`stat_fit_glance`

fits a model and returns a "tidy" version
of the model's fit, using '`glance()`

methods from packages 'broom',
'broom.mixed', or other sources.

## 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 data set - 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, glance.args
list of arguments to pass to

`method`

and to [generics::glance()], respectively.- n.min
integer Minimum number of distinct values in the explanatory variable (on the rhs of formula) for fitting to the attempted.

- label.x, label.y
`numeric`

with range 0..1 "normalized parent coordinates" (npc units) or character if using`geom_text_npc()`

or`geom_label_npc()`

. If using`geom_text()`

or`geom_label()`

numeric in native data units. If too short they will be recycled.- hstep, vstep
numeric in npc units, the horizontal and vertical step used between labels for different groups.

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

- na.rm
a 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.

## Value

The output of the `glance()`

methods is returned almost as is in
the `data`

object, as a data frame. The names of the columns in the
returned data are consistent with those returned by method `glance()`

from package 'broom', that will frequently differ from the name of values returned by the print methods corresponding to the fit or test function used. To explore the values returned by this statistic including the name of variables/columns, which vary depending on the model fitting function and model formula we suggest the use of

`geom_debug`

. An example is shown below.

## Details

`stat_fit_glance`

together with `stat_fit_tidy`

and
`stat_fit_augment`

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

Although arguments passed to parameter `glance.args`

will be
passed to [generics::glance()] whether they are silently ignored or obeyed
depends on each specialization of [glance()], so do carefully read the
documentation for the version of [glance()] 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_glance`

applies the function
given by `method`

separately to each group of observations, and
factors mapped to aesthetics, including `x`

and `y`

, create a
separate group for each factor level. Because of this,
`stat_fit_glance`

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

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

statistic which applies the model fitting per panel.

## Model formula required

The current implementation works only with
methods that accept a formula as argument and which have a `data`

parameter through which a data frame can be passed. For example,
`lm()`

should be used with the formula interface, as the evaluation of
`x`

and `y`

needs to be delayed until the internal `data`

object of the ggplot is available. With some methods like
`stats::cor.test()`

the data embedded in the `"ggplot"`

object
cannot be automatically passed as argument for the `data`

parameter of
the test or model fit function. Please, for annotations based on
`stats::cor.test()`

use `stat_correlation()`

.

## 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_augment()`

,
`stat_fit_deviations()`

,
`stat_fit_residuals()`

,
`stat_fit_tb()`

,
`stat_fit_tidy()`

## Examples

```
# package 'broom' needs to be installed to run these examples
if (requireNamespace("broom", quietly = TRUE)) {
broom.installed <- TRUE
library(broom)
library(quantreg)
# Inspecting the returned data using geom_debug()
if (requireNamespace("gginnards", quietly = TRUE)) {
library(gginnards)
ggplot(mtcars, aes(x = disp, y = mpg)) +
stat_smooth(method = "lm") +
geom_point(aes(colour = factor(cyl))) +
stat_fit_glance(method = "lm",
method.args = list(formula = y ~ x),
geom = "debug")
}
}
#> `geom_smooth()` using formula = 'y ~ x'
#> [1] "Summary of input 'data' to 'draw_panel()':"
#> npcx npcy r.squared adj.r.squared sigma statistic p.value df
#> 1 NA NA 0.7183433 0.7089548 3.251454 76.51266 9.380327e-10 1
#> logLik AIC BIC deviance df.residual nobs fm.class fm.method
#> 1 -82.10469 170.2094 174.6066 317.1587 30 32 lm lm
#> fm.formula fm.formula.chr x y PANEL group
#> 1 y ~ x y ~ x 91.145 32.725 1 -1
if (broom.installed)
# Regression by panel example
ggplot(mtcars, aes(x = disp, y = mpg)) +
stat_smooth(method = "lm", formula = y ~ x) +
geom_point(aes(colour = factor(cyl))) +
stat_fit_glance(method = "lm",
label.y = "bottom",
method.args = list(formula = y ~ x),
mapping = aes(label = sprintf('italic(r)^2~"="~%.3f~~italic(P)~"="~%.2g',
after_stat(r.squared), after_stat(p.value))),
parse = TRUE)
# Regression by group example
if (broom.installed)
ggplot(mtcars, aes(x = disp, y = mpg, colour = factor(cyl))) +
stat_smooth(method = "lm") +
geom_point() +
stat_fit_glance(method = "lm",
label.y = "bottom",
method.args = list(formula = y ~ x),
mapping = aes(label = sprintf('r^2~"="~%.3f~~italic(P)~"="~%.2g',
after_stat(r.squared), after_stat(p.value))),
parse = TRUE)
#> `geom_smooth()` using formula = 'y ~ x'
# Weighted regression example
if (broom.installed)
ggplot(mtcars, aes(x = disp, y = mpg, weight = cyl)) +
stat_smooth(method = "lm") +
geom_point(aes(colour = factor(cyl))) +
stat_fit_glance(method = "lm",
label.y = "bottom",
method.args = list(formula = y ~ x, weights = quote(weight)),
mapping = aes(label = sprintf('r^2~"="~%.3f~~italic(P)~"="~%.2g',
after_stat(r.squared), after_stat(p.value))),
parse = TRUE)
#> `geom_smooth()` using formula = 'y ~ x'
# correlation test
if (broom.installed)
ggplot(mtcars, aes(x = disp, y = mpg)) +
geom_point() +
stat_fit_glance(method = "cor.test",
label.y = "bottom",
method.args = list(formula = ~ x + y),
mapping = aes(label = sprintf('r[Pearson]~"="~%.3f~~italic(P)~"="~%.2g',
after_stat(estimate), after_stat(p.value))),
parse = TRUE)
#> 'formula' extracted from arguments
if (broom.installed)
ggplot(mtcars, aes(x = disp, y = mpg)) +
geom_point() +
stat_fit_glance(method = "cor.test",
label.y = "bottom",
method.args = list(formula = ~ x + y, method = "spearman", exact = FALSE),
mapping = aes(label = sprintf('r[Spearman]~"="~%.3f~~italic(P)~"="~%.2g',
after_stat(estimate), after_stat(p.value))),
parse = TRUE)
#> 'formula' extracted from arguments
# Quantile regression by group example
if (broom.installed)
ggplot(mtcars, aes(x = disp, y = mpg)) +
stat_smooth(method = "lm") +
geom_point() +
stat_fit_glance(method = "rq",
label.y = "bottom",
method.args = list(formula = y ~ x),
mapping = aes(label = sprintf('AIC = %.3g, BIC = %.3g',
after_stat(AIC), after_stat(BIC))))
#> `geom_smooth()` using formula = 'y ~ x'
#> Warning: Solution may be nonunique
```