---
title: "Regression Tables"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Regression Tables}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

# Regression Tables

Go from fitted models to publication-ready tables without hand-formatting
effect estimates. `gtregression` supports logistic, log-binomial, Poisson,
robust Poisson, negative binomial, and linear regression.

```{r reg-setup, message=FALSE, warning=FALSE}
library(gtregression)
library(dplyr)

data("data_birthwt", package = "gtregression")

birthwt_data <- data_birthwt |>
  mutate(
    race = factor(race, levels = c(1, 2, 3),
                  labels = c("White", "Black", "Other")),
    smoke = factor(smoke, levels = c(0, 1), labels = c("No", "Yes")),
    ht = factor(ht, levels = c(0, 1), labels = c("No", "Yes")),
    ui = factor(ui, levels = c(0, 1), labels = c("No", "Yes")),
    low = factor(low, levels = c(0, 1), labels = c("Normal BW", "Low BW")),
    ptl_cat = factor(ifelse(ptl > 0, "Yes", "No"), levels = c("No", "Yes")),
    ftv_cat = factor(case_when(
      ftv == 0 ~ "None",
      ftv == 1 ~ "One",
      ftv >= 2 ~ "Two or more"
    ), levels = c("None", "One", "Two or more"))
  )

birthwt_exposures <- c(
  "age", "lwt", "race", "smoke", "ht", "ui", "ptl_cat", "ftv_cat"
)
```

## Univariable Models

`uni_reg()` fits one model per exposure and returns a table ready for reports.

```{r uni-logit, message=FALSE, warning=FALSE}
birthwt_uni <- uni_reg(
  data = birthwt_data,
  outcome = "low",
  exposures = birthwt_exposures,
  approach = "logit",
  theme = clinical
)

birthwt_uni$table
```

## Adjusted Models

`multi_reg()` can fit all exposures in one model, or fit one adjusted model per
exposure using the same adjustment set.

```{r multi-adjusted, message=FALSE, warning=FALSE}
birthwt_multi <- multi_reg(
  data = birthwt_data,
  outcome = "low",
  exposures = c("smoke", "ht", "ui", "ptl_cat", "ftv_cat"),
  adjust_for = c("age", "lwt", "race"),
  approach = "logit",
  theme = striped
)

birthwt_multi$table
```

The adjustment variables are recorded in the table footnote so the result is
ready for manuscript-style reporting.

## Other Effect Measures

Switch the `approach` to change the estimand.

```{r reg-risk-ratio, message=FALSE, warning=FALSE}
uni_reg(
  data = birthwt_data,
  outcome = "low",
  exposures = c("smoke", "ht", "ui", "ptl_cat"),
  approach = "logbinomial"
)$table
```

## Continuous Outcomes

Linear regression outputs beta coefficients and keeps diagnostics under
`$reg_check`.

```{r reg-linear, message=FALSE, warning=FALSE}
birthwt_linear <- multi_reg(
  data = birthwt_data,
  outcome = "bwt",
  exposures = c("age", "lwt", "race", "smoke", "ht", "ui"),
  approach = "linear"
)

birthwt_linear$table
```

## What To Inspect

- `$table`: publication-ready table.
- `$table_body`: numeric estimates behind the display.
- `$models`: fitted model objects.
- `$model_summaries`: model-level summaries.
- `$reg_check`: diagnostics for linear models.