Unidentifiable parameters in multiple sharing per-contact infectivity model
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As a Core Curriculum Teaching Fellow, my project will develop a new course called “Statistics for Research” focusing on innovation, undergraduate research, and writing across the curriculum as an alternative to the traditional Introduction to Statistics, Stat 145. Intro Stats is one of the largest courses on campus with 1000-1200 students per semester in over 20 sections. This Statistics for Research course will cover the traditional material, but in a modern evidence-based way by integrating real data in the context of case studies, fostering active learning, and using technology to explore concepts and analyze large real datasets and communicate the results. This is a computer- and a project-based course in the style of Dierker’s Passion-Driven Statistics and modeled directly after the already-successful UNM Stat 427/527 Advanced Data Analysis 1 and 2 where I’ve piloted these learning strategies for advanced undergraduates and graduate students for four semesters. Unlike my previous six-section intervention study showing active learning increases success by a third of a letter grade for everyone, this will be a pilot course developed for one or two sections. This implementation is just one of the many evidence-based recommendations I made as a 2016-17 Teaching Fellow for improving statistics education at UNM, including offering multiple versions (Stat Literacy with a first-year learning community, Stats for Research, Advanced Math-Stats); train/mentor our TAs prior to their teaching a course; offer a new Undergraduate Stat Ed Practicum course for all undergrad stat majors to serve as peer learning facilitators in at least one intro stat course; use multidisciplinary project-based learning to improve the outcomes of statistical interest, attitude, and confidence for under-represented minority students; and make other small changes such as moving the final from the first day of finals at 7:30 AM to a later time when students are shown to have greater success. A long-term goal is to hire a professor of practice in statistics for continued leadership and research in statistical education at UNM.
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rmarkdown::render(fn)
. This has the advantage of using the console environment instead of the RMarkdown environment, so all the objects are available for manipulation in the console. However, it is hard to debug when there are scores of unnamed chunks. Now, I can easily name all the code chunk and can quickly identify where issues are.
Install from github: https://github.com/erikerhardt/RmdNameChunk
devtools::install_github("erikerhardt/RmdNameChunk")Read an Rmd file, update existing prefixed code chunks, and renumber.
library(RmdNameChunk) rmd_name_chunks( fn_in = "test_in.Rmd" , fn_out = "test_out.Rmd" , prefix_chunk_name = "chunk-" )Review the input and output files to see how the chunk header names have been updated. Below is an example of the two Rmd files in the vignette. test_in.Rmd was read in and test_out.Rmd was created. The chunk headers are shown below from each file.
test_in.Rmd 8 : ```{r setup, include=FALSE} 22 : ```{r cars} 28 : ```{r} 32 : ```{ r } 36 : ```{r, echo=FALSE} 40 : ```{ r, eval=FALSE} 44 : ```{r , eval=FALSE} 48 : ```{r chunk-2, eval=FALSE} 52 : ```{r chunk-XXX , eval=FALSE} 56 : ```{r chunk-XXX2 , eval=FALSE} 60 : ```{r chunk-XXX3 , eval=FALSE} 64 : ```{r chunk-XXX4 , eval=FALSE} 68 : ```{r chunk-XXX5 , eval=FALSE} 72 : ```{r chunk-XXX6 , eval=FALSE} 81 : ```{r pressure, echo=FALSE}These code chunk headers were changed to those below:
test_out.Rmd 8 : ```{r setup, include=FALSE} 22 : ```{r cars} 28 : ```{r chunk-01} 32 : ```{r chunk-02} 36 : ```{r chunk-03, echo=FALSE} 40 : ```{r chunk-04, eval=FALSE} 44 : ```{r chunk-05, eval=FALSE} 48 : ```{r chunk-06, eval=FALSE} 52 : ```{r chunk-07, eval=FALSE} 56 : ```{r chunk-08, eval=FALSE} 60 : ```{r chunk-09, eval=FALSE} 64 : ```{r chunk-10, eval=FALSE} 68 : ```{r chunk-11, eval=FALSE} 72 : ```{r chunk-12, eval=FALSE} 81 : ```{r pressure, echo=FALSE}
Body: Chapter 5 was also helpful for structuring the remaining slides in a hierarchy of key points (sections) with explanations (subsections) and details (slides). Key Point 1 Analysis 1 Fact 1 Fact 2 Fact 3 Analysis 2 Fact 1 Fact 2 Fact 3 Analysis 3 Fact 1 Fact 2 Fact 3 Key Point 2 Analysis 1 Fact 1 Fact 2 Fact 3 Analysis 2 Fact 1 Fact 2 Fact 3 Analysis 3 Fact 1 Fact 2 Fact 3 Key Point 3 Analysis 1 Fact 1 Fact 2 Fact 3 Analysis 2 Fact 1 Fact 2 Fact 3 Analysis 3 Fact 1 Fact 2 Fact 3 Conclusion (recap): The Point A Headline (What challenges do I face in this setting?) The Point B Headline (Where do I want to be?) (The Gap Between A and B) (Why am I here?) The Call to Action Headline (How do I get from A to B?)