R statistics cookbook over 100 recipes for performing complex statistical operations with R 3.5
Solve real-world statistical problems using the most popular R packages and techniques Key Features Learn how to apply statistical methods to your everyday research with handy recipes Foster your analytical skills and interpret research across industries and business verticals Perform t-tests, chi-s...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England ; Mumbai :
Packt
2019.
|
| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630430606719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Table of Contents
- Preface
- Chapter 1: Getting Started with R and Statistics
- Introduction
- Technical requirements
- Maximum likelihood estimation
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Calculating densities, quantiles, and CDFs
- Getting ready
- How to do it...
- How it works...
- There's more...
- Creating barplots using ggplot
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Generating random numbers from multiple distributions
- Getting ready
- How to do it...
- How it works...
- There's more...
- Complex data processing with dplyr
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- 3D visualization with the plot3d package
- Getting ready
- How to do it...
- How it works...
- Formatting tabular data with the formattable package
- Getting ready
- How to do it...
- How it works...
- There's more...
- Simple random sampling
- Getting ready
- How to do it...
- How it works...
- Creating diagrams via the DiagrammeR package
- Getting ready
- How to do it...
- How it works...
- See also
- C++ in R via the Rcpp package
- Getting ready
- How to do it...
- How it works...
- See also
- Interactive plots with the ggplot GUI package
- Getting ready
- How to do it...
- How it works...
- There's more...
- Animations with the gganimate package
- Getting ready
- How to do it...
- How it works...
- See also
- Using R6 classes
- Getting ready
- How to do it...
- How it works...
- There's more...
- Modeling sequences with the TraMineR package
- Getting ready
- How to do it...
- How it works...
- There's more...
- Clustering sequences with the TraMineR package
- Getting ready
- How to do it.
- How it works...
- There's more...
- Displaying geographical data with the leaflet package
- Getting ready
- How to do it...
- How it works...
- Chapter 2: Univariate and Multivariate Tests for Equality of Means
- Introduction
- The univariate t-test
- Getting ready
- How to do it...
- How it works...
- There's more...
- The Fisher-Behrens problem
- How to do it...
- How it works...
- There's more...
- Paired t-test
- How to do it...
- How it works...
- There's more...
- Calculating ANOVA sum of squares and F tests
- How to do it...
- Two-way ANOVA
- How to do it...
- How it works...
- There's more...
- Type I, Type II, and Type III sum of squares
- Type I
- Type II
- Type III
- Getting ready
- How to do it...
- How it works...
- Random effects
- Getting ready
- How to do it...
- How it works...
- There's more...
- Repeated measures
- Getting ready
- How to do it...
- How it works...
- There's more...
- Multivariate t-test
- Getting ready...
- How to do it...
- How it works...
- There's more...
- MANOVA
- Getting ready
- How to do it...
- How it works...
- There's more...
- Chapter 3: Linear Regression
- Introduction
- Computing ordinary least squares estimates
- How to do it...
- How it works...
- Reporting results with the sjPlot package
- Getting ready
- How to do it...
- How it works...
- There's more...
- Finding correlation between the features
- Getting ready...
- How to do it...
- Testing hypothesis
- Getting ready
- How to do it...
- How it works...
- Testing homoscedasticity
- Getting ready
- How to do it...
- How it works...
- Implementing sandwich estimators
- Getting ready
- How to do it...
- How it works...
- Variable selection
- Getting ready
- How to do it...
- How it works...
- Ridge regression
- Getting ready
- How to do it...
- How it works.
- Working with LASSO
- Getting ready
- How to do it...
- How it works...
- There's more...
- Leverage, residuals, and influence
- Getting ready
- How to do it...
- How it works...
- Chapter 4: Bayesian Regression
- Introduction
- Getting the posterior density in STAN
- Getting ready
- How to do it...
- How it works...
- Formulating a linear regression model
- Getting ready
- How to do it...
- How it works...
- There's more...
- Assigning the priors
- Defining the support
- How to decide the parameters for a prior
- Getting ready
- How to do it...
- How it works...
- Doing MCMC the manual way
- Getting ready
- How to do it...
- How it works...
- Evaluating convergence with CODA
- One or multiple chains?
- Getting ready
- How to do it...
- How it works...
- There's more...
- Bayesian variable selection
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Using a model for prediction
- Getting ready
- How to do it...
- How it works...
- GLMs in JAGS
- Getting ready
- How to do it...
- How it works...
- Chapter 5: Nonparametric Methods
- Introduction
- The Mann-Whitney test
- How to do it...
- How it works...
- There's more...
- Estimating nonparametric ANOVA
- Getting ready
- How to do it...
- How it works...
- The Spearman's rank correlation test
- How to do it...
- How it works...
- There's more...
- LOESS regression
- Getting ready
- How to do it...
- How it works...
- There's more...
- Finding the best transformations via the acepack package
- Getting ready
- How to do it...
- How it works...
- There is more...
- Nonparametric multivariate tests using the npmv package
- Getting ready
- How to do it...
- How it works...
- Semiparametric regression with the SemiPar package
- Getting ready
- How to do it...
- How it works...
- There's more.
- Chapter 6: Robust Methods
- Introduction
- Robust linear regression
- Getting ready
- How to do it...
- How it works...
- Estimating robust covariance matrices
- Getting ready
- How to do it...
- How it works...
- Robust logistic regression
- Getting ready
- How to do it...
- How it works...
- Robust ANOVA using the robust package
- Getting ready
- How to do it...
- How it works...
- Robust principal components
- Getting ready
- How to do it...
- How it works...
- Robust Gaussian mixture models with the qclust package
- Getting ready
- How to do it...
- How it works...
- Robust clustering
- Getting ready
- How to do it...
- How it works...
- Chapter 7: Time Series Analysis
- Introduction
- The general ARIMA model
- Getting ready
- How to do it...
- How it works...
- Seasonality and SARIMAX models
- Getting ready
- How to do it...
- There's more...
- Choosing the best model with the forecast package
- Getting ready
- How to do it...
- How it works...
- Vector autoregressions (VARs)
- Getting ready
- How to do it...
- How it works...
- Facebook's automatic Prophet forecasting
- Getting ready
- How to do it...
- How it works...
- There's more...
- Modeling count temporal data
- Getting ready
- How to do it...
- There's more...
- Imputing missing values in time series
- Getting ready
- How to do it...
- How it works...
- There's more...
- Anomaly detection
- Getting ready
- How to do it...
- How it works...
- There's more...
- Spectral decomposition of time series
- Getting ready
- How to do it...
- How it works...
- Chapter 8: Mixed Effects Models
- Introduction
- The standard model and ANOVA
- Getting ready
- How to do it...
- How it works...
- Some useful plots for mixed effects models
- Getting ready
- How to do it...
- There's more...
- Nonlinear mixed effects models.
- Getting ready
- How to do it...
- How it works...
- There's more...
- Crossed and nested designs
- Crossed design
- Nested design
- Getting ready
- How to do it...
- How it works..
- Robust mixed effects models with robustlmm
- Getting ready
- How to do it...
- How it works...
- Choosing the best linear mixed model
- Getting ready
- How to do it...
- How it works...
- Mixed generalized linear models
- Getting ready
- How to do it...
- How it works...
- There's more...
- Chapter 9: Predictive Models Using the Caret Package
- Introduction
- Data splitting and general model fitting
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Preprocessing
- Getting ready
- How to do it...
- How it works...
- Variable importance and feature selection
- Getting ready
- How to do it...
- How it works...
- Model tuning
- Getting ready
- How to do it...
- How it works...
- Classification in caret and ROC curves
- Getting ready
- How to do it...
- How it works...
- Gradient boosting and class imbalance
- Getting ready
- How to do it...
- How it works...
- Lasso, ridge, and elasticnet in caret
- Getting ready
- How to do it...
- How it works...
- Logic regression
- Getting ready
- How to do it...
- How it works...
- Chapter 10: Bayesian Networks and Hidden Markov Models
- Introduction
- A discrete Bayesian network via bnlearn
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Conditional independence tests
- Getting ready
- How to do it...
- How it works...
- There's more...
- Continuous and hybrid Bayesian networks via bnlearn
- Getting ready
- How to do it...
- How it works...
- See also
- Interactive visualization of BNs with the bnviewer package
- Getting ready
- How to do it...
- How it works.
- An introductory hidden Markov model.
