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#' @param method Smoothing method (function) to use, accepts either a character vector,
#' e.g. `"auto"`, `"lm"`, `"glm"`, `"gam"`, `"loess"` or a function, e.g.
#' `MASS::rlm` or `mgcv::gam`, `base::lm`, or `base::loess`.
#'
#' For `method = "auto"` the smoothing method is chosen based on the
#' size of the largest group (across all panels). [loess()] is
#' used for less than 1,000 observations; otherwise [mgcv::gam()] is
#' used with `formula = y ~ s(x, bs = "cs")`. Somewhat anecdotally,
#' `loess` gives a better appearance, but is \eqn{O(N^{2})}{O(N^2)} in memory,
#' so does not work for larger datasets.
#'
#' If you have fewer than 1,000 observations but want to use the same `gam()`
#' model that `method = "auto"` would use, then set
#' `method = "gam", formula = y ~ s(x, bs = "cs")`.
#' @param formula Formula to use in smoothing function, eg. `y ~ x`,
#' `y ~ poly(x, 2)`, `y ~ log(x)`
#' @param se Display confidence interval around smooth? (`TRUE` by default, see
#' `level` to control.)
#' @param fullrange Should the fit span the full range of the plot, or just
#' the data?
#' @param level Level of confidence interval to use (0.95 by default).
#' @param span Controls the amount of smoothing for the default loess smoother.
#' Smaller numbers produce wigglier lines, larger numbers produce smoother
#' lines.
#' @param n Number of points at which to evaluate smoother.
#' @param method.args List of additional arguments passed on to the modelling
#' function defined by `method`.
#' @section Computed variables:
#' \describe{
#' \item{y}{predicted value}
#' \item{ymin}{lower pointwise confidence interval around the mean}
#' \item{ymax}{upper pointwise confidence interval around the mean}
#' \item{se}{standard error}
#' }
#' @export
#' @rdname geom_smooth
stat_smooth <- function(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) {
layer(
data = data,
mapping = mapping,
stat = StatSmooth,
geom = geom,
position = position,
show.legend = show.legend,
inherit.aes = inherit.aes,
params = list(
method = method,
formula = formula,
se = se,
n = n,
fullrange = fullrange,
level = level,
na.rm = na.rm,
method.args = method.args,
span = span,
...
)
)
}
#' @rdname ggplot2-ggproto
#' @format NULL
#' @usage NULL
#' @export
StatSmooth <- ggproto("StatSmooth", Stat,
setup_params = function(data, params) {
if (identical(params$method, "auto")) {
# Use loess for small datasets, gam with a cubic regression basis for
# larger. Based on size of the _largest_ group to avoid bad memory
# behaviour of loess
max_group <- max(table(interaction(data$group, data$PANEL, drop = TRUE)))
if (max_group < 1000) {
params$method <- "loess"
} else {
params$method <- "gam"
params$formula <- y ~ s(x, bs = "cs")
}
message("`geom_smooth()` using method = '", params$method,
"' and formula '", deparse(params$formula), "'")
}
if (identical(params$method, "gam")) {
params$method <- mgcv::gam
}
params
},
compute_group = function(data, scales, method = "auto", formula = y~x,
se = TRUE, n = 80, span = 0.75, fullrange = FALSE,
xseq = NULL, level = 0.95, method.args = list(),
na.rm = FALSE) {
if (length(unique(data$x)) < 2) {
# Not enough data to perform fit
return(data.frame())
}
if (is.null(data$weight)) data$weight <- 1
if (is.null(xseq)) {
if (is.integer(data$x)) {
if (fullrange) {
xseq <- scales$x$dimension()
} else {
xseq <- sort(unique(data$x))
}
} else {
if (fullrange) {
range <- scales$x$dimension()
} else {
range <- range(data$x, na.rm = TRUE)
}
xseq <- seq(range[1], range[2], length.out = n)
}
}
# Special case span because it's the most commonly used model argument
if (identical(method, "loess")) {
method.args$span <- span
}
if (is.character(method)) method <- match.fun(method)
base.args <- list(quote(formula), data = quote(data), weights = quote(weight))
model <- do.call(method, c(base.args, method.args))
predictdf(model, xseq, se, level)
},
required_aes = c("x", "y")
)