---
title: "Analyze and Visualize Important QOIs"
output: rmarkdown::html_vignette
description: >
  How to estimate and correct marginal means (MMs) or average marginal component effects (AMCEs), including predicting IRR if necessary. Visualize these and other QOIs of interest.
vignette: >
  %\VignetteIndexEntry{Analyze and Visualize Important QOIs}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
library(projoint)
library(dplyr)
library(ggplot2)
library(patchwork)
```

## 🎯 Estimate Corrected MMs or AMCEs

In conjoint analysis, default MMs and AMCEs can be **biased** due to measurement error from intra-respondent variability.

**projoint** corrects for this bias automatically.

The following instructions apply to choice-level data. What if you have profile-level data?<sup><a href="faq.html#what-if-i-have-profile-level-data" target="_blank" style="text-decoration: none;">&#9432;</a></sup> Our <a href="faq.html" target="_blank">FAQ Page</a> has instructions to estimate and visualize profile-level QOIs.

---

## 📦 Prepare Example Data

> **Outcome naming & order (important)**
> 
> - List `.outcomes` in the **order questions were asked**.  
> - If you have a repeated task, its outcome must be the **last element**.  
> - For base tasks (all but last), the function reads the **digits** in each name as the task id (e.g., `"choice4"`, `"Q4"`, `"task04"` → task 4).  
> - The **repeated base task** is inferred from the **first base outcome’s digits**. The repeated outcome itself **need not** contain digits—only its position (last) matters.  
> - Outcome strings must end with verified choice labels. Prefer an explicit
>   map such as `.choice_map = c("A" = 1, "B" = 2)`. Verify this mapping from
>   the survey instrument or QSF file; it cannot be inferred from the CSV.
> - Invalid labels, trailing whitespace, and missing choices stop with an
>   informative error. Use `.allow_missing_choices = TRUE` only after review.

**Examples**

```{r}
# Standard order; repeated = task 1
data("exampleData1")
outcomes <- c(paste0("choice", 1:8), "choice1_repeated_flipped")
out1 <- reshape_projoint(exampleData1, outcomes)
```

---

## 🛠️ Why Use IDs (e.g., `att1`, `level1`)?

Before estimating quantities, it’s important to understand how attribute and level IDs work inside projoint.

<details>
We recommend working with **attribute IDs** rather than actual text labels because:

- Safer against special characters, languages, or typos
- Allows **multiple attributes** to have identical labels (e.g., "High" for both "Teaching Quality" and "Research Quality")

Check attribute-level mappings:

```{r}
out1$labels
```

You can also save these labels for easier editing:

```r
save_labels(out1, "labels.csv")
```
</details>
---

## 📈 Estimate Marginal Means (MMs)

<details><summary><b>Choice-Level MMs (Specific Level)</b></summary>

Suppose you want to estimate, within a given profile pair, the probability of choosing a profile that includes "40% of pre-tax income" (`level3`) for Housing Cost (`att1`) rather than one that includes "15% of pre-tax income" (`level1`) for the same attribute, averaging over all combinations of the other attributes and across respondents; then use the following code:

```{r}
qoi <- set_qoi(
  .structure = "choice_level",
  .att_choose = "att1",
  .lev_choose = "level3",
  .att_notchoose = "att1",
  .lev_notchoose = "level1"
)

mm2 <- projoint(out1, qoi)
print(mm2)
summary(mm2)
```

</details>
---

## 📉 Estimate AMCEs

<details><summary><b>Choice-Level AMCEs (Specific Level)</b></summary>

Suppose you want to quantify how the choice probability **changes** between the following profile pairs:

- choosing a profile that includes "40% of pre-tax income" (`level3`) for Housing Cost (`att1`) versus one that includes "15% of pre-tax income" (`level1`) for Housing Cost (`att1`); **and**
- [baseline] choosing a profile that includes "30% of pre-tax income" (`level2`) for Housing Cost (`att1`) versus one that includes "15% of pre-tax income" (`level1`) for Housing Cost (`att1`); 

averaging over all combinations of the other attributes and across respondents. Then write the following code:

```{r}
qoi <- set_qoi(
  .structure = "choice_level",
  .estimand = "amce",
  .att_choose = "att1",
  .lev_choose = "level3",
  .att_notchoose = "att1",
  .lev_notchoose = "level1",
  .att_choose_b = "att1",
  .lev_choose_b = "level2",
  .att_notchoose_b = "att1",
  .lev_notchoose_b = "level1"
)

amce2 <- projoint(out1, qoi)
print(amce2)
summary(amce2)
```

</details>
---

## 🔎 Predict Intra-Respondent Reliability (IRR)

If your design **does not include** a repeated task, you can **predict** IRR using predict_tau(), based on observed respondent behavior.

<details><summary><b>Predict IRR Using `predict_tau()`</b></summary>

```{r}
data(out1_arranged)
predicted_irr <- predict_tau(out1_arranged)

print(predicted_irr)
summary(predicted_irr)
plot(predicted_irr)
```

</details>
---

```{r fig-setup, include=FALSE}
# Global default settings for all figures
knitr::opts_chunk$set(
  fig.width = 6,
  fig.height = 3,
  fig.align = "center",
  dpi = 300  # Optional: high-resolution plots
)

# Helper functions for special figure sizes
narrow_fig <- function() list(fig.width = 5, fig.height = 4)
wide_fig <- function() list(fig.width = 8, fig.height = 5)
tall_fig <- function() list(fig.width = 6, fig.height = 7)

```

```{r, echo=FALSE}
library(projoint)
library(ggplot2)

data(out1_arranged, package = "projoint")
```

## 🎨 Visualize MMs or AMCEs

The **projoint** package provides ready-to-publish plotting tools for conjoint analysis results.

<span style="color:DarkRed"><b>Note:</b></span> The current version of **projoint** supports plotting choice-level MMs only.  
Support for **choice-level AMCEs** will be available in future updates!

---

### ⚖️ Choice-Level Analysis

<details>
<summary><b>Estimate</b></summary>

- Specify your quantity of interest:
```{r}
qoi_mm <- set_qoi(
  .structure = "choice_level", # default
  .att_choose = "att1", 
  .lev_choose = "level1", 
  .att_notchoose = "att1", 
  .lev_notchoose = "level3"
)
```

- Estimate
```{r}
choice_mm <- projoint(
  .data = out1_arranged, 
  .qoi = qoi_mm, 
  .ignore_position = TRUE
)
```
</details>

<details>
<summary><b>Visualize (Levels) </b></summary>

```{r, fig.width = 6, fig.height = 3}
plot(choice_mm)
```
</details>

<details>
<summary><b>Visualize (Differences)</b></summary>

```{r, fig.width = 8, fig.height = 2}
plot(choice_mm, .type = "pointrange")
```
</details>

---

🏠 **Home:** [Home](https://yhoriuchi.github.io/projoint/index.html)
