Categories
- RBasics 16
- Univariate 25
- Regression 9
- Multivariate 9
- Descriptive 11
- ANOVA 9
- Nonparametric 13
- ClassicalNonparametric 8
- Workflow 2
- DataFrames 10
- Diagrams 13
- SpecificDiagrams 6
- Resampling 5
- BasicDiagrams 6
- Survival 3
- Numerical 3
RBasics
- Exploring the workspace
- Generate systematic and random data
- Recode variables
- Time and date
- Vectors
- Character strings
- Arithmetic with matrices
- Sets
- Matrices and arrays
- Combinatorics
- Factors: Representing categorical data
- Add-on packages
- Variable transformations
- Basic arithmetic and logic
- Getting help and documentation
- R user interfaces
Univariate
- Robust and penalized regression
- Analysis of covariance (ANCOVA)
- Multiple linear regression
- Regression diagnostics
- Two-way repeated-measures ANOVA (RBF-pq design)
- Binary logistic regression
- Mixed-effects models for repeated-measures ANOVA
- Survival analysis: Cox proportional hazards model
- One-way ANOVA (CR-p design)
- Survival analysis: Parametric proportional hazards models
- One-way repeated measures ANOVA (RB-p design)
- Crossvalidation and bootstrap prediction error for linear and generalized linear models
- Ordinal regression
- Two-way ANOVA (CRF-pq design)
- Survival analysis: Kaplan-Meier
- Multinomial regression
- Sum of squares type I, II, and III
- Assess univariate and multivariate normality
- Three-way split-plot-factorial ANOVA (SPF-pq.r and SPF-p.qr design)
- Split-plot-factorial ANOVA (SPF-p.q design)
- Moderated and mediated linear regression
- Poisson-regression
- Log-linear models
- Variance homogeneity in two or more groups
- t-tests
Regression
- Robust and penalized regression
- Multiple linear regression
- Regression diagnostics
- Binary logistic regression
- Ordinal regression
- Multinomial regression
- Moderated and mediated linear regression
- Poisson-regression
- Log-linear models
Multivariate
- Hotelling’s T^2-test
- Principal components analysis
- Exploratory factor analysis
- Linear discriminant analysis (LDA)
- Multivariate analysis of variance (MANOVA)
- Multidimensional scaling (MDS)
- Exploratory factor analysis for ordinal categorical data
- Linear algebra calculations
- Multivariate multiple regression
Descriptive
- Sum, product, and range of data
- Descriptive statistics for data in matrices
- Association tests and measures for ordered categorical variables
- Association tests and measures for unordered categorical variables
- The mean and other location measures
- Frequency tables
- Aggregate data vectors by group
- Inter-rater-reliability and Intra-class-correlation
- Variance, robust spread measures, skewness and kurtosis
- Covariance, correlation, association measures for continuous variables
- Deal with missing data
ANOVA
- Analysis of covariance (ANCOVA)
- Two-way repeated-measures ANOVA (RBF-pq design)
- Mixed-effects models for repeated-measures ANOVA
- One-way ANOVA (CR-p design)
- One-way repeated measures ANOVA (RB-p design)
- Two-way ANOVA (CRF-pq design)
- Sum of squares type I, II, and III
- Three-way split-plot-factorial ANOVA (SPF-pq.r and SPF-p.qr design)
- Split-plot-factorial ANOVA (SPF-p.q design)
Nonparametric
- Runs-test
- Parametric bootstrapping
- Nonparametric location tests for one and two samples
- Nonparametric bootstrapping linear models
- Stuart-Maxwell-test for marginal homogeneity
- Binomial test and chi^2-test for proportions
- Nonparametric location tests for more than two samples
- Cochran-Q-test
- McNemar-test
- Nonparametric bootstrapping
- Permutation tests
- Nonparametric stratified bootstrapping
- Bowker-test
ClassicalNonparametric
- Runs-test
- Nonparametric location tests for one and two samples
- Stuart-Maxwell-test for marginal homogeneity
- Binomial test and chi^2-test for proportions
- Nonparametric location tests for more than two samples
- Cochran-Q-test
- McNemar-test
- Bowker-test
Workflow
DataFrames
- Split and combine data frames
- Choose names, variables, cases - base R
- Choose names, variables, cases - dplyr
- Create and analyze data frames
- Split and combine data frames - dplyr
- Reshape data frames: wide and long format
- Aggregate data frames - base R
- Data import and export
- Reshape data frames - tidyr
- Aggregate data frames by group - dplyr
Diagrams
- Linear interpolation, polynomial smoothing, and splines
- Diagram types with the ggplot2 package
- Color, line type, and point shape in ggplot2 diagrams
- Visualize univariate and bivariate distributions
- Scatter plots and line diagrams
- Customize diagrams: Formatting
- Diagrams for multivariate data
- Diagrams for categorical data
- Convex hull, (minimum) bounding box, and minimum enclosing circle
- Graphics devices: Opening and saving diagrams
- Customize diagrams: Add additional elements
- Multiple diagrams per window or device
- Format ggplot2 diagrams
SpecificDiagrams
- Linear interpolation, polynomial smoothing, and splines
- Visualize univariate and bivariate distributions
- Diagrams for multivariate data
- Diagrams for categorical data
- Convex hull, (minimum) bounding box, and minimum enclosing circle
- Multiple diagrams per window or device
Resampling
- Parametric bootstrapping
- Nonparametric bootstrapping linear models
- Nonparametric bootstrapping
- Permutation tests
- Nonparametric stratified bootstrapping
BasicDiagrams
- Diagram types with the ggplot2 package
- Color, line type, and point shape in ggplot2 diagrams
- Scatter plots and line diagrams
- Customize diagrams: Formatting
- Graphics devices: Opening and saving diagrams
- Customize diagrams: Add additional elements
Survival
- Survival analysis: Cox proportional hazards model
- Survival analysis: Parametric proportional hazards models
- Survival analysis: Kaplan-Meier
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