Regression Class - Graduate Methods - 11/30/2022

regression_grad_methods_2022.R

# install.packages("jtools")
# install.packages("interactions")
# install.packages("ggstance")
# install.packages("broom.mixed")

library(jtools)
library(interactions)
library(ggstance)
library(broom.mixed)
library(socsci)

df <- read_csv("https://raw.githubusercontent.com/ryanburge/pls2003_sp17/master/living.csv")

reg <- lm(weight ~ height, data = df)

summ(reg)

reg <- lm(weight ~ height + white, data = df)

summ(reg)

60*4.56

273.6 (-9.6 * 0)

264 - 133.50 = 130.5

plot_coefs(reg)

reg <- lm(weight ~ height + white + black, data = df)
summ(reg)
plot_coefs(reg)

reg <- lm(weight ~ height + white + black + male + physical + educ, data = df)
summ(reg)
plot_coefs(reg)

int <- lm(weight ~ height*white*stamps, data = df)

interact_plot(int, pred = height, modx = white, mod2 = stamps, interval = TRUE)

df <- df %>% 
  mutate(white_new = frcode(white == 1 ~ "White",
                            white == 0 ~ "Non-White")) %>% 
  mutate(stamps_new = frcode(stamps == 1 ~ "On Food Stamps",
                             stamps == 0 ~ "Not on Food Stamps")) %>% 
  mutate(coll = frcode(educ >= 16 ~ "College Grad",
                       educ <= 15 ~ "No College"))


int <- lm(weight ~ height*white_new*coll, data = df)

interact_plot(int, pred = height, modx = white_new, mod2 = coll, interval = TRUE)