## S3 method for class 'lm'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

library(ggplot2)
library(dplyr)

mod <- lm(mpg ~ wt + qsec, data = mtcars)

tidy(mod)
glance(mod)

# coefficient plot
d <- tidy(mod, conf.int = TRUE)

ggplot(d, aes(estimate, term, xmin = conf.low, xmax = conf.high, height = 0)) +
  geom_point() +
  geom_vline(xintercept = 0, lty = 4) +
  geom_errorbarh()
  
# Aside: There are tidy() and glance() methods for lm.summary objects too. 
# This can be useful when you want to conserve memory by converting large lm 
# objects into their leaner summary.lm equivalents.
s <- summary(mod)
tidy(s, conf.int = TRUE)
glance(s)

augment(mod)
augment(mod, mtcars, interval = "confidence")

# predict on new data
newdata <- mtcars %>%
  head(6) %>%
  mutate(wt = wt + 1)
augment(mod, newdata = newdata)

# ggplot2 example where we also construct 95% prediction interval
mod2 <- lm(mpg ~ wt, data = mtcars) ## simpler bivariate model since we're plotting in 2D

au <- augment(mod2, newdata = newdata, interval = "prediction")

ggplot(au, aes(wt, mpg)) + 
  geom_point() +
  geom_line(aes(y = .fitted)) + 
  geom_ribbon(aes(ymin = .lower, ymax = .upper), col = NA, alpha = 0.3)

# predict on new data without outcome variable. Output does not include .resid
newdata <- newdata %>%
  select(-mpg)
augment(mod, newdata = newdata)

au <- augment(mod, data = mtcars)

ggplot(au, aes(.hat, .std.resid)) +
  geom_vline(size = 2, colour = "white", xintercept = 0) +
  geom_hline(size = 2, colour = "white", yintercept = 0) +
  geom_point() +
  geom_smooth(se = FALSE)

plot(mod, which = 6)
ggplot(au, aes(.hat, .cooksd)) +
  geom_vline(xintercept = 0, colour = NA) +
  geom_abline(slope = seq(0, 3, by = 0.5), colour = "white") +
  geom_smooth(se = FALSE) +
  geom_point()

# column-wise models
a <- matrix(rnorm(20), nrow = 10)
b <- a + rnorm(length(a))
result <- lm(b ~ a)
tidy(result)