Predicted values and a confidence band are computed and, by default, plotted.
`stat_ma_line()`

behaves similarly to `stat_smooth`

except for fitting the model with `lmodel2::lmodel2()`

with `"MA"`

as default for `method`

.

## Usage

```
stat_ma_line(
mapping = NULL,
data = NULL,
geom = "smooth",
position = "identity",
...,
method = "lmodel2:MA",
method.args = list(),
n.min = 2L,
formula = NULL,
range.y = NULL,
range.x = NULL,
se = TRUE,
fm.values = FALSE,
n = 80,
nperm = 99,
fullrange = FALSE,
level = 0.95,
na.rm = FALSE,
orientation = NA,
show.legend = NA,
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

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

- ...
other arguments passed on to

`layer`

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

for more details.- method
function or character If character, "MA", "SMA" , "RMA" or "OLS", alternatively "lmodel2" or the name of a model fit function are accepted, possibly followed by the fit function's

`method`

argument separated by a colon (e.g.`"lmodel2:MA"`

). If a function different to`lmodel2()`

, it must accept arguments named`formula`

,`data`

,`range.y`

,`range.x`

and`nperm`

and return a model fit object of class`lmodel2`

.- 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

`x`

and`y`

instead of original variable names.- range.y, range.x
character Pass "relative" or "interval" if method "RMA" is to be computed.

- se
logical Return confidence interval around smooth? (`TRUE` by default, see `level` to control.)

- fm.values
logical Add R2, p-value and n as columns to returned data? (`FALSE` by default.)

- n
Number of points at which to evaluate smoother.

- nperm
integer Number of permutation used to estimate significance.

- fullrange
Should the fit span the full range of the plot, or just the data?

- level
Level of confidence interval to use (only 0.95 currently).

- 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 shouldn't inherit behaviour from the default plot specification, e.g.`borders`

.

## Value

The value returned by the statistic is a data frame, that will have

`n`

rows of predicted values and their confidence limits. Optionally
it will also include additional values related to the model fit.

## Details

This statistic fits major axis (`"MA"`

) and other model II
regressions with function `lmodel2`

. Model II
regression is called for when both `x`

and `y`

are subject to
random variation and the intention is not to predict `y`

from `x`

by means of the model but rather to study the relationship between two
independent variables. A frequent case in biology are allometric
relationships among body parts.

As the fitted line is the same whether `x`

or `y`

is on the rhs
of the model equation, `orientation`

even if accepted does not have an
effect on the fit. In contrast, `geom_smooth`

treats
each axis differently and can thus have two orientations. The orientation
is easy to deduce from the argument passed to `formula`

. Thus,
`stat_ma_line()`

will by default guess which orientation the layer
should have. If no argument is passed to `formula`

, the orientation
can be specified directly passing an argument to the `orientation`

parameter, which can be either `"x"`

or `"y"`

. The value gives
the axis that is on the rhs of the model equation, `"x"`

being the
default orientation. Package 'ggpmisc' does not define new geometries
matching the new statistics as they are not needed and conceptually
transformations of `data`

are expressed as statistics.

The minimum number of observations with distinct values can be set through
parameter `n.min`

. The default `n.min = 2L`

is the smallest
possible value. However, model fits with very few observations are of
little interest and using a larger number for `n.min`

than the default
is wise.

## Computed variables

`stat_ma_line()` provides the following variables, some of which depend on the orientation:

- y *or* x
predicted value

- ymin *or* xmin
lower pointwise confidence interval around the mean

- ymax *or* xmax
upper pointwise confidence interval around the mean

- se
standard error

If `fm.values = TRUE`

is passed then columns based on the summary of
the model fit are added, with the same value in each row within a group.
This is wasteful and disabled by default, but provides a simple and robust
approach to achieve effects like colouring or hiding of the model fit line
based on P-values, r-squared or the number of observations.

## Aesthetics

`stat_ma_line`

understands `x`

and `y`

,
to be referenced in the `formula`

. Both must be mapped to
`numeric`

variables. In addition, the aesthetics understood by the
geom (`"geom_smooth"`

is the default) are understood and grouping
respected.

## See also

Other ggplot statistics for major axis regression:
`stat_ma_eq()`

## Examples

```
# generate artificial data
set.seed(98723)
my.data <- data.frame(x = rnorm(100) + (0:99) / 10 - 5,
y = rnorm(100) + (0:99) / 10 - 5,
group = c("A", "B"))
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_ma_line()
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_ma_line(method = "MA")
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_ma_line(method = "SMA")
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_ma_line(method = "RMA",
range.y = "interval", range.x = "interval")
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_ma_line(method = "OLS")
# plot line to the ends of range of data (the default)
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_ma_line(fullrange = FALSE) +
expand_limits(x = c(-10, 10), y = c(-10, 10))
# plot line to the limits of the scales
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_ma_line(fullrange = TRUE) +
expand_limits(x = c(-10, 10), y = c(-10, 10))
# plot line to the limits of the scales
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_ma_line(orientation = "y", fullrange = TRUE) +
expand_limits(x = c(-10, 10), y = c(-10, 10))
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_ma_line(formula = x ~ y)
# Smooths are automatically fit to each group (defined by categorical
# aesthetics or the group aesthetic) and for each facet.
ggplot(my.data, aes(x, y, colour = group)) +
geom_point() +
stat_ma_line()
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_ma_line() +
facet_wrap(~group)
# Inspecting the returned data using geom_debug()
gginnards.installed <- requireNamespace("gginnards", quietly = TRUE)
if (gginnards.installed)
library(gginnards)
if (gginnards.installed)
ggplot(my.data, aes(x, y)) +
stat_ma_line(geom = "debug")
#> [1] "Summary of input 'data' to 'draw_panel()':"
#> x y ymin ymax flipped_aes PANEL group
#> 1 -6.560610 -6.050104 -6.711421 -5.450245 FALSE 1 -1
#> 2 -6.392213 -5.890429 -6.534468 -5.306240 FALSE 1 -1
#> 3 -6.223816 -5.730753 -6.357516 -5.162236 FALSE 1 -1
#> 4 -6.055419 -5.571077 -6.180563 -5.018232 FALSE 1 -1
#> 5 -5.887021 -5.411402 -6.003610 -4.874228 FALSE 1 -1
#> 6 -5.718624 -5.251726 -5.826657 -4.730224 FALSE 1 -1
if (gginnards.installed)
ggplot(my.data, aes(x, y)) +
stat_ma_line(geom = "debug", fm.values = TRUE)
#> [1] "Summary of input 'data' to 'draw_panel()':"
#> x y ymin ymax p.value r.squared n fm.class
#> 1 -6.560610 -6.050104 -6.711421 -5.450245 0.01 0.7917998 100 lmodel2
#> 2 -6.392213 -5.890429 -6.534468 -5.306240 0.01 0.7917998 100 lmodel2
#> 3 -6.223816 -5.730753 -6.357516 -5.162236 0.01 0.7917998 100 lmodel2
#> 4 -6.055419 -5.571077 -6.180563 -5.018232 0.01 0.7917998 100 lmodel2
#> 5 -5.887021 -5.411402 -6.003610 -4.874228 0.01 0.7917998 100 lmodel2
#> 6 -5.718624 -5.251726 -5.826657 -4.730224 0.01 0.7917998 100 lmodel2
#> fm.method fm.formula fm.formula.chr flipped_aes PANEL group
#> 1 lmodel2:MA y ~ x y ~ x FALSE 1 -1
#> 2 lmodel2:MA y ~ x y ~ x FALSE 1 -1
#> 3 lmodel2:MA y ~ x y ~ x FALSE 1 -1
#> 4 lmodel2:MA y ~ x y ~ x FALSE 1 -1
#> 5 lmodel2:MA y ~ x y ~ x FALSE 1 -1
#> 6 lmodel2:MA y ~ x y ~ x FALSE 1 -1
```