carlislerainey

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carlislerainey

# load packages
library(tidyverse)
library(brms)
library(marginaleffects)

# load fake data that should resemble final data set (these are "big" effects--effects might be smaller than this)
toy <- read_csv("https://gist.githubusercontent.com/carlislerainey/ba2c556f322135fe439ed9639975c922/raw/bc0ab6fecc23762d8da736eecb888bd1e4cbc92a/toy.csv") %>%
  glimpse()

carlislerainey

carlislerainey

library(tidyverse)
library(DeclareDesign)

# Original parameters
tau <- 1  # treatment effect
se <- tau / (qnorm(0.95) + qnorm(0.80))  # standard error for 80% power
n_planned <- 500  # sample size per condition in planned full study
# calculate required standard deviation to yield 80% power given the above
sigma <- se * sqrt(2 * n_planned / 2)

carlislerainey

# load packages
library(modelsummary)

# get latest version of {crdata} package, if updated
remotes::install_github("carlislerainey/crdata")

# load (and slightly wrangle) data from Barrilleaux and Rainey (2014)
br <- crdata::br2014 %>%
  # recode from centered GOP gov. indicator to 0/1 Dem. gov. indicator
  # (explanatory variables are centered about mean)

carlislerainey

# load packages
library(tidyverse)

# update {crdata} if necessary
remotes::install_github("carlislerainey/crdata")

# load data
weis <- crdata::weisiger2014

# rescale weisiger2014 explanatory variables using arm::rescale()

carlislerainey

# load packages
library(tidyverse)

# create data frame of the potential outcomes
po <- tribble(
  ~Name,        ~Y1, ~Y0,
  "Alex Smith",     7,   5,
  "Jamie Doe",      2,   3,
  "Pat Johnson",    7,   7,

carlislerainey

# load packages
library(tidyverse)

# create data frame of the potential outcomes
po <- tribble(
  ~Name,        ~Y1, ~Y0,
  "Alex Smith",     7,   5,
  "Jamie Doe",      2,   3,
  "Pat Johnson",    7,   7,

carlislerainey

# load packages
library(tidyverse)

# create data frame of the potential outcomes
po <- tribble(
  ~Name,        ~Y1, ~Y0,
  "Alex Smith",     7,   5,
  "Jamie Doe",      2,   3,
  "Pat Johnson",    7,   7,

carlislerainey

# load packages
library(rstan)
library(bayesplot)

# make a stan model
fancy_stan_model <- "
generated quantities {
    int<lower=0, upper=1> y_sim = bernoulli_rng(0.5); 
}
"