---
title: "SDM4 in R: Regression Wisdom (Chapter 8)"
author: "Nicholas Horton (nhorton@amherst.edu)"
date: "June 13, 2018"
output:
pdf_document:
fig_height: 3
fig_width: 6
html_document:
fig_height: 3
fig_width: 5
word_document:
fig_height: 4
fig_width: 6
---
```{r, include = FALSE}
# Don't delete this chunk if you are using the mosaic package
# This loads the mosaic and dplyr packages
require(mosaic)
```
```{r, include = FALSE}
# knitr settings to control how R chunks work.
require(knitr)
opts_chunk$set(
tidy = FALSE, # display code as typed
size = "small" # slightly smaller font for code
)
```
## Introduction and background
This document is intended to help describe how to undertake analyses introduced
as examples in the Fourth Edition of *Stats: Data and Models* (2014) by De Veaux, Velleman, and Bock.
More information about the book can be found at http://wps.aw.com/aw_deveaux_stats_series. This
file as well as the associated R Markdown reproducible analysis source file used to create it can be found at http://nhorton.people.amherst.edu/sdm4.
This work leverages initiatives undertaken by Project MOSAIC (http://www.mosaic-web.org), an NSF-funded effort to improve the teaching of statistics, calculus, science and computing in the undergraduate curriculum. In particular, we utilize the `mosaic` package, which was written to simplify the use of R for introductory statistics courses. A short summary of the R needed to teach introductory statistics can be found in the mosaic package vignettes (http://cran.r-project.org/web/packages/mosaic).
A paper describing the mosaic approach was published in the *R Journal*: https://journal.r-project.org/archive/2017/RJ-2017-024.
## Chapter 8: Regression Wisdom
### Section 8.1: Examining residuals
Figure 8.1 (page 220) displays the scatterplot of heart rate vs duration for the Penguins dataset (along with a superimposed regression line and a smoother).
```{r message = FALSE}
library(mosaic)
library(readr)
options(digits = 3)
Penguins <- read_csv("http://nhorton.people.amherst.edu/sdm4/data/Penguins.csv")
gf_point(DiveHeartRate ~ Duration, ylab = "Dive Heart Rate (bpm)",
xlab = "Duration (mins)", data = Penguins) %>%
gf_lm() %>%
gf_smooth(se = FALSE)
```
Figure 8.2 (page 220) displays the residuals from a linear regression model as a function of duration.
```{r message=FALSE}
Penguinmod <- lm(DiveHeartRate ~ Duration, data = Penguins)
gf_point(resid(Penguinmod) ~ Duration, data = Penguins) %>%
gf_lm() %>%
gf_smooth(se = FALSE)
```
Figure 8.3 (page 221) displays the histogram of residuals for the cereal data from Chapter 7.
```{r message=FALSE}
Cereals <- read_csv("http://nhorton.people.amherst.edu/sdm4/data/Cereals.csv")
Cerealmod <- lm(calories ~ sugars, data = Cereals)
gf_histogram(~ resid(Cerealmod), binwidth = 7.5)
```
Figure 8.4 (page 221) displays a scatterplot of the residuals vs predicted values. Without jittering, the display has an odd pattern.
```{r}
gf_point(resid(Cerealmod) ~ fitted(Cerealmod))
```
By adding some random noise we can more easily observe values that are shared by more than one cereal.
```{r}
gf_point(jitter(resid(Cerealmod)) ~ jitter(fitted(Cerealmod)))
```
Figure 8.5 (page 222) displays the scatterplot stratified by what shelf it is displayed on at the store.
```{r}
tally(~ shelf, data = Cereals)
Cereals <- mutate(Cereals, shelfgrp =
recode(shelf,
`1` = "bottomshelf",
`2` = "middleshelf",
`3` = "topshelf")
)
tally(~ shelfgrp, data = Cereals)
gf_point(calories ~ sugars, color = ~ shelfgrp,
lwd = 2, data = Cereals) %>%
gf_lm()
```
### Section 8.2: Extrapolation and reaching beyond the data
### Section 8.3: Outliers, leverage, and influence
### Section 8.4: Lurking variables and causation
### Section 8.5: Working with summary values