carlislerainey

# ---- setup ----

# nice printing
options(digits = 3)

# load packages
library(tidyverse)
library(posterior)
library(foreach)
library(doParallel)

carlislerainey

# ---- setup ----

# nice printing
options(digits = 3)

# load packages
library(tidyverse)
library(posterior)
library(foreach)
library(doParallel)

carlislerainey

# load packages
library(survival)

# load data
canc <- survival::cancer |>
  mutate(sex = case_when(sex == 1 ~ "Male",
                         sex == 2 ~ "Female"))
# quick look
glimpse(canc)

carlislerainey

# a function for rejection algorithm
rej <- function(f, S, M) {
  
  # record start time
  start_time <- Sys.time()
  
  # create containers and initialize counters
  samples <- numeric(S)  # container to store samples
  rejects <- NULL  # container to track rejected values; for teaching; slow!
  s <- 1 # currently trying to take sample 1

carlislerainey

# prior
alpha_star <- 3
beta_star <- 15

# data
N <- 150
k <- 8

# posterior
alpha_prime <- alpha_star + k

carlislerainey

# load packages
library(tidyverse)
library(marginaleffects)

# data 
devtools::install_github("jrnold/ZeligData")
turnout <- ZeligData::turnout

# fit model
f <- vote ~ age + educate + income + race

carlislerainey

# load package
library(tidyverse)
library(marginaleffects)
library(modelsummary)
library(glmmTMB)

# load data for santiago
sant <- crdata::holland2015 |> 
  filter(city == "santiago") |>
  glimpse()

carlislerainey

# load packages
library(tidyverse)
library(tinytable)

# data 
devtools::install_github("jrnold/ZeligData")
turnout <- ZeligData::turnout

# fit model
f <- vote ~ age + educate + income + race

carlislerainey

# ---- fit negative binomial model with optim() ----

# data; see ?crdata::holland2015
holland <- crdata::holland2015 |>
  filter(city == "santiago")

# formula corresponds to model 1 for each city in holland (2015) table 2
f <- operations ~ lower + vendors + budget + population

# ---- create a function to fit the model ----

carlislerainey

# ---- fit poisson model with optim() ----

# data; see ?crdata::holland2015
holland <- crdata::holland2015 |>
  filter(city == "santiago")

# formula corresponds to model 1 for each city in holland (2015) table 2
f <- operations ~ lower + vendors + budget + population