---
title: "Getting Started with earthUI"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting Started with earthUI}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Introduction

`earthUI` provides both an interactive Shiny GUI and a set of composable R
functions for building Earth (MARS-style) models using the `earth` package.

This vignette demonstrates the programmatic API. To launch the interactive
app, simply run:
```{r eval=FALSE}
library(earthUI)
launch()
```

## Basic Workflow

### 1. Import Data

```{r}
library(earthUI)

# For this example, we use the built-in mtcars dataset
df <- mtcars
head(df)
```

You can also import from files:
```{r eval=FALSE}
df <- import_data("my_data.csv")        # CSV
df <- import_data("my_data.xlsx")       # Excel
```

### 2. Detect Categorical Variables

```{r}
cats <- detect_categoricals(df)
cats
```

Variables with few unique values (default: 10 or fewer) are flagged as likely
categorical. Character and factor columns are always flagged.

### 3. Fit the Model

```{r}
result <- fit_earth(
  df = df,
  target = "mpg",
  predictors = c("cyl", "disp", "hp", "wt", "qsec", "am", "gear"),
  categoricals = c("am", "gear"),
  degree = 1
)
```

**Important defaults:**

- `degree = 1` — no interaction terms. This is intentional to avoid overfitting.
  Set `degree = 2` or higher only when you have domain knowledge supporting
  interactions.
- When `degree >= 2`, cross-validation (10-fold) is automatically enabled.

### 4. Examine Results

```{r}
# Model summary
s <- format_summary(result)
cat(sprintf("R²: %.4f\nGRSq: %.4f\nTerms: %d\n",
            s$r_squared, s$grsq, s$n_terms))
```

```{r}
# Coefficients
s$coefficients
```

```{r}
# Variable importance
format_variable_importance(result)
```

```{r}
# ANOVA decomposition
format_anova(result)
```

### 5. Plots

```{r fig.width=7, fig.height=4}
plot_variable_importance(result)
```

```{r fig.width=7, fig.height=4}
plot_partial_dependence(result, "wt")
```

```{r fig.width=7, fig.height=4}
plot_actual_vs_predicted(result)
```

```{r fig.width=7, fig.height=4}
plot_residuals(result)
```

## Controlling Interactions

When using `degree >= 2`, you can control which variable pairs are allowed
to interact:

```{r}
# Build default all-allowed matrix
preds <- c("wt", "hp", "cyl", "disp")
mat <- build_allowed_matrix(preds)

# Block wt-cyl interaction
mat["wt", "cyl"] <- FALSE
mat["cyl", "wt"] <- FALSE

# Convert to earth-compatible function
allowed_fn <- build_allowed_function(mat)

# Fit with interactions
result2 <- fit_earth(
  df = df,
  target = "mpg",
  predictors = preds,
  degree = 2,
  allowed_func = allowed_fn
)

s2 <- format_summary(result2)
cat(sprintf("Training R²: %.4f\nCV R²: %s\n",
            s2$r_squared,
            if (!is.na(s2$cv_rsq)) sprintf("%.4f", s2$cv_rsq) else "N/A"))
```

## Exporting Reports

Generate publication-quality reports in HTML, PDF, or Word:

```{r eval=FALSE}
render_report(result, output_format = "html", output_file = "my_report.html")
```

This requires the `quarto` R package and a Quarto installation.