stat_poly_line: Predicted line from linear model fit

In ggpmisc: Miscellaneous Extensions to 'ggplot2'

Predicted line from linear model fit

Description

Predicted values and a confidence band are computed and, by default, plotted.

Usage

stat_poly_line(
  mapping = NULL,
  data = NULL,
  geom = "smooth",
  position = "identity",
  ...,
  method = "lm",
  formula = NULL,
  se = TRUE,
  fm.values = FALSE,
  n = 80,
  fullrange = FALSE,
  level = 0.95,
  method.args = list(),
  n.min = 2L,
  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, "lm", "rlm" 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. "rlm:M"). If a function different to lm(), it must accept arguments named formula, data, weights, and method and return a model fit object of class lm.
formula	a formula object. Using aesthetic names x and y instead of original variable names.
se	Display confidence interval around smooth? ('TRUE' by default, see 'level' to control.)
fm.values	logical Add R2, adjusted R2, p-value and n as columns to returned data? ('FALSE' by default.)
n	Number of points at which to evaluate smoother.
fullrange	Should the fit span the full range of the plot, or just the data?
level	Level of confidence interval to use (0.95 by default).
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.
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.

Details

This statistic is similar to stat_smooth but has different defaults. It interprets the argument passed to formula differently, accepting y as explanatory variable and setting orientation automatically. The default for method is "lm" and spline-based smoothers like loess are not supported. Other defaults are consistent with those in stat_poly_eq(), stat_quant_line(), stat_quant_eq(), stat_ma_line(), stat_ma_eq().

geom_poly_line() treats the x and y aesthetics differently and can thus have two orientations. The orientation can be deduced from the argument passed to formula. Thus, stat_poly_line() will by default guess which orientation the layer should have. If no argument is passed to formula, the formula defaults to y ~ x. For consistency with stat_smooth orientation can be also specified directly passing an argument to the orientation parameter, which can be either "x" or "y". The value of orientation gives the axis that is taken as the explanatory variable or x in the model formula. Package 'ggpmisc' does not define new geometries matching the new statistics as they are not needed and conceptually transformations of data are statistics in the grammar of graphics.

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. stat_poly_eq() mimics how stat_smooth() works, except that only polynomials can be fitted. Similarly to these statistics the model fits respect grouping, so the scales used for x and y should both be continuous scales rather than discrete.

With method "lm", singularity results in terms being dropped with a message if more numerous than can be fitted with a singular (exact) fit. In this case and if the model results in a perfect fit due to low number of observation, estimates for various parameters are NaN or NA.

With methods other than "lm", the model fit functions simply fail in case of singularity, e.g., singular fits are not implemented in "rlm".

In both cases the minimum number of observations with distinct values in the explanatory variable can be set through parameter n.min. The default n.min = 2L is the smallest suitable for method "lm" but too small for method "rlm" for which n.min = 3L is needed. Anyway, model fits with very few observations are of little interest and using larger values of n.min than the default is wise.

Value

The value returned by the statistic is a data frame, with n rows of predicted values and their confidence limits. Optionally it will also include additional values related to the model fit.

Computed variables

'stat_poly_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, adjusted r-squared or the number of observations.

Aesthetics

stat_poly_line understands x and y, to be referenced in the formula and weight passed as argument to parameter weights. All three 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 linear and polynomial regression: stat_poly_eq()

Examples

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_poly_line()

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_poly_line(formula = x ~ y)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_poly_line(formula = y ~ poly(x, 3))

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_poly_line(formula = x ~ poly(y, 3))

# Smooths are automatically fit to each group (defined by categorical
# aesthetics or the group aesthetic) and for each facet.

ggplot(mpg, aes(displ, hwy, colour = class)) +
  geom_point() +
  stat_poly_line(se = FALSE)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_poly_line() +
  facet_wrap(~drv)

# Inspecting the returned data using geom_debug()
gginnards.installed <- requireNamespace("gginnards", quietly = TRUE)

if (gginnards.installed)
  library(gginnards)

if (gginnards.installed)
  ggplot(mpg, aes(displ, hwy)) +
    stat_poly_line(geom = "debug")

if (gginnards.installed)
  ggplot(mpg, aes(displ, hwy)) +
    stat_poly_line(geom = "debug", fm.values = TRUE)

if (gginnards.installed)
  ggplot(mpg, aes(displ, hwy)) +
    stat_poly_line(geom = "debug", method = lm, fm.values = TRUE)

ggpmisc documentation built on Nov. 16, 2023, 5:10 p.m.