animint2: geom_smooth – R documentation

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geom_smooth

Add a smoothed conditional mean.

Description

Aids the eye in seeing patterns in the presence of overplotting. geom_smooth and stat_smooth are effectively aliases: they both use the same arguments. Use geom_smooth unless you want to display the results with a non-standard geom.

Usage

geom_smooth(
  mapping = NULL,
  data = NULL,
  stat = "smooth",
  position = "identity",
  ...,
  method = "auto",
  formula = y ~ x,
  se = TRUE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_smooth(
  mapping = NULL,
  data = NULL,
  geom = "smooth",
  position = "identity",
  ...,
  method = "auto",
  formula = y ~ x,
  se = TRUE,
  n = 80,
  span = 0.75,
  fullrange = FALSE,
  level = 0.95,
  method.args = list(),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

`mapping`	Set of aesthetic mappings created by `aes` or `aes_`. If specified and `inherit.aes = TRUE` (the default), it is combined with the default mapping at the top level of the plot. You must supply `mapping` if there is no plot mapping.
`data`	The data to be displayed in this layer. There are three options: If `NULL`, the default, the data is inherited from the plot data as specified in the call to `ggplot`. A `data.frame`, or other object, will override the plot data. All objects will be fortified to produce a data frame. See `fortify` for which variables will be created. A `function` will be called with a single argument, the plot data. The return value must be a `data.frame.`, and will be used as the layer data.
`position`	Position adjustment, either as a string, or the result of a call to a position adjustment function.
`...`	other arguments passed on to `layer`. These are often aesthetics, used to set an aesthetic to a fixed value, like `color = "red"` or `size = 3`. They may also be parameters to the paired geom/stat.
`method`	smoothing method (function) to use, eg. lm, glm, gam, loess, rlm. For datasets with n < 1000 default is `loess`. For datasets with 1000 or more observations defaults to gam, see `gam` for more details.
`formula`	formula to use in smoothing function, eg. `y ~ x`, `y ~ poly(x, 2)`, `y ~ log(x)`
`se`	display confidence interval around smooth? (TRUE by default, see level to control
`na.rm`	If `FALSE` (the default), removes missing values with a warning. If `TRUE` silently removes missing values.
`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`.
`geom, stat`	Use to override the default connection between `geom_smooth` and `stat_smooth`.
`n`	number of points to evaluate smoother at
`span`	Controls the amount of smoothing for the default loess smoother. Smaller numbers produce wigglier lines, larger numbers produce smoother lines.
`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`	List of additional arguments passed on to the modelling function defined by `method`.

Details

Calculation is performed by the (currently undocumented) predictdf generic and its methods. For most methods the standard error bounds are computed using the predict method - the exceptions are loess which uses a t-based approximation, and glm where the normal confidence interval is constructed on the link scale, and then back-transformed to the response scale.

Aesthetics

geom_smoothunderstands the following aesthetics (required aesthetics are in bold):

x
y
alpha
colour
fill
linetype
size
weight

Computed variables

y: predicted value
ymin: lower pointwise confidence interval around the mean
ymax: upper pointwise confidence interval around the mean
se: standard error

Examples

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

# Use span to control the "wiggliness" of the default loess smoother
# The span is the fraction of points used to fit each local regression:
# small numbers make a wigglier curve, larger numbers make a smoother curve.
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth(span = 0.3)

# Instead of a loess smooth, you can use any other modelling function:
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth(method = "lm", formula = y ~ splines::bs(x, 3), se = FALSE)

# Smoothes 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() +
  geom_smooth(se = FALSE, method = "lm")
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth(span = 0.8) +
  facet_wrap(~drv)


binomial_smooth <- function(...) {
  geom_smooth(method = "glm", method.args = list(family = "binomial"), ...)
}
# To fit a logistic regression, you need to coerce the values to
# a numeric vector lying between 0 and 1.
ggplot(rpart::kyphosis, aes(Age, Kyphosis)) +
  geom_jitter(height = 0.05) +
  binomial_smooth()

ggplot(rpart::kyphosis, aes(Age, as.numeric(Kyphosis) - 1)) +
  geom_jitter(height = 0.05) +
  binomial_smooth()

ggplot(rpart::kyphosis, aes(Age, as.numeric(Kyphosis) - 1)) +
  geom_jitter(height = 0.05) +
  binomial_smooth(formula = y ~ splines::ns(x, 2))

# But in this case, it's probably better to fit the model yourself
# so you can exercise more control and see whether or not it's a good model

animint2

Animated Interactive Grammar of Graphics

v2020.9.18

GPL-3

Authors

Toby Hocking [aut, cre] (Original animint code), Hadley Wickham [aut] (Forked ggplot2 code), Winston Chang [aut] (Forked ggplot2 code), RStudio [cph] (Forked ggplot2 code), Nicholas Lewin-Koh [aut] (hexGrob), Martin Maechler [aut] (hexGrob), Randall Prium [aut] (cut_width), Susan VanderPlas [aut] (Animint GSOC 2013), Carson Sievert [aut] (Animint GSOC 2014), Kevin Ferris [aut] (Animint GSOC 2015), Jun Cai [aut] (Animint GSOC 2015), Faizan Khan [aut] (Animint GSOC 2016-2017), Vivek Kumar [aut] (Animint GSOC 2018), Himanshu Singh [aut] (Animint2 GSoC 2020)

Initial release