`stat_fit_augment`

fits a model and returns a "tidy"
version of the model's data with prediction added, using '`augmnent()`

methods from packages 'broom', 'broom.mixed', or other sources. The
prediction can be added to the plot as a curve.

## Arguments

- mapping
The aesthetic mapping, usually constructed with

`aes`

. Only needs to be set at the layer level if you are overriding the plot defaults.- data
A layer specific dataset - only needed if you want to override the plot defaults.

- geom
The geometric object to use display the data

- method
character or function.

- method.args, augment.args
list of arguments to pass to

`method`

and to to`broom::augment`

.- n.min
integer Minimum number of distinct values in the explanatory variable (on the rhs of formula) for fitting to the attempted.

- level
numeric Level of confidence interval to use (0.95 by default)

- y.out
character (or numeric) index to column to return as

`y`

.- position
The position adjustment to use for overlapping points on this layer

- na.rm
logical indicating whether NA values should be stripped before the computation proceeds.

- show.legend
logical. Should this layer be included in the legends?

`NA`

, the default, includes if any aesthetics are mapped.`FALSE`

never includes, and`TRUE`

always includes.- inherit.aes
If

`FALSE`

, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g.`borders`

.- ...
other arguments passed on to

`layer`

. This can include aesthetics whose values you want to set, not map. See`layer`

for more details.

## Details

`stat_fit_augment`

together with `stat_fit_glance`

and `stat_fit_tidy`

, based on package 'broom' can be used
with a broad range of model fitting functions as supported at any given
time by 'broom'. In contrast to `stat_poly_eq`

which can
generate text or expression labels automatically, for these functions the
mapping of aesthetic `label`

needs to be explicitly supplied in the
call, and labels built on the fly.

A ggplot statistic receives as data a data frame that is not the one passed
as argument by the user, but instead a data frame with the variables mapped
to aesthetics. In other words, it respects the grammar of graphics and
consequently within arguments passed through `method.args`

names of
aesthetics like $x$ and $y$ should be used instead of the original variable
names, while data is automatically passed the data frame. This helps ensure
that the model is fitted to the same data as plotted in other layers.

## Note

The statistic `stat_fit_augment`

can be used only with
`methods`

that accept formulas under any formal parameter name and a
`data`

argument. Use `ggplot2::stat_smooth()`

instead of
`stat_fit_augment`

in production code if the additional features are
not needed.

Although arguments passed to parameter `augment.args`

will be
passed to [generics::augment()] whether they are silently ignored or obeyed
depends on each specialization of [augment()], so do carefully read the
documentation for the version of [augment()] corresponding to the `method`
used to fit the model.

## Warning!

Not all `glance()` methods are defined in package 'broom'. `glance()` specializations for mixed models fits of classes `lme`, `nlme`, `lme4`, and many others are defined in package 'broom.mixed'.

## Handling of grouping

`stat_fit_augment`

applies the function
given by `method`

separately to each group of observations; in ggplot2
factors mapped to aesthetics generate a separate group for each level.
Because of this, `stat_fit_augment`

is not useful for annotating plots
with results from `t.test()`

or ANOVA or ANCOVA. In such cases use
instead `stat_fit_tb()`

which applies the model fitting per panel.

## Computed variables

The output of `augment()`

is
returned as is, except for `y`

which is set based on `y.out`

and
`y.observed`

which preserves the `y`

returned by the
`generics::augment`

methods. This renaming is needed so that the geom
works as expected.

To explore the values returned by this statistic, which vary depending
on the model fitting function and model formula we suggest the use of
`geom_debug`

. An example is shown below.

## See also

`broom`

and `broom.mixed`

for details on how
the tidying of the result of model fits is done.

Other ggplot statistics for model fits:
`stat_fit_deviations()`

,
`stat_fit_glance()`

,
`stat_fit_residuals()`

,
`stat_fit_tb()`

,
`stat_fit_tidy()`

## Examples

```
# Package 'broom' needs to be installed to run these examples.
# We check availability before running them to avoid errors.
broom.installed <- requireNamespace("broom", quietly = TRUE)
gginnards.installed <- requireNamespace("gginnards", quietly = TRUE)
if (broom.installed) {
library(broom)
library(quantreg)
}
#> Loading required package: SparseM
# Inspecting the returned data using geom_debug()
if (gginnards.installed) {
library(gginnards)
}
# Regression by panel, inspecting data
if (broom.installed & gginnards.installed) {
ggplot(mtcars, aes(x = disp, y = mpg)) +
geom_point(aes(colour = factor(cyl))) +
stat_fit_augment(method = "lm",
method.args = list(formula = y ~ x),
geom = "debug",
summary.fun = colnames)
}
#> Warning: Ignoring unknown parameters: `summary.fun`
#> [1] "PANEL 1; group(s) -1; 'draw_function()' input 'data' (head):"
#> ymax ymin y x .fitted .resid .hat .sigma .cooksd
#> 1 NA NA 23.00544 160 23.00544 -2.005436 0.04175345 3.285085 0.008649080
#> 2 NA NA 23.00544 160 23.00544 -2.005436 0.04175345 3.285085 0.008649080
#> 3 NA NA 25.14862 108 25.14862 -2.348622 0.06287776 3.276208 0.018678646
#> 4 NA NA 18.96635 258 18.96635 2.433646 0.03281262 3.274958 0.009825376
#> 5 NA NA 14.76241 360 14.76241 3.937588 0.06634737 3.219297 0.055812097
#> 6 NA NA 20.32645 225 20.32645 -2.226453 0.03131875 3.280251 0.007824999
#> .std.resid y.observed t.value .se.fit fm.class fm.method fm.formula
#> 1 -0.6300752 21.0 2.042272 NA lm lm y ~ x
#> 2 -0.6300752 21.0 2.042272 NA lm lm y ~ x
#> 3 -0.7461691 22.8 2.042272 NA lm lm y ~ x
#> 4 0.7610697 21.4 2.042272 NA lm lm y ~ x
#> 5 1.2533143 18.7 2.042272 NA lm lm y ~ x
#> 6 -0.6957374 18.1 2.042272 NA lm lm y ~ x
#> fm.formula.chr PANEL group
#> 1 y ~ x 1 -1
#> 2 y ~ x 1 -1
#> 3 y ~ x 1 -1
#> 4 y ~ x 1 -1
#> 5 y ~ x 1 -1
#> 6 y ~ x 1 -1
# Regression by panel example
if (broom.installed)
ggplot(mtcars, aes(x = disp, y = mpg)) +
geom_point(aes(colour = factor(cyl))) +
stat_fit_augment(method = "lm",
method.args = list(formula = y ~ x))
# Residuals from regression by panel example
if (broom.installed)
ggplot(mtcars, aes(x = disp, y = mpg)) +
geom_hline(yintercept = 0, linetype = "dotted") +
stat_fit_augment(geom = "point",
method = "lm",
method.args = list(formula = y ~ x),
y.out = ".resid")
# Regression by group example
if (broom.installed)
ggplot(mtcars, aes(x = disp, y = mpg, colour = factor(cyl))) +
geom_point() +
stat_fit_augment(method = "lm",
method.args = list(formula = y ~ x))
# Residuals from regression by group example
if (broom.installed)
ggplot(mtcars, aes(x = disp, y = mpg, colour = factor(cyl))) +
geom_hline(yintercept = 0, linetype = "dotted") +
stat_fit_augment(geom = "point",
method.args = list(formula = y ~ x),
y.out = ".resid")
# Weighted regression example
if (broom.installed)
ggplot(mtcars, aes(x = disp, y = mpg, weight = cyl)) +
geom_point(aes(colour = factor(cyl))) +
stat_fit_augment(method = "lm",
method.args = list(formula = y ~ x,
weights = quote(weight)))
# Residuals from weighted regression example
if (broom.installed)
ggplot(mtcars, aes(x = disp, y = mpg, weight = cyl)) +
geom_hline(yintercept = 0, linetype = "dotted") +
stat_fit_augment(geom = "point",
method.args = list(formula = y ~ x,
weights = quote(weight)),
y.out = ".resid")
# Quantile regression
if (broom.installed)
ggplot(mtcars, aes(x = disp, y = mpg)) +
geom_point() +
stat_fit_augment(method = "rq")
```