# Fitted-Model-Based Annotations :: Cheat Sheet

### ‘ggpmisc’ 1.0.0

#### Pedro J. Aphalo

#### 2026-07-05

## Basics

ggpmisc follows the grammar of graphics implemented in ggplot2, based on the idea that many different data visualizations can be built by combining the same components: a data set, a coordinate system, and geoms—visual marks that represent data or summaries derived from data. These elements are complemented by stats that compute data summaries to be passed to geoms and scales that describe the mapping of data into graphical elements.

There are multiple variations of each element of the grammar, providing a vocabulary. Thus, the grammar allows us to ‘speak/write’ a graph from composable elements, instead of being limited to a predefined set of charts. ‘ggpmisc’ adds new stats and scales, expanding the vocabulary while remaining consistent with the grammar. ‘ggpmisc’ relies on geoms from packages ‘ggpmisc’ and ‘ggplot2’ for its defaults, while also compatible with geoms from other R packages including ‘ggtext’, ‘marquee’, ‘xdvir’, ‘ggrepel’ and ‘gganimate’.

If you are not already familiar with the grammar of graphics and ggplot2 you should visit the ggplot2 Cheat Sheet first, and afterwards come back to this Cheat Sheet.

Differently to ggplot2, no matching geometries with the new stats as their default are provided. The plot layers described here are always added with a stat, and when necessary, their default `geom` argument overridden.

    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 or statistical summaries. All the stats from ‘ggpmisc’ do computations by data group except for `stat_fit_tb()` and `stat_multcomp()` that do computations by plot panel.

The statistics that return predicted values for regressions return `x` and `y` where one of the variables is a sequence of numbers for the explanatory variables and the other contains the predictions based on them; depending on the `orientation` or `formula`, `ymin` and `ymax`, or `xmin` and `xmax`, give the lower and upper confidence limits for the fitted line or curve.

The statistics returning fitted or residual values return these values as variables `y.fitted` or `x.fitted`, `y.resid` or `x.resid`, `weights` and `posterior.weights`. Variables `x` and `y` contain the observed values. When present, `weights` are the prior weights, and `posterior.weights` are posterior weights, those actually used by the model fit function, possibly computed by it.

The statistics that return text labels for annotating plots, return in `x` and `y` as coordinates of the text or label annotaions the values passed as arguments to parameters `label.x` and `label.y`, or values computed based on them. The character strings are returned as variables with names ending in `.label`. These variables can be used in mappings created with `aes()`, `use_label()` and `f_use_labels()`. The difference is that `use_label()` and `f_use_label()` accept short names for the labels, recognize them as computed by a stat and combine them into a single character string. For example, `use_label("eq", "R2", "n", sep = ", ")` is equivalent to `aes(label = paste(after_stat("eq.label", "rr.label", "n.label), sep = ", ")`, saving some typing. Numeric values for the parameter estimates are also returned, making possible 1) to assemble labels in user code within a call to `aes()` and 2) to map outcomes to additional aesthetics such as `fontface` or `colour` based on a threshold.

## Correlation

- `stat_correlation()` computes parametric \(r\) or non-parametric correlation coefficients, \(\tau\) and \(\rho\), and optionally their confidence intervals, \(P\), and \(n\), the number of observations, flexibly adding an annotation to the plot.

## Fitted models

The statistics for fitted models come in matched pairs, one that adds a plot layer with one or more curves and confidence band(s), and one that annotates the plot with the fitted model equation and/or other parameter estimates. These depend on the type of fitted model and include \(R^2\), \(F\), \(P\), \(AIC\), \(BIC\), \(n\), and in many cases also the fitted-model equation. The curve plotting stats fulfil a role similar to `ggplot2::stat_smooth()` while the statistics for textual annotations have no equivalent in ‘ggplot2’.

- `stat_poly_line()` and `stat_poly_eq()` support a broad set of model fit functions: e.g., linear models (OLS, resistant and robust), general linear model (gls), linear splines, cubic splines, additive models (gam), major axis (MA) and standardised major axis (SMA) regression, etc. The fitted model equation is automatically generated for regular polynomials, and can be assembled in user code for other model formulas.

- `stat_quant_line()`, `stat_quant_band()` and `stat_quant_eq()` support quantile regression based on both polynomials and smoothing splines (using ‘quantreg’). The fitted model equation is automatically generated for regular polynomials, and can be assembled in user code for other model formulas. Prior and posterior weights are returned.

- `stat_ma_line()` and `stat_ma_eq()` support major axis (MA), standardised major axis (SMA) and ranged major axis (RMA) regression (using ‘lmodel2’). The fitted model equation is automatically generated for regular polynomials, and can be assembled in user code for other model formulas. Prior and posterior weights are returned.

- `stat_distrmix_line()`, `stat_distrmix_area()` and `stat_distrmix_eq()` support fitting of univariate Normal-distribution mixture models or a of a single Normal distribution. The fitted model equation is automatically. The areas delimited by quantiles of the fitted distribution are tagged.

- `stat_fit_fitted()` and `stat_fit_deviations()` can be used to plot the fitted values and to display them as segments between predicted and observed values (deviations), respectively. Prior and posterior weights are returned.

- `stat_fit_residuals()` can be used to create consistent plots of residuals for a wide range of model fit functions. Prior and posterior weights are returned.

- `stat_fit_augment()` (`broom::glance()`, similar to R’s `fitted()` plus `residuals()` and possibly plus `weights()` and/or `predict()`) works with model fit functions supported by `broom::augment()` methods including non-linear models. Provides an alternative to `stat_poly_line()` for an even broader range of model fit functions. It only returns numeric values.

- `stat_fit_tidy()` (`broom::tidy()`, similar to R’s `summary()`) works with model fit functions supported by `broom::tidy()` methods including non-linear models. Provides numeric values from which equation labels can be created for an even broader range of model fit functions than those supported by `stat_poly_eq()`. `broom::tidy()` is similar to R’s `summary()` for fitted models. It only returns numeric values.

- `stat_fit_glance()` (`broom::glance()`, similar to R’s `anova()`) works with model fit functions supported by `broom::glance()` methods including non-linear models. Provides an alternative to `stat_poly_eq()` for an even broader range of model fit functions. It only returns numeric values.

## ANOVA or summary tables

- `stat_fit_tb()` fits any model supported by a `broom::tidy()` method. Adds an ANOVA or Summary table as a plot inset. Which columns and rows are included and their naming can be set by the user. The formatting of the table can be changed in part with aesthetic mappings and with table themes.

## Multiple comparisons

- `stat_multcomp()` fits a model, computes an ANOVA or equivalent and subsequently calls functions from package ‘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.

## Aesthetic mappings

- Functions `use_label()` and `f_use_label()` combine together labels generated by the stats as formatted `character` strings and map the combined `character` string to the `label` aesthetic.

## Peaks and valleys

Formatted character labels are returned both for `x` and `y` coordinates. Both `numeric`, time and dates can be mapped to `x` (or `y` with `orientation = "y"`).

- `stat_peaks()` finds and labels peaks (= global or local maxima).

- `stat_valleys()` finds and labels valleys (= global or local minima).

- `stat_spikes()` finds and labels very narrow and prominent peaks and valleys (local disturbances).

## 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`) on multiple logarithm bases both for input and for output, false discovery ratio (`FDR`), P-value (`Pvalue`) and binary or ternary test outcomes (`outcome`).

- Discrete manual scales: `scale_colour_outcome()`, `scale_fill_outcome()`, `scale_shape_outcome()`.

- Continuous scales: `scale_x_logFC()`, `scale_y_logFC()`, `scale_colour_logFC()`, `scale_fill_logFC()`.

- Continuous scales: `scale_x_Pvalue()`, `scale_y_Pvalue()`, `scale_x_FDR()`, `scale_y_FDR()`.

## Utility functions

Most of the functions used to generate formatted labels in the statistics and scales listed above are also exported. However, several of them now have equivalents in recent versions of R package ‘scales’.

Learn more at docs.r4photobiology.info/ggpmisc/.