--- title: "Project A Template for 432 in Spring 2026" subtitle: "Insert Your Subtitle Here (No More than 120 characters)" author: "Your Name and Your Partner's Name" date: last-modified format: html: toc: true number-sections: true code-fold: show code-tools: true code-overflow: wrap embed-resources: true date-format: iso theme: zephyr ## change the theme if you prefer --- ## R Packages and Setup {.unnumbered} ```{r} #| message: false knitr::opts_chunk$set(comment = NA) # do not remove this library(janitor) library(naniar) # load other packages as desired library(easystats) library(tidyverse) # load tidyverse last theme_set(theme_bw()) # set theme for ggplots ``` # Data Source Follow the Project A instructions. # The Subjects Follow the Project A instructions. # Loading and Tidying the Data ## Loading the Raw Data Follow the Project A instructions. ## Cleaning the Data Follow the Project A instructions, using the subsections below that are necessary, and eliminating the others. ### Selecting Variables We'll Use ### Changing Variable Names ### Sampling the Data ### Converting Variable Types ### Working with Categorical Predictors ### Arranging the Tibble # The Tidy Tibble Follow the Project A instructions. ## Listing the Tibble ## Size and Identifiers ## Saving as an Rds file # The Code Book ## Five Key Statements Follow the Project A instructions. See the [Project A Demonstration](https://thomaselove.github.io/432-2026/432_projectA_demo.html) for an example. ## Describing the Variables Variables included in the `nh_demo` data are summarized and described in five different ways in the panel below. Click on the tab that interests you to see results. :::{.callout-tip} Please provide, at minimum, the variable descriptions list and the data_codebook() results here. You can include the others if you like. ::: ::: {.panel-tabset} ### Definitions Provide definitions of your variables in a table here. See the [Project A Demonstration](https://thomaselove.github.io/432-2026/432_projectA_demo.html) for a more detailed example. ### `data_codebook()` Provide `data_codebook()` results for your tibble here. ```{r} data_codebook(nh_demo) ``` ### Other Tabs (optional) Add other tabs here if you like with things like `describe()`, `tbl_summary()` or Missingness information. See the [Project A Demonstration](https://thomaselove.github.io/432-2026/432_projectA_demo.html) for a detailed example. ::: # Linear Regression Design Follow the Project A instructions. ## My First Research Question ## My Quantitative Outcome ### Numerical Summary of my linear outcome ```{r} #| eval: false # note that you'll need to remove this line completely Hmisc::describe(yourdatasetname$youroutcomenamegoeshere) mosaic::favstats(yourdatasetname$youroutcomenamegoeshere) yourdatasetname |> tabyl(youroutcomenamegoeshere) |> adorn_pct_formatting() |> arrange(desc(n)) |> head(5) ``` ### Summary Statements about my outcome, insertyouroutcomenamehere My outcome is insertnamehere. - I have XXX observations and no missing values on insertnamehere. - I have XXX distinct values of insertnamehere. - The range is XX to XX, with mean XX and standard deviation XX insertunitshere. - The most common value for insertnamehere is XX, which occurs in XX% of my subjects. ## Linear Model Predictor Candidates Follow the Project A instructions. ### Anticipated Direction of Effects ### Missingness Summary ```{r} #| eval: false # you'll have to drop this line linear_model_predictors <- insertdetailshere miss_var_summary(linear_model_predictors) |> filter(n_miss > 0) miss_case_table(linear_model_predictors) ``` - I have complete data for all of the linear model predictors in XX (XX%) of the XX rows in my data. - I am missing XX values (XX%) for insertvariablenamehere. # Logistic Regression Design Follow the Project A instructions. ## My Second Research Question ## My Binary Outcome ## Logistic Model Predictor Candidates ### Anticipated Direction of Effects ### Missingness Summary ```{r} #| eval: false # you'll have to drop this line logistic_model_predictors <- insertdetailshere miss_var_summary(logistic_model_predictors) |> filter(n_miss > 0) miss_case_table(logistic_model_predictors) ``` - I have complete data for all of the logistic model predictors in XX (XX%) of the XX rows in my data. - I am missing XX values (XX%) for insertvariablenamehere. # Linear Regression Analyses Follow the Project A instructions. ## Missingness ## Outcome Transformation ## Scatterplot Matrix and Collinearity ## Model A ### Fitting Model A ### Tidied Coefficient Estimates (Model A) ### Summarizing Fit (Model A) ### Regression Diagnostics (Model A) ```{r} #| fig-height: 8 # check_model(modelA) ``` ## Non-Linearity ## Model B ### Fitting Model B ### Tidied Coefficient Estimates (Model B) ### Effects Plot for Model B ### Summarizing Fit (Model B) ### Regression Diagnostics (Model B) ## Validating Models A and B ### Validated $R^2$ statistics Model | Validated $R^2$ -----: | -------- A | B | ## Final Linear Regression Model I prefer Model `XX`, because ... ### Winning Model's Parameter Estimates ### Effects Plot for Winning Model ### Numerical Description of Effect Sizes ### Nomogram of Winning Model ### Prediction for a New Subject # Logistic Regression Analyses Follow the Project A instructions. ## Missingness ## Model Y ### Fitting Model Y ### Tidied Odds Ratio Estimates (Model Y) ### Effects Plot (Model Y) ### Summarizing Fit (Model Y) ### Confusion Matrix (Model Y) My prediction rule is ... ## Non-Linearity ## Model Z ### Fitting Model Z ### Tidied Odds Ratio Estimates (Model Z) ### Effects Plot (Model Z) ### Summarizing Fit (Model Z) ### Confusion Matrix (Model Z) As in Model Y, my prediction rule is ... ## Validating Models Y and Z ### Validated $R^2$ and $C$ statistics for each model ## Final Logistic Regression Model I prefer Model `XX`, because ... ### Winning Model's Parameter Estimates ### Plot of Effect Sizes for Winning Model ### Numerical Description of Effect Sizes ### Plot of ROC Curve for Winning Model ### Validated $R^2$ and $C$ statistic for Winning Model ### Nomogram of Winning Model ### Predictions for Two New Subjects # Discusssion Follow the Project A instructions. ## Answering My Research Questions ## Thoughts on Project A # Affirmation Follow the Project A instructions. The text we want to see here is > I am certain that it is completely appropriate for these data to be shared with anyone, without any conditions. There are no concerns about privacy or security. # References 1. Data Source description 2. Others, as needed. # Use of AI Follow the Project A instructions. # Session Information ```{r} xfun::session_info() ```