23: Fit a Bayesian Model with RStan
Learning objectives
23: Fit a Bayesian Model with RStan
Learning objectives - Video Tutorials & Practice Problems
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<v ->Bayesian data analysis uses simulations</v> to fit both simple and complex models. Andrew Gelman's new language, Stan, makes this fast and easier than ever before. We explore this by fitting a simple linear regression in a varying intercept multi level model.
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Table of contents
- Part 1: R as a Tool — Introduction3m
- 1: Getting Started with R30m
- 2: The Basic Building Blocks in R40m
- 3: Advanced Data Structures in R36m
- 4: Reading Data into R54m
- 5: Making Statistical Graphs1h 8m
- Learning objectives0m
- 5.1 Find the diamonds in the data1m
- 5.2 Make histograms with base graphics1m
- 5.3 Make scatterplots with base graphics1m
- 5.4 Make boxplots with base graphics1m
- 5.5 Get familiar with ggplot22m
- 5.6 Plot histograms and densities with ggplot23m
- 5.7 Make scatterplots with ggplot25m
- 5.8 Make boxplots and violin plots with ggplot24m
- 5.9 Make line plots8m
- 5.10 Create small multiples4m
- 5.11 Control colors and shapes1m
- 5.12 Add themes to graphs2m
- 5.13 Use Web graphics29m
- 6: Basics of Programming51m
- Learning objectives0m
- 6.1 Write the classic "Hello, World!" example2m
- 6.2 Understand the basics of function arguments10m
- 6.3 Return a value from a function2m
- 6.4 Gain flexibility with do.call3m
- 6.5 Use "if" statements to control program flow2m
- 6.6 Stagger "if" statements with "else"5m
- 6.7 Check multiple statements with switch3m
- 6.8 Run checks on entire vectors5m
- 6.9 Check compound statements5m
- 6.10 Iterate with a for loop6m
- 6.11 Iterate with a while loop1m
- 6.12 Control loops with break and next2m
- 7: Data Munging1h 15m
- Learning objectives0m
- 7.1 Repeat an operation on a matrix using apply4m
- 7.2 Repeat an operation on a list3m
- 7.3 Apply a function over multiple lists with mapply4m
- 7.4 Perform group summaries with the aggregate function5m
- 7.5 Do group operations with the plyr Package17m
- 7.6 Combine datasets3m
- 7.7 Join datasets5m
- 7.8 Switch storage paradigms5m
- 7.9 Use tidyr2m
- 7.10 Get faster group operations21m
- 8: In-Depth with dplyr23m
- 9: Manipulating Strings39m
- 10: Reports and Slideshows with knitr36m
- Learning objectives0m
- 10.1 Understand the basics of LaTeX7m
- 10.2 Weave R code into LaTeX using knitr5m
- 10.3 Understand the basics of Markdown2m
- 10.4 Understand the basics of RMarkdown4m
- 10.5 Weave R code into Markdown using knitr2m
- 10.6 Convert Markdown files to Word1m
- 10.7 Convert Markdown to PDF1m
- 10.8 Create slideshows with RMarkdown3m
- 10.9 Write equations with RMarkdown7m
- 11: Include HTML Widgets in HTML Documents22m
- 12: Shiny22m
- 13: Package Building23m
- 14: Rcpp for Faster Code33m
- Part 1 - Summary1m
- Part 2: R for Statistics, Modeling and Machine Learning — Introduction2m
- 15: Basic Statistics56m
- 16: Linear Models1h 38m
- Learning objectives0m
- 16.1 Fit simple linear models10m
- 16.2 Explore the data8m
- 16.3 Fit multiple regression models19m
- 16.4 Fit logistic regression10m
- 16.5 Fit Poisson regression7m
- 16.6 Analyze survival data12m
- 16.7 Assess model quality with residuals5m
- 16.8 Compare models7m
- 16.9 Judge accuracy using cross-validation9m
- 16.10 Estimate uncertainty with the bootstrap6m
- 16.11 Choose variables using stepwise selection2m
- 17: Other Models47m
- 18: Time Series30m
- 19: Clustering20m
- 20: More Machine Learning26m
- 21: Network Analysis36m
- 22: Automatic Parameter Tuning with Caret10m
- 23: Fit a Bayesian Model with RStan15m
- Part 2 - SummaryComing soon