reproduce all analyses. Updates and solutions to selected exercises are also available. Assuming only basic statistical knowledge, the book is ideal for research scientists, final-year undergraduate or graduate level students of applied statistics, and practising statisticians. It is both for learning and for reference. This revised edition reflects changes in R since 2003 and has new material on survival analysis, random coefficient models, and the handling of high-dimensional data.
Τι λένε οι χρήστες - Σύνταξη κριτικής
Δεν εντοπίσαμε κριτικές στις συνήθεις τοποθεσίες.
Styles of data analysis
Multiple linear regression
Exploiting the linear model framework
Generalized linear models and survival analysis
Time series models
Άλλες εκδόσεις - Προβολή όλων
alternative analysis of variance argument assumptions autocorrelation bodywt bootstrap boxplots calculations Chapter coefficients columns comparison confidence interval correlation cross-validation DAAG DAAG package data frame data set default degrees of freedom density deviance discriminant discussion Error t value Estimate Std example explanatory variables F-statistic factor female Figure fitted values function gives graphics graphs groups included Intercept lattice levels linear logarithmic logarithmic scale logistic logistic regression Maindonald males mean square measures median methodology methods model matrix multiple normal distribution Note objects observations obtained outliers output p-value panel parameter points population possible predictive accuracy principal components propensity score random effects regression model residuals rows rpart S-PLUS scale scatterplot Scatterplot matrix scores shows simulation slope specify spline split standard deviation standard error statistical structure Subsection sum of squares summary Table transformation treatment tree tree-based value Pr(>|t variation vector weight workspace
Σελίδα 478 - Smyth, GK 2004. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments.
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