2023 Feedback on STATS 2020

[dylanbeaudette]

Ideas / commentary after working with mentees and reviewing lecture material.

• many namespace collisions → cut down on the number of packages used / loaded at any given time, this is esp. a problem with library(tidyverse) approach to loading everything
• aqp::allocate() can be very noise, add verbose argument
• include examples / interpretation of plot(Predict(model.rms)) and plot(summary(model.rms))
• explain / link to additional information on odds ratio, interpret all examples in the book
• label probability axes on all figures
• re-think / simplify glm examples: predictor variables too complex / hard to interpret
• more explanation of rms::validate()
• CA790 regression examples need more context / explanation
• num. tax. examples: explain type = 'n' when making plots
• link to / integrate evaluation of ordination, new exercise / examples
• tree methods: more expressive use of corrplot() → colors, shading, ordering, etc.
• ordered factor syntax / interpretation / importance
• convert everything to terra
• categorical data modeling, EDA, etc. → link to Michael Friendly's work