saudiwin

gistr

saudiwin

gistr

saudiwin

#' Helper function to create arrays
#' 
#' Function takes a data.frame in long mode and converts it to an array. Function can also repeat a 
#' single matrix to fill out an array.
#' 
#' @param input_matrix Either a data.frame in long mode or a single matrix
#' @param arr_dim If \code{input_matrix} is a single matrix, \code{arr_dim} determines the length of the resulting array
#' @param row_var Unquoted variable name that identifies the data.frame column corresponding to the rows (1st dimension) of the array (must be unique)
#' @param col_var_name Unquoted variable name that identifies the data.frame column corresponding names of the columns (2nd dimension) of the array
#' @param col_var_value Unquoted variable name that identifies the data.frame column corresponding to the values that populate the cells of the array

saudiwin

# Ordinal versus binary outcome power calculation
# Robert Kubinec
# New York University Abu Dhabi
# January 31, 2022

library(dplyr)
library(ordinal)
library(ggplot2)

saudiwin

# test glmmTMB's MLE implementation of the ordered beta regression model
# and compare it to the ordbetareg package, which uses
# Bayesian estimation (Stan/brms)

library(glmmTMB)
library(ordbetareg)
library(dplyr)

data("pew")

saudiwin

# calculate Pr(Y=1) and Pr(Y=0) (i.e. the discrete categories of the scale)
# for different groups in the data
# useful for treatment/control comparisons

library(ordbetareg)
library(dplyr)
library(tidyr)
library(posterior)
library(tidybayes)
library(ggplot2)

saudiwin

library(DeclareDesign)
library(ordbetareg)
library(fabricatr)
library(future)
library(marginaleffects)
library(broom)
library(glmmTMB)

set.seed(20241125)

saudiwin

# ChatGPT 4o created this distribution with the following prompt:
# "can you define a new statistical distribution that no one 
# has ever used before and 
# write out the stan code for the likelihood"

# it required one additional prompt to ask it to provide
# a function for initial starting values after Stan
# failed to initialize the MCMC chain

# it did so successfully and no alterations were made to the code below

saudiwin

library(dplyr)

# define code as emojis
# simulate data from Normal distribution and fit OLS regression

`🙄` <- lm

`🤩` <- tibble

`🤪` <- mutate

saudiwin

# generate google drive files for class

library(googledrive)
library(readr)
library(readxl)
library(tidyverse)
library(stringr)

# need class roster/blackboard export