---
title: "ezcox: Easily Show Cox Forestplot in One Command"
author: "Shixiang Wang \\
        SYSUCC"
date: "`r Sys.Date()`"
output:
  prettydoc::html_pretty:
    toc: true
    theme: cayman
    highlight: github
  pdf_document:
    toc: true
vignette: >
  %\VignetteIndexEntry{ezcox: Easily Show Cox Forestplot in One Command}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

```{r setup}
library(survival)
library(ezcox)
```

## forester

For simple and general forest data, you can use `forester()`, it is lightweight
and can be applied to any proper data (not limited to Cox model).

```{r fig.width=4, fig.height=3}
t1 <- ezcox(lung, covariates = c(
  "age", "sex",
  "ph.karno", "pat.karno"
))
p <- forester(t1, xlim = c(0, 1.5))
p
p2 <- forester(t1, xlim = c(0.5, 1.5))
p2
```

## show_forest

For more powerful plot features, you need to use `show_forest()`.
Unlike the `forester()`, the `ezcox()` is included in the function.

```{r, fig.width=7, fig.height=5}
show_forest(lung, covariates = c("sex", "ph.ecog"), controls = "age")
show_forest(lung, covariates = c("sex", "ph.ecog"), controls = "age", merge_models = TRUE)
show_forest(lung,
  covariates = c("sex", "ph.ecog"), controls = "age", merge_models = TRUE,
  drop_controls = TRUE
)
show_forest(lung,
  covariates = c("sex", "ph.ecog"), controls = "age", merge_models = TRUE,
  vars_to_show = "sex"
)
```