Package ‘**ggpmisc**’ (Miscellaneous Extensions to
‘ggplot2’) is a set of extensions to R package ‘ggplot2’ (>= 3.0.0)
with emphasis on annotations and plotting related to fitted models.
Estimates from model fit objects can be displayed in ggplots as text,
tables or equations. Predicted values, residuals, deviations and weights
can be plotted for various model fit functions. Linear models, quantile
regression and major axis regression as well as those functions with
accessors following the syntax of package ‘broom’ are supported. Package
‘**ggpmisc**’ continues to give access to extensions moved
as of version 0.4.0 to package ‘**ggpp**’.

Package ‘**ggpmisc**’ is consistent with the grammar of
graphics, and opens new possibilities retaining the flexibility inherent
to this grammar. Its aim is not to automate plotting or annotations in a
way suitable for fast data exploration by use of a “fits-all-sizes”
predefined design. Package ‘**ggpmisc**’ together with
package ‘**ggpp**’, provide new layer functions, position
functions and scales. In fact, these packages follow the tenets of the
grammar even more strictly than ‘**ggplot2**’ in the
distinction between geometries and statistics. The new statistics in
‘**ggpmisc**’ focus mainly on model fitting, but there is
not yet support for multiple comparisons among groups. The default
annotations are those most broadly valid and of easiest interpretation.
We follow R’s approach of expecting that users know what they need or
want, and will usually want to adjust how results from model fits are
presented both graphically and textually. The approach and mechanics of
plot construction and rendering remain unchanged from those implemented
in package ‘**ggplot2**’.

Statistics that help with reporting the results of model fits are:

Statistic | Returned values (default geometry) | Met |
hods |
---|---|---|

`stat_poly_eq()` |
equation, R^{2}, P, etc.
(`text_npc` ) |
lm, rlm (1, 2, 7) |

`stat_ma_eq()` |
equation, R^{2}, P, etc.
(`text_npc` ) |
lmodel2 (6, 7) |

`stat_quant_eq()` |
equation, P, etc. (`text_npc` ) |
rq (1, 3, 4, 7) |

`stat_correlation()` |
correlation, P-value, CI (`text_npc` ) |
Pearson (t), Kendall (z), Spearman
(S) |

`stat_poly_line()` |
line + conf. (`smooth` ) |
lm, rlm (1, 2, 7) |

`stat_ma_line()` |
line + conf. (`smooth` ) |
lmodel2 (6, 7) |

`stat_quant_line()` |
line + conf. (`smooth` ) |
rq, rqss (1, 3, 4, 7) |

`stat_quant_band()` |
median + quartiles (`smooth` ) |
rq, rqss (1, 4, 5, 7) |

`stat_fit_residuals()` |
residuals (`point` ) |
lm, rlm, rq (1, 2, 4, 7, 8) |

`stat_fit_deviations()` |
deviations from observations (`segment` ) |
lm, rlm, lqs, rq (1, 2, 4, 7, 9) |

`stat_fit_glance()` |
equation, R^{2}, P, etc.
(`text_npc` ) |
all those supported by ‘broom’ |

`stat_fit_augment()` |
predicted and other values (`smooth` ) |
all those supported by ‘broom’ |

`stat_fit_tidy()` |
fit results, e.g., for equation (`text_npc` ) |
all those supported by ‘broom’ |

`stat_fit_tb()` |
ANOVA and summary tables (`table_npc` ) |
all those supported by ‘broom’ |

Notes: (1) *weight* aesthetic supported; (2) user defined fit
functions that return an object of a class derived from `lm`

are supported even if they override the statistic’s *formula*
argument; (3) unlimited quantiles supported; (4) user defined fit
functions that return an object of a class derived from `rq`

or `rqs`

are supported even if they override the statistic’s
*formula* and/or *quantiles* argument; (5) two and three
quantiles supported; (6) user defined fit functions that return an
object of a class derived from `lmodel2`

are supported; (7)
`method`

arguments support colon based notation; (8) various
functions if method `residuals()`

defined for returned value;
(9) various functions if method `fitted()`

defined for
returned value.

Statistics `stat_peaks()`

and `stat_valleys()`

can be used to highlight and/or label maxima and minima in a plot.

Scales `scale_x_logFC()`

and `scale_y_logFC()`

are suitable for plotting of log fold change data. Scales
`scale_x_Pvalue()`

, `scale_y_Pvalue()`

,
`scale_x_FDR()`

and `scale_y_FDR()`

are suitable
for plotting *p*-values and adjusted *p*-values or false
discovery rate (FDR). Default arguments are suitable for volcano and
quadrant plots as used for transcriptomics, metabolomics and similar
data.

Scales `scale_colour_outcome()`

,
`scale_fill_outcome()`

and `scale_shape_outcome()`

and functions `outome2factor()`

,
`threshold2factor()`

, `xy_outcomes2factor()`

and
`xy_thresholds2factor()`

used together make it easy to map
ternary numeric outputs and logical binary outcomes to color, fill and
shape aesthetics. Default arguments are suitable for volcano, quadrant
and other plots as used for genomics, metabolomics and similar data.

Several geoms and other extensions formerly included in package
‘ggpmisc’ until version 0.3.9 were migrated to package ‘ggpp’. They are
still available when ‘ggpmisc’ is loaded, but the documentation now
resides in the new package ‘**ggpp**’.

Functions for the manipulation of layers in ggplot objects, together
with statistics and geometries useful for debugging extensions to
package ‘ggplot2’, included in package ‘ggpmisc’ until version 0.2.17
are now in package ‘**gginnards**’.

```
library(ggpmisc)
library(ggrepel)
library(broom)
```

In the first example we plot a time series using the specialized
version of `ggplot()`

that converts the time series into a
tibble and maps the `x`

and `y`

aesthetics
automatically. We also highlight and label the peaks using
`stat_peaks`

.

```
ggplot(lynx, as.numeric = FALSE) + geom_line() +
stat_peaks(colour = "red") +
stat_peaks(geom = "text", colour = "red", angle = 66,
hjust = -0.1, x.label.fmt = "%Y") +
stat_peaks(geom = "rug", colour = "red", sides = "b") +
expand_limits(y = 8000)
```

In the second example we add the equation for a fitted polynomial
plus the adjusted coefficient of determination to a plot showing the
observations plus the fitted curve, deviations and confidence band. We
use `stat_poly_eq()`

together with `use_label()`

to assemble and map the desired annotations.

```
<- y ~ x + I(x^2)
formula ggplot(cars, aes(speed, dist)) +
geom_point() +
stat_fit_deviations(formula = formula, colour = "red") +
stat_poly_line(formula = formula) +
stat_poly_eq(use_label(c("eq", "adj.R2")), formula = formula)
```

The same figure as in the second example but this time annotated with
the ANOVA table for the model fit. We use `stat_fit_tb()`

which can be used to add ANOVA or summary tables.

```
<- y ~ x + I(x^2)
formula ggplot(cars, aes(speed, dist)) +
geom_point() +
geom_smooth(method = "lm", formula = formula) +
stat_fit_tb(method = "lm",
method.args = list(formula = formula),
tb.type = "fit.anova",
tb.vars = c(Effect = "term",
"df",
"M.S." = "meansq",
"italic(F)" = "statistic",
"italic(P)" = "p.value"),
tb.params = c(x = 1, "x^2" = 2),
label.y.npc = "top", label.x.npc = "left",
size = 2.5,
parse = TRUE)
#> Dropping params/terms (rows) from table!
```

The same figure as in the second example but this time using quantile regression, median in this example.

```
<- y ~ x + I(x^2)
formula ggplot(cars, aes(speed, dist)) +
geom_point() +
stat_quant_line(formula = formula, quantiles = 0.5) +
stat_quant_eq(formula = formula, quantiles = 0.5)
```

Band highlighting the region between both quartile regressions and a line for the median regression.

```
<- y ~ x + I(x^2)
formula ggplot(cars, aes(speed, dist)) +
geom_point() +
stat_quant_band(formula = formula)
```

A quadrant plot with counts and labels, using
`geom_text_repel()`

from package ‘ggrepel’.

```
ggplot(quadrant_example.df, aes(logFC.x, logFC.y)) +
geom_point(alpha = 0.3) +
geom_quadrant_lines() +
stat_quadrant_counts() +
stat_dens2d_filter(color = "red", keep.fraction = 0.02) +
stat_dens2d_labels(aes(label = gene), keep.fraction = 0.02,
geom = "text_repel", size = 2, colour = "red") +
scale_x_logFC(name = "Transcript abundance after A%unit") +
scale_y_logFC(name = "Transcript abundance after B%unit")
```

Installation of the most recent stable version from CRAN:

`install.packages("ggpmisc")`

Installation of the current unstable version from GitHub:

```
# install.packages("devtools")
::install_github("aphalo/ggpmisc") devtools
```

HTML documentation for the package, including help pages and the
*User Guide*, is available at https://docs.r4photobiology.info/ggpmisc/.

News about updates are regularly posted at https://www.r4photobiology.info/.

Chapter 7 in Aphalo (2020) explains basic concepts of the grammar of graphics as implemented in ‘ggplot2’ as well as extensions to this grammar including several of those made available by packages ‘ggpp’ and ‘ggpmisc’. Open access supplementary chapters and other information related to the book is available at https://www.learnr-book.info/.

Please report bugs and request new features at https://github.com/aphalo/ggpmisc/issues. Pull requests are welcome at https://github.com/aphalo/ggpmisc.

If you use this package to produce scientific or commercial publications, please cite according to:

```
citation("ggpmisc")
#>
#> To cite package 'ggpmisc' in publications use:
#>
#> Aphalo P (2022). _ggpmisc: Miscellaneous Extensions to 'ggplot2'_.
#> https://docs.r4photobiology.info/ggpmisc/,
#> https://github.com/aphalo/ggpmisc.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {ggpmisc: Miscellaneous Extensions to 'ggplot2'},
#> author = {Pedro J. Aphalo},
#> year = {2022},
#> note = {https://docs.r4photobiology.info/ggpmisc/,
#> https://github.com/aphalo/ggpmisc},
#> }
```

Aphalo, Pedro J. (2020) *Learn R: As a Language.* The R
Series. Boca Raton and London: Chapman and Hall/CRC Press. ISBN:
978-0-367-18253-3. 350 pp.

© 2016-2022 Pedro J. Aphalo (pedro.aphalo@helsinki.fi). Released under the GPL, version 2 or greater. This software carries no warranty of any kind.