Publications by Amir Freund
Ordinal Regression
This article aims to demonstrate how to perform Ordinal regression using the MASS package - POLR function 0.1 Initialization Required Libraries library(MASS) # for polr library(stargazer) library(sjPlot) library(ggplot2) library(ggthemes) library(dplyr) library(tidyr) library(viridis) library(gt) library(extrafont) library(effect...
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Regression Diagnostics
1 Required libraries library(performance) library(gt) library(dplyr) library(afcommon) 2 Introduction This document demonstrates the usage of two regression diagnostic functions: af_vif_test(): Tests for multicollinearity using Variance Inflation Factors (VIF) af_hetero_test(): Tests for heteroscedasticity using the Breusch-Pagan test We�...
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Stargazer Tables
1 Required Libraries library(stargazer) library(afcommon) 2 Prepare Dataset We’ll use the mtcars dataset and create some categorical variables to demonstrate the functionality. # Load and prepare the data data <- mtcars # Create categorical variables data$size_cat <- cut(data$wt, breaks = c(-Inf, 2.5, 3.5, Inf), ...
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RMarkdown Header
--- title: 'RMarkdown Header' subtitle: 'Template for RMarkdown files.' author: 'Amir Freund' date: '`r Sys.Date()`' output: html_document: code_folding: hide # show / hide toc: true # table of content true toc_depth: 3 # upto three depths of headings (specified by #, ## and ###) toc_float: # Allows the TOC to float on ...
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Coefficient Visualization
1 Required libraries library(ggplot2) library(gt) library(afcommon) 2 Data Preparation We’ll use the mtcars dataset to demonstrate the coefficient visualization functions. # Prepare data data(mtcars) mtcars$am <- as.factor(mtcars$am) # Create multiple models predicting mpg m1 <- lm(mpg ~ wt, data = mtcars) m2 <- lm(mpg ~ wt + am, data =...
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Descriptive Statistics
1 Required libraries and source files library(tidyverse) library(explore) library(gt) library(viridis) library(afcommon) 2 Creating Sample Dataset We’ll create a dataset with the following variables: - salary: normally distributed numeric variable - sales: right-skewed numeric variable - gender: binary variable - student_status: binary v...
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Interactive (Plotly) Analysis
Required Libraries library(dplyr) library(plotly) library(htmlwidgets) library(htmltools) library(DT) Process the mtcars dataset mtcars_processed <- mtcars %>% mutate( car_name = rownames(.), # Convert binary variables to factors vs = factor(vs, levels = c(0, 1), labels = c("V-shape", "Straight")), am = factor(am, levels...
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Descriptive Statistics
1 Required libraries and source files library(tidyverse) library(explore) library(gt) library(viridis) source("../Common/af_descriptive.R") 2 Creating Sample Dataset We’ll create a dataset with the following variables: - salary: normally distributed numeric variable - sales: right-skewed numeric variable - gender: binary variable - stud...
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RMarkdown Header
--- title: 'RMarkdown Header' author: 'Amir Freund' date: '`r Sys.Date()`' output: html_document: code_folding: hide # show / hide toc: true # table of content true toc_depth: 3 # upto three depths of headings (specified by #, ## and ###) toc_float: # Allows the TOC to float on the page. collapsed: false # Ensures...
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Multilevel Regression Analysis
1 Required libraries and source files library(ggplot2) library(dplyr) library(gt) library(lme4) library(performance) library(sjPlot) 2 Introduction This document demonstrates multilevel regression analysis using a simulated dataset. We’ll create nested data representing students within schools, fit several multilevel models, calculate IC...
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