Andrew Heiss andrewheiss

library(tidyverse)
library(lme4)
library(marginaleffects)

model <- lmer(weight ~ Time + I(Time^2) + Diet*Time + (1 | Chick), data = ChickWeight)

# This is a shortcut for plotting predictions automatically
plot_predictions(model, condition = "Time")

library(MASS)
library(tidyverse)
library(marginaleffects)
library(palmerpenguins)

# Make a categorical weight column
penguins <- penguins |> 
  drop_na(sex) |> 
  mutate(weight_cat = cut(

library(tidyverse)
library(marginaleffects)
library(gapminder)

gapminder_2007 <- gapminder |> 
  filter(year == 2007)

# Use log() in the model formula
model <- lm(lifeExp ~ log(gdpPercap), data = gapminder_2007)

library(tidyverse)
library(mlogit)
library(dfidx)
library(marginaleffects)

chocolate <- read_csv("https://www.andrewheiss.com/blog/2023/08/12/conjoint-multilevel-multinomial-guide/data/choco_candy.csv") %>% 
  mutate(
    dark = case_match(dark, 0 ~ "Milk", 1 ~ "Dark"),
    dark = factor(dark, levels = c("Milk", "Dark")),

library(tidyverse)
library(palmerpenguins)

penguins <- penguins |> drop_na()

# This splits the dataset into three smaller datasets behind the scenes, but
# then doesn't do anything with them. But secretly it's waiting to do things
# within the three groups (hence the "Groups: species [3]") note
penguins |>