---
title: "Fitted-Model-Based Annotations :: Cheat Sheet"
subtitle: "'ggpmisc' `r packageVersion('ggpmisc')`"
author: "Pedro J. Aphalo"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    toc: yes
vignette: >
  %\VignetteIndexEntry{Fitted-Model-Based Annotations :: Cheat Sheet}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
---

## Basics

**ggpmisc** is based on the **grammar of graphics** implemented in **ggplot2**, the idea that you can build every graph from the same components: a **data** set, a **coordinate system**, and **geoms**---visual marks that represent data points. If you are not already familiar with this grammar and **ggplot2** you should visit the [**ggplot2** Cheat Sheet](https://rstudio.github.io/cheatsheets/html/data-visualization.html) first, and afterwards come back to this Cheat Sheet.

Differently to **ggplot2**, no geometries with the new stats as default are provided. The plot layers described here are always added with a _stat_, and when necessary, their default `geom` argument can be overridden. The default _geoms_ for the statistics described below are from packages **ggplot2** and **ggpp**.

```{r, eval=FALSE}
library(ggpmisc)
```

Most of the layer functions in **ggpmisc** aim at making it easier to add to plots information derived from model fitting, tests of significance and some summaries. All layer functions work as expected with groups and facets. 

## Correlation

* `stat_correlation()` computes parametric or non-parametric regression coefficients and optionally their confidence interval.

## Fitted lines

In general these layer functions have additional features than those in **ggplot2** or support different kinds of models. They add one or more curves and confidence band.

* `stat_poly_line()` fits a linear model.

* `stat_quant_line()` and `stat_quant_band()` fit a quantile regression model.

* `stat_ma_line()` fits a major axis regression.

* `stat_fit_augment()` fits any model supported by a `broom::augment()` method.

## Fitted equations and parameters

These layer functions have no equivalent in **ggplot2**. They flexibly construct labels to add a layer with one or more equations and/or estimates of parameters a fitted model. In the model is a true polynomial, the "label" can be formatted to be parsed into an R expression, or as $LaTeX$ or $markdown$ encoded character strings. The numeric values of the estimates are also returned and can be used to construct a label within a call to `aes()`.

* `stat_poly_eq()` is similar to `ggplot2::stat_smooth()` and fits a linear model.

* `stat_quant_eq()` is similar to `ggplot2::stat_quantile()` and fits a quantile regression model.

* `stat_ma_eq()` fits a major axis regression.

* `stat_fit_tidy()` fits any model supported by a `broom::tidy()` method.

## ANOVA or summary tables

* `stat_fit_tb()` fits any model supported by a `broom::tidy()` method. Adds an ANOVA or Summary table. Which columns are included and their naming can be set by the user.

## Multiple comparisons

* `stat_multcomp()` fits a model, computes ANOVA and subsequently calls **multcomp** to test the significance of Tukey, Dunnet or arbitrary sets of pairwise contrasts, with a choice of the adjustment method for the _P_-values. Significance of differences can be indicated with letters, asterisks or _P_-values. Sizes of differences are also computed and available for user-assembled labels. 

## Peaks and valleys

* `stat_peaks()` finds and labels peaks (= local maxima).

* `stat_valleys()` finds and labels valleys (= local minima).

## Volcano and quadrant plots

These plots are frequently used with gene expression data, and each of the many genes labelled based on the ternary outcome from a statistical test. Data are usually, in addition transformed. **ggpmisc** provides several variations on continuous, colour, fill and shape scales, with defaults set as needed. Scales support log fold-change (`logFC`), false discovery ratio (`FDR`), _P_-value (`Pvalue`) and binary or ternary test outcomes (`outcome`).

## Utility functions

Most of the functions used to generate formatted labels in layers and scales are also exported.

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Learn more at [docs.r4photobiology.info/ggpmisc/](https://docs.r4photobiology.info/ggpmisc/).

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