Lectures Click on lecture titles to view slides or the buttons to download them as PDFs. Topic 1 Introduction to the Course, Probability, and R You may want to read through Kevin Quinn’s matrix algebra and probability distribution reviews, or consult my undergrad lectures on discrete and continuous distributions. Topic 2 Introduction to Maximum Likelihood The R code to simulate heteroskedastic data and model that data using a heteroskedastic normal maximum likelihood is here. Topic 3 There are separate R scripts for interpreting and selecting binary logit models, as well as an example dataset. The goodness of fit code also relies on R functions for computing the percent correctly predicted and making predicted-versus-actual plots and ROC plots, which you should place in your working directory. An example trio of plots showing actual versus predicted probabilities, error versus predicted probabilities, and the ROC curve can be seen here. Topic 4 R code and data for an ordered probit, which produces graphics for expected value plots and first difference plots. Topic 5 R code for a multinomial logit, which produces a variety of graphical summaries of a multinomial logit model: for expected values plotted together, expected values plotted separately in a tiled format, first difference plotted for a single scenario and all categories, relative risks plotted for a single scenario and all categories, and relative risks plotted for many scenarios at once. Topic 6 Two code examples are discussed in this lecture. - The main R script to run the models, cross-validation, and graphics
- Data (csv format) from the Washington Secretary of State & US Census
- An R helper file with cross-validation functions
The second example analyzes unbounded counts using Poisson, Negative Binomial, Quasipoisson, Zero-inflated Poisson, and Zero-inflated Negative Binomial models of foreclosure filings by Houston, Texas area Home Owner Associations (HOAs). Example output includes this plot of expected values from a zero-inflated negative binomial model. You will need: - The main R script to run the models and graphics
- Data (csv format) from HOAdata.org
Advanced Topic 1 Missing Data and Multiple Imputation See the Topic 6 example on turnout for an R code using multiple imputation of missing data. Also available is an example (R script, data, plot) showing the use of overimputation to compute coverage of multiple imputation prediction intervals for real data. Advanced Topic 2 Introduction to Multilevel Models For the curious, the R script used to construct the example plots in the first half of this lecture is here. Self-Study Lecture 1 Introduction to Contingency Tables This lecture and the two below it introduce log-linear models of tabular data, and will not be presented as part of POLS/CSSS 510. They are posted here for interested students, especially for the use of mosaic plots to investigate cross-tabulated data (in this lecture, and in the third lecture on multidimensional tables). Students interested in a CSSS course on log-linear models should investigate CSSS 536. Self-Study Lecture 2 Log-linear Models of Contingency Tables: 2D tables Self-Study Lecture 3 Log-linear Models of Contingency Tables: 3D+ tables Student Assignments Due in Canvas by start of class Wednesday 11 October 2023 Due in Canvas by start of class Monday 23 October 2023 Due in Canvas by start of class Wednesday 8 November 2023 Data for problem 1 in comma-separated variable format. Due in Canvas by start of class Wednesday 22 November 2023 Data for problem 1 in comma-separated variable format. Due in Canvas by start of class Wednesday 29 November 2023 Data for problem 1 in comma-separated variable format; data for problem 2 in R data format. Poster Presentations 29 November 2023 to 6 December 2023 Requirements and suggestions for poster presentations will be presented in class. Final Paper Due Tuesday 12 December 2023, 3:00 pm by email See the syllabus for paper requirements, and see my guidelines and recommendations for quantitative research papers. Labs Lab 1 Course logistics and review of R Supplementary material: Find here the lab section syllabus. For today's lab, you will need to download the following R script and datasets: review_script.R, pop.csv, and gapminder.csv. You can download all the materials in the following ZIP file. You can access to the lab recording in this link. Lab 2 Intro to RMarkdown and Overleaf Supplementary material: Here's an RMarkdown template and today's code practice exercise. Additionally, you can access the code practice key in RMarkdown and script files. You can download all the materials in the following ZIP file. The lab recording is available at this link. Lab 3 Supplementary material: First, download the following R script file. Here's today's code practice exercise. You can now download the lab code practice solutions and the script file with all the code I used in this lab. All the materials are in the following ZIP file. The lab recording is available at this link. Lab 4 Review, Simulations, and Quantities of Interest (QoI) Supplementary material: You can access here to the lab's script file. We did not have time to start with binary models, but please feel free to review the code from this script for the binary model estimation and visualization. The lab recording is available at this link. Lab 5 Supplementary material: You can access here the lab's script file. To run the lab script, you must download the following data file and the support code "theme_caviz" for ggplot, originally created by former TA Brian Leung. You can download all the materials in the following ZIP file. Note: if you cannot download the ZIP file, please copy the link's address and paste it into your internet browser to access the ZIP file. The lab recording is available at this link. Lab 6 Goodness of Fit and Model Selection Supplementary material: You can access here the lab's script file. See RMarkdown files for HW02 solutions and HW03 pre-view. For some functions, you will need to use Chris's source code binaryGOF and binPredict as support in computing some quantities and visualization. You can download all the materials in the following ZIP file. Note: if you cannot download the ZIP file, please copy the link's address and paste it into your internet browser to access the ZIP file. The lab recording is available at this link. Lab 7 Ordinal Probit and Multinominal Logit Supplementary material: You can access here the lab's script file. This code is adapted from Chris's ordered and multinominal code scripts. You can download all the materials in the following ZIP file. Note: if you cannot download the ZIP file, please copy the link's address and paste it into your internet browser to access the ZIP file. The lab recording is available at this link. |

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