RStudio
script editor pane: RStudio
command console (usually lower left pane)
require(Hmisc)
to load the getRs
function
getRs('xxx.qmd', put='rstudio')
where the script file name xxx
is replaced by the name given in the Examples column below
Date | Room | Material to read/watch prior to class |
Session Topics | Assignments | Examples | |
---|---|---|---|---|---|---|
Thurs | 1 | Zoom | Course Webpage Syllabus Key Concepts Glossary A17.1-17.7, A17.10-1 B10-B10.5 H2.4.7 |
Simple Linear & Nonparametric Regression | Getting Started | |
2 | Zoom | A18, B10.6 B9.4-9.9 |
Multiple Linear Regression R rms Package |
HW 1 | abd17-lion : lion age from nose |
|
Mon | 5 | Zoom | B10.7-10.10 | " | HW 2 | |
6 | Zoom | B13-13.6.1, 13.13 | Analysis of Covariance in Randomized Studies | HW 3 | abd18-molerats : mole rats |
|
7 | Zoom | Hxiii, H1, H2-2.3.1, classification blog | Regression Modeling Strategies: Introduction | Exercise: ID the model Final project dataset in hand |
abd18-molerats : chunk tests cat-pred : categorical predictors, taking control of contrasts |
|
8 | Zoom | H2.3.2-2.4.1-2.4.6 demos | Methods for Multivariable Models | HW 4 | splinex : polynomial and spline fitting |
|
9 | Zoom | H2.4.8-2.7.2 (omit 2.5.1), 2.8 | " | HW 5 | support : nonlinear relationships |
|
Mon | 12 | Zoom | H3, H4-4.1.2 | Missing Data, Multivariable Modeling Strategies | HW 6 HW 7 (optional) |
support : missing data nhgh : basic diagnostics |
13 | Zoom | H4.3-4.7.2, 4.7.5-4.7.7 | Variable Selection, Overfitting, Shrinkage, Collinearity, Data Reduction | HW 8 | nhgh : data reduction support : variable clustering |
|
14 | Zoom | H4.9-5.1.1, this | Influential Observations, Comparing Models, Improving Practice, Strategies, Describing the Fitted Model | nhgh |
||
15 | Zoom | H5.1.2, 5.1.3, H5.2-5.4, (omit 5.5), B10.10 | Performance Indexes, Relative Explained Variation, Bootstrap, Validation, Bootstrapping Ranks | HW 9 |
framingham : outliersframingham : internal validation |
|
16 | Zoom | H5.6, B6.8, 6.9, 6.10-6.10.2, A17.9 | Relative Effect Measures, Introduction to LRM | Prep for critique | Simple Logistic acath : binary logistic 2 |
|
Mon | 19 | Zoom | Student led discussion of papers | HW 10 | Final project statistical analysis plan finished | |
20 | Zoom | A17.9, H10-10.5, 10.8-10.10, NNT | Final project analysis file finished Binary Logistic Models, NNT |
|||
21 | Zoom | B6.10.3, B13.6.2-13.7 (brief), blog, blog, H11 | " + Case Study | support : ordinal predictors |
||
22 | Zoom | H12, HTE, HTE, B19, More Analyses of Diagnostic Yield, Simple Decision Analysis |
Binary Logistic Model Case Study, Risk-based diagnostic research, ROC curves, HTE | HW 11 | Bayes and this | |
23 | Zoom | B4.1.2, B5.12.4-.5, Power, B7.6-7.7, H13.1-13.3, 13.4.3, 13.4.5, 13.4.7, H14.1 and Table 14.1, 14.2, H15-15.2, 15.5.2, Figure 15.15, 15.16 | Proportional Odds Ordinal Logistic Models | HW 12 (Optional) HW 13 |
simhiv : ordinal regression |
|
Mon | 26 | Zoom | Review and catch-up | |||
27 | Zoom | H17.1-17.3, 17.5.1, H18.1, 18.3-18.3.6, H20-20.1.5, 20.3, 20.7, H21 |
Survival Analysis Cox Model |
|||
28 | Zoom | B15, Advantages, Efficiency, H22 | Analysis of Serial Measurements Ordinal Longitudinal Data |
|||
29 | Zoom | Student Presentations of Final Project | Final Project |