---
title: "432 Project A Portfolio Template"
subtitle: "Here's a place for a subtitle"
author: "Your Name (and Your Partner's Name) Here"
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
#| warning: false

knitr::opts_chunk$set(comment = NA) # do not remove this

library(janitor) # load other packages as desired

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

## Save The Tibble

# The Code Book

## Defining the Variables

## Numerical Description

# Linear Regression Plans

Follow the Project A instructions.

## My First Research Question

## My Quantitative Outcome

## My Planned Predictors (Linear Model)

# Logistic Regression Plans

Follow the Project A instructions.

## My Second Research Question

## My Binary Outcome

## My Planned Predictors (Logistic Model)

# 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

# par(mfrow = c(2,2)); plot(modelA); par(mfrow = c(1,1))
```

## 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.

# Session Information

```{r}
xfun::session_info()
```
