noamross

### Title: Back to basics: High quality plots using base R graphics
###	An interactive tutorial for the Davis R Users Group meeting on April 24, 2015
###
### Date created: 20150418
### Last updated: 20150423
###
### Author: Michael Koontz
### Email: mikoontz@gmail.com
###	Twitter: @michaeljkoontz
###

hrbrmstr

library(ggplot2)
library(dplyr)
library(tidyr)
library(stringr)
library(scales)
library(gridExtra)
library(grid)

# use the NPR story data file ---------------------------------------------
# and be kind to NPR's bandwidth budget

gtallen1187

"A little bit of slope makes up for a lot of y-intercept"

01/13/2012. From a lecture by Professor John Ousterhout at Stanford, class CS140

Here's today's thought for the weekend. A little bit of slope makes up for a lot of Y-intercept.

[Laughter]

cmrnp

library(emmeans)
data(mtcars)

# in the example here, all models give the same point estimates and similar
# SEs because there is only a single, categorical, variable in the model.
# if you add a continuous predictor, they will no longer, because the relationship
# assumed by the model will be different for the three models!

m <- glm(am ~ vs, data = mtcars, family = binomial)
em1 <- emmeans(m, "vs")

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models