如何识别geom_smooth（）使用的函数

Question

I would like to display a plot created by geom_smooth() but it is important for me to be able to describe how the plot was created.

I can see from the documentation when n >= 1000, gam is used as the smoothing function, but I cannot see how many knots are used or what function generated the smoothing.

Example:

library(ggplot2)

set.seed(12345)
n <- 3000
x1 <- seq(0, 4*pi,, n)
x2 <- runif(n)
x3 <- rnorm(n)
lp <- 2*sin(2* x1)+3*x2 + 3*x3
p <- 1/(1+exp(-lp))
y <- ifelse(p > 0.5, 1, 0)

df <- data.frame(x1, x2, x3, y)

# default plot
ggplot(df, aes(x = x1, y = y)) +
  geom_smooth() 

# specify method='gam'
# linear
ggplot(df, aes(x = x1, y = y)) +
  geom_smooth(method = 'gam') 

# specify gam and splines
# Shows non-linearity, but different from default
ggplot(df, aes(x = x1, y = y)) +
  geom_smooth(method = 'gam',
              method.args = list(family = "binomial"),
              formula = y ~ splines::ns(x, 7)) 

If I want to use the default parameters, is there a way to identify the function used to create the smoothing so I can accurately describe it in a methods section of the analysis?

Answer 1

I wrote a function to reverse-engineer the steps used in StatSmooth 's setup_params function to get the actual method / formula parameters used for plotting.

The function expects a ggplot object as its input, with an additional optional parameter specifying the layer that corresponds to geom_smooth (defaults to 1 if unspecified). It returns a text string in the form "Method: [method used], Formula: [formula used]" , and also prints out all the parameters to console.

The envisaged use case is two-fold:

1. Add the text string as-is to the plot as plot title / subtitle / caption, for quick reference during analysis;
2. Read off the console printout, & include the information elsewhere or manually format it nicely (eg parsed plotmath expressions) for annotation in the plot, for report / presentation.

Function :

get.params <- function(plot, layer = 1){

  # return empty string if the specified geom layer doesn't use stat = "smooth"
  if(!"StatSmooth" %in% class(plot$layers[[layer]]$stat)){
    message("No smoothing function was used in this geom layer.")
    return("")
  }

  # recreate data used by this layer, in the format expected by StatSmooth
  # (this code chunk takes heavy reference from ggplot2:::ggplot_build.ggplot)
  layer.data <- plot$layers[[layer]]$layer_data(plot$data)
  layout <- ggplot2:::create_layout(plot$facet, plot$coordinates)
  data <- layout$setup(list(layer.data), plot$data, plot$plot_env)
  data[[1]] <- plot$layers[[layer]]$compute_aesthetics(data[[1]], plot)
  scales <- plot$scales
  data[[1]] <- ggplot2:::scales_transform_df(scales = scales, df = data[[1]])
  layout$train_position(data, scales$get_scales("x"), scales$get_scales("y"))
  data <- layout$map_position(data)[[1]]

  # set up stat params (e.g. replace "auto" with actual method / formula)
  stat.params <- suppressMessages(
    plot$layers[[layer]]$stat$setup_params(data = data, 
                                           params = plot$layers[[layer]]$stat_params)
    )

  # reverse the last step in setup_params; we don't need the actual function
  # for mgcv::gam, just the name
  if(identical(stat.params$method, mgcv::gam)) stat.params$method <- "gam"

  print(stat.params)

  return(paste0("Method: ", stat.params$method, ", Formula: ", deparse(stat.params$formula)))
}

Demonstration :

p <- ggplot(df, aes(x = x1, y = y)) # df is the sample dataset in the question

# default plot for 1000+ observations
# (method defaults to gam & formula to 'y ~ s(x, bs = "cs")')
p1 <- p + geom_smooth()
p1 + ggtitle(get.params(p1))

# specify method = 'gam'
# (formula defaults to `y ~ x`)
p2 <- p + geom_smooth(method='gam')
p2 + ggtitle(get.params(p2))

# specify method = 'gam' and splines for formula
p3 <- p + geom_smooth(method='gam',
              method.args = list(family = "binomial"),
              formula = y ~ splines::ns(x, 7))
p3 + ggtitle(get.params(p3))

# specify method = 'glm'
# (formula defaults to `y ~ x`)
p4 <- p + geom_smooth(method='glm')
p4 + ggtitle(get.params(p4))

# default plot for fewer observations
# (method defaults to loess & formula to `y ~ x`)
# observe that function is able to distinguish between plot data 
# & data actually used by the layer
p5 <- p + geom_smooth(data = . %>% slice(1:500))
p5 + ggtitle(get.params(p5))