jackobailey

bayes_dydx.default_mn <- function(model, data = NULL, variable, stepsize = 1e-7, re_formula = NULL){
  
  # Check that everything is running properly and that the
  # user has provided all of the relevant information.
  
  if(is.null(model) == T){
    
    stop("Please provide a model to the function using the 'model =' argument (e.g. model = m1)")
    
  } else if(is.null(variable) == T){

jackobailey

bayes_margin.factor_mn <- function(model, variable = NULL, data = NULL, draws = NULL, n = NULL, re_formula = NA){
  
  # Check that everything is running properly and that the
  # user has provided all of the relevant information.
  
  if(is.null(model) == T){
    
    stop("Please provide a model to the function using the 'model =' argument (e.g. model = m1)")
    
  } else if(is.null(variable) == T){

jackobailey

bayes_dydx.factor_mn <- function(model, variable = NULL, data = NULL, draws = NULL, n = NULL, re_formula = NA){
  
  # Check that everything is running properly and that the
  # user has provided all of the relevant information.
  
  if(is.null(model) == T){
    
    stop("Please provide a model to the function using the 'model =' argument (e.g. model = m1)")
    

jackobailey

# Load packages using the 'librarian' package
librarian::shelf(tidyverse,
                 tidybayes,
                 brms,
                 here)
                 
# Create function to simulate the outcome scale for arbitrary
# prior distributions
rlikert <- function(n = 1e3,
                    t1 = -0.84,

jackobailey

# Load packages
library(brms)
library(tidybayes)
library(tidyverse)
library(magrittr)
library(reshape2)
library(margins)

# Function 1: Compute numerical derivatives for continuous variables ----------
bayes_dydx.default <- function(model, data = NULL, variable, stepsize = 1e-7, re_formula = NULL){

jackobailey

# Install and load packages
librarian::shelf(brms, tidybayes, tidyverse, magrittr, margins)

# Create "posterior_ame" function
posterior_ame <- function(model, variable, eps = 1e-7){
  
  # Get model outcome
  resp <- model$formula$resp
  
  # Omit outcome variable from data

jackobailey

# Load packages
library(tidyverse)
library(brms)
library(broom)

# We need to simulate data from an ordinal distribution. This is hard.
# Unlike many distributions, treatment effects can affect the mean *and*
# the sd of the latent y* distribution that underlies the ordinal distribution
# (see Liddell & Kruschke (2018) and Bürkner & Vuorre (2019))

jackobailey

<?xml version="1.0" encoding="UTF-8"?>
<style xmlns="http://purl.org/net/xbiblio/csl" class="in-text" version="1.0" demote-non-dropping-particle="sort-only">
   <info>
      <title>Manchester Business School Harvard Reference format 1.1 (Author-Date)</title>
      <id>http://www.zotero.org/styles/mbsharvard1.1</id>
      <link href="http://www.zotero.org/styles/mbsharvard1.1" rel="self"/>
      <author>
         <name>James Haddow</name>
         <email>jbh78@me.com</email>
      </author>