`stat_fit_deviations`

fits a linear model and returns fitted values and
residuals ready to be plotted as segments.

## Usage

```
stat_fit_deviations(
mapping = NULL,
data = NULL,
geom = "segment",
method = "lm",
method.args = list(),
n.min = 2L,
formula = NULL,
position = "identity",
na.rm = FALSE,
orientation = NA,
show.legend = FALSE,
inherit.aes = TRUE,
...
)
stat_fit_fitted(
mapping = NULL,
data = NULL,
geom = "point",
method = "lm",
method.args = list(),
n.min = 2L,
formula = NULL,
position = "identity",
na.rm = FALSE,
orientation = NA,
show.legend = FALSE,
inherit.aes = TRUE,
...
)
```

## 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
function or character If character, "lm", "rlm", "lqs", "rq" and the name of a function to be matched, possibly followed by the fit function's

`method`

argument separated by a colon (e.g.`"rq:br"`

). Functions implementing methods must accept arguments to parameters`formula`

,`data`

,`weights`

and`method`

. A`fitted()`

method must exist for the returned model fit object class.- method.args
named list with additional arguments.

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

- formula
a "formula" object. Using aesthetic names instead of original variable names.

- 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.

- orientation
character Either "x" or "y" controlling the default for

`formula`

.- 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 should not 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

This stat can be used to automatically highlight residuals as segments in a plot of a fitted model equation. This stat only generates the residuals, the predicted values need to be separately added to the plot, so to make sure that the same model formula is used in all steps it is best to save the formula as an object and supply this object as argument to the different statistics.

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 the model `formula`

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

## Note

In the case of `method = "rq"`

quantiles are fixed at ```
tau =
0.5
```

unless `method.args`

has length > 0. Parameter `orientation`

is redundant as it only affects the default for `formula`

but is
included for consistency with `ggplot2`

.

## Computed variables

Data frame with same `nrow`

as `data`

as subset for each group containing five numeric variables.

- x
x coordinates of observations

- x.fitted
x coordinates of fitted values

- y
y coordinates of observations

- y.fitted
y coordinates of fitted values

To explore the values returned by this statistic we suggest the use of
`geom_debug`

. An example is shown below, where one
can also see in addition to the computed values the default mapping of the
fitted values to aesthetics `xend`

and `yend`

.

## See also

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

,
`stat_fit_glance()`

,
`stat_fit_residuals()`

,
`stat_fit_tb()`

,
`stat_fit_tidy()`

## Examples

```
# generate artificial data
library(MASS)
set.seed(4321)
x <- 1:100
y <- (x + x^2 + x^3) + rnorm(length(x), mean = 0, sd = mean(x^3) / 4)
my.data <- data.frame(x, y)
# plot residuals from linear model
ggplot(my.data, aes(x, y)) +
geom_smooth(method = "lm", formula = y ~ x) +
stat_fit_deviations(method = "lm", formula = y ~ x, colour = "red") +
geom_point()
# plot residuals from linear model with y as explanatory variable
ggplot(my.data, aes(x, y)) +
geom_smooth(method = "lm", formula = y ~ x, orientation = "y") +
stat_fit_deviations(method = "lm", formula = x ~ y, colour = "red") +
geom_point()
# as above using orientation
ggplot(my.data, aes(x, y)) +
geom_smooth(method = "lm", orientation = "y") +
stat_fit_deviations(orientation = "y", colour = "red") +
geom_point()
#> `geom_smooth()` using formula = 'y ~ x'
# both regressions and their deviations
ggplot(my.data, aes(x, y)) +
geom_smooth(method = "lm") +
stat_fit_deviations(colour = "blue") +
geom_smooth(method = "lm", orientation = "y", colour = "red") +
stat_fit_deviations(orientation = "y", colour = "red") +
geom_point()
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'
# give a name to a formula
my.formula <- y ~ poly(x, 3, raw = TRUE)
# plot linear regression
ggplot(my.data, aes(x, y)) +
geom_smooth(method = "lm", formula = my.formula) +
stat_fit_deviations(formula = my.formula, colour = "red") +
geom_point()
ggplot(my.data, aes(x, y)) +
geom_smooth(method = "lm", formula = my.formula) +
stat_fit_deviations(formula = my.formula, method = stats::lm, colour = "red") +
geom_point()
# plot robust regression
ggplot(my.data, aes(x, y)) +
stat_smooth(method = "rlm", formula = my.formula) +
stat_fit_deviations(formula = my.formula, method = "rlm", colour = "red") +
geom_point()
# plot robust regression with weights indicated by colour
my.data.outlier <- my.data
my.data.outlier[6, "y"] <- my.data.outlier[6, "y"] * 10
ggplot(my.data.outlier, aes(x, y)) +
stat_smooth(method = MASS::rlm, formula = my.formula) +
stat_fit_deviations(formula = my.formula, method = "rlm",
mapping = aes(colour = after_stat(weights)),
show.legend = TRUE) +
scale_color_gradient(low = "red", high = "blue", limits = c(0, 1),
guide = "colourbar") +
geom_point()
# plot quantile regression (= median regression)
ggplot(my.data, aes(x, y)) +
stat_quantile(formula = my.formula, quantiles = 0.5) +
stat_fit_deviations(formula = my.formula, method = "rq", colour = "red") +
geom_point()
# plot quantile regression (= "quartile" regression)
ggplot(my.data, aes(x, y)) +
stat_quantile(formula = my.formula, quantiles = 0.75) +
stat_fit_deviations(formula = my.formula, colour = "red",
method = "rq", method.args = list(tau = 0.75)) +
geom_point()
# inspecting the returned data with geom_debug()
gginnards.installed <- requireNamespace("gginnards", quietly = TRUE)
if (gginnards.installed)
library(gginnards)
# plot, using geom_debug() to explore the after_stat data
if (gginnards.installed)
ggplot(my.data, aes(x, y)) +
geom_smooth(method = "lm", formula = my.formula) +
stat_fit_deviations(formula = my.formula, geom = "debug") +
geom_point()
#> [1] "PANEL 1; group(s) -1; 'draw_function()' input 'data' (head):"
#> xend yend x y x.fitted y.fitted weights hjust PANEL group
#> 1 1 -3691.7638 1 -27205.450 1 -3691.7638 1 0 1 -1
#> 2 2 -2578.9186 2 -14242.651 2 -2578.9186 1 0 1 -1
#> 3 3 -1498.5419 3 45790.918 3 -1498.5419 1 0 1 -1
#> 4 4 -443.8109 4 53731.420 4 -443.8109 1 0 1 -1
#> 5 5 592.0973 5 -8028.578 5 592.0973 1 0 1 -1
#> 6 6 1616.0056 6 102863.943 6 1616.0056 1 0 1 -1
#> orientation
#> 1 NA
#> 2 NA
#> 3 NA
#> 4 NA
#> 5 NA
#> 6 NA
if (gginnards.installed)
ggplot(my.data.outlier, aes(x, y)) +
stat_smooth(method = MASS::rlm, formula = my.formula) +
stat_fit_deviations(formula = my.formula, method = "rlm", geom = "debug") +
geom_point()
#> [1] "PANEL 1; group(s) -1; 'draw_function()' input 'data' (head):"
#> xend yend x y x.fitted y.fitted weights hjust PANEL
#> 1 1 -2515.4775 1 -27205.450 1 -2515.4775 1.00000000 0 1
#> 2 2 -1531.5327 2 -14242.651 2 -1531.5327 1.00000000 0 1
#> 3 3 -573.5792 3 45790.918 3 -573.5792 1.00000000 0 1
#> 4 4 365.1016 4 53731.420 4 365.1016 1.00000000 0 1
#> 5 5 1291.2282 5 -8028.578 5 1291.2282 1.00000000 0 1
#> 6 6 2211.5193 6 1028639.429 6 2211.5193 0.07650433 0 1
#> group orientation
#> 1 -1 NA
#> 2 -1 NA
#> 3 -1 NA
#> 4 -1 NA
#> 5 -1 NA
#> 6 -1 NA
```