Introduction | Installation | Code of Conduct | Contributions | Commit messages | Credit where credit is due | Coding style | Contact | Session info

Introduction | Scope | Functions | Writing custom functions | Conditionals | Map/Apply | Plotting | Plot framework | Basic statistical testing | Exercises

Introduction | R and Rstudio | Setting up your system | Directory Structure | Projects | Setting up a project | Project organization | Git and github | Setting up git and github for the first time on a system | Set up your project to use git and github | Using git and github | Packages | Installing packages | Get a new project library | Exercises

Introduction | Data formatting | Input and data types | More on data types | Variable assignment | Data structures | Subsetting with base R functions | Data operations | Dplyr | Exercises

Introduction | Saving R objects | Setting up the package | Adding data | Adding data-processing code | Option: include raw data | Documenting data | Finish up the data package | A note about saving single cell RNA-seq data | Installing the Data Package | Exercises

Introduction | Modelling strategy | Model specification | Probability distribution of censored dependent variable | Likelihood function | Model evaluation and selection using goodness of fit measures | Model selection using Vuong tests | Prediction and marginal effects | Software rationale | Estimation | Tests | Bibliography

Basic Use: Plotting Results from One or More Regression Models | Advanced Use: Decoration and Modification | Plotting Results Stored in a Tidy Data Frame | Grouping Predictors | Presenting Regression Results as Normal Distributions | The 'Secret Weapon' and 'Small Multiple' Plots | Model Fit Statistics | Conclusion | Affiliation | References

Figure 6: Presenting a Single Regression Model Using a Dot Plot with Error Bars. | Figure 7: Using Parallel Dot Plots with Error Bars to Present Two Regression Models. | Figure 8: Using "Small Multiple" Plots to Present Regression Results from Several Models. | Affiliation | References

1. Prepare/download the input regions | 2. Load the RcisTarget databases | 3. Run the analysis

What is RcisTarget? | Overview of the workflow | Before starting | Setup | Some tips... | Help | Reports | Running RcisTarget | Input: Gene sets | Required databases | Gene-motif rankings | Motif annotations | Database example (subset) | Running the analysis | Advanced: Execute step by step | 1. Calculate enrichment | 2. Select significant motifs and/or annotate to TFs | 3. Identify the genes with the best enrichment for each Motif | Output | Follow up examples | TFs annotated to the enriched motifs | Building a network | sessionInfo()

1. Select the gene/region set to analyze & the "background" | 2. Create the background-ranking | 3. Run RcisTarget with this new ranking

Motivation | Using Optional Arguments in margins() | Average Marginal Effects and Average Partial Effects | Using the at Argument | Subsetting | Marginal Effects Plots | The plot() method for "margins" objects | More advanced plotting | Interpreting Interactions with Marginal Effects | Interactions in OLS | ggplot2 examples | Interactions in Logit | References

OLS marginal effects | Stata | R | OLS marginal effects with interaction | OLS marginal effects with factor term | OLS marginal effects with squared term | OLS marginal effects with squared term (but no first-order term) | Logit effects on log-odds and probability scales | Logit effects with factor variable on log-odds and probability scales | Logit with interaction on probability and Log-Odds scales | Probit effects on latent and probability scales | Poisson effects on latent and probability scales