---
title: "Objects, summaries and plotting"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Objects, summaries and plotting}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
set.seed(20260607)
```

`GLBFP` objects are ordinary S3 objects.

| Object type | Class | Main helpers |
|---|---|---|
| Pointwise estimator | `"glbfp_fit"` | `print()`, `summary()`, `predict()` |
| Grid estimator | `"glbfp_grid"` | `print()`, `summary()`, `predict()`, `plot()`, `as.data.frame()` |
| Leave-one-out scores | `"glbfp_di"` | `print()`, `summary()`, `plot()`, `as.data.frame()` |

```{r}
library(GLBFP)

x <- matrix(rnorm(200), ncol = 1)
b <- compute_bi_optim(x, m = 1)

point_fit <- glbfp(0, x, b = b, m = 1)
class(point_fit)
summary(point_fit)
predict(point_fit)
```

Grid fits support `summary()`, `predict()`, `plot()` and `as.data.frame()`.

```{r}
grid_fit <- glbfp_estimate(x, b = b, m = 1, grid_size = 60)

class(grid_fit)
summary(grid_fit)

grid_df <- as.data.frame(grid_fit)
head(grid_df)
```

Prediction from a grid fit uses nearest-grid lookup. This is intended as a
lightweight helper for exploratory workflows.

```{r}
new_points <- matrix(c(-1, 0, 1), ncol = 1)
predict(grid_fit, newdata = new_points)
```

Plot methods return `ggplot` objects for one-dimensional grids and for
two-dimensional contour plots.

```{r}
plot(grid_fit)
```

For two-dimensional grids, `contour = TRUE` gives a static `ggplot` object.
The default two-dimensional display uses `plotly`.

```{r}
x2 <- cbind(rnorm(120), rnorm(120))
grid_2d <- glbfp_estimate(x2, b = c(0.8, 0.8), m = c(1, 1), grid_size = 12)

plot(grid_2d, contour = TRUE)
```

For leave-one-out score objects, use the dedicated vignette "Leave-one-out D_i
diagnostics" for interpretation.