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Introduction, Installation and Basic Principles
Data frames
Types of data
Data visualization with graphs
A useful graphics package
Coloring the points by a grouping variable
Jittered points
Line graphs
Barplots
Barplots with Multiple Groups
Stacked barplots
Boxplots
Grouped boxplots
Notched boxplots
Violin plot
Combination graphs
Flipping the axes
Multiple variable plot
Regression line
Non-linear Smooth curve
Density plot
Plotting vertical and horizontal lines
Putting text on plot
Tile plot
Histogram
Histogram using ggplot2
Angled x-axis labels
Adding title and axis labels to plot of base R
Adding title and axis-labels to ggplots
Lineplot with points and confidence intervals
Prism Plot
Type of distributions
Test to assess distribution
Converting non-normal to normal distributions
Summarizing data
Stars plot
Tableplot
Creating a 3-D scatterplot
Mouse - rotatable 3D plots
Parametric versus non-parametric tests
Testing correlation
Graphical representation of correlation
Correlation matrix
Item Cluster Analysis
Omega estimates
Creating clusters from data
Score-based Structure Learning
Summary of data by group
Parallel Coordinate Plot
Linear Discriminant Analysis
Creating algorithm using classfication and trees
Rotatable 3d plots with principal component analysis
Comparing 2 groups: unpaired data
Graphical representation of 2 numeric groups
Effect size for comparison of 2 numeric groups
Graphical representation of paired data
Comparing many groups
Graphical representation of multiple numeric groups
Contingency tables
Graphical representation of contingency table
Effect size: Odds ratio
Multiple regression
Multiple regression for binary outcome
Multiple regression for ordinal outcome
Multiple regression for multiple categorical outcome (multinomial regression)
Regression diagnostics
Repeated measures ANOVA
Analysis of Pre-Post Treatment-Control studies
Graphic visualization of pre-post active-control data
Stepwise regression
Linear regression using package rms
Logistic and ordinal regression using rms package
Regression after Box-Cox transformation
RegBest of FactoMineR package
Most important predictors with bestglm package
Robust regression
Robust regression using robustbase package
Bayesian regression
MCMCregress of MCMCpack (Markov Chain Monte Carlo)
Ridge, Elastic net and Lasso regression
Partial least squares (pls) regression
Relative importance of predictors
Principal component analysis
Factor analysis
Multiple correpondence analysis
Dendrograms
Multidimensional Scaling
Andrews curves
Cluster analysis
Canonical correlation analysis
Survival analysis
Partial Least Squares Discriminant Analysis
Growth charts
Structural equation modelling
Meta-analysis
Bootstrap
Evaluating a new test: Sensitivity, specificity, ROC curve & other parameters
Nomograms
Creating predictive models
Bland Altman Plot
Author and Reviewers
Citations
Title
Text
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Most important predictors with bestglm package
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