Plot a trend on log-log paper.

LogLogPlot(
  frame,
  xvar,
  yvar,
  title,
  ...,
  use_coord_trans = FALSE,
  point_color = "black",
  linear_color = "#018571",
  quadratic_color = "#a6611a",
  smoothing_color = "blue"
)

Arguments

frame

data frame to get values from

xvar

name of the independent (input or model) column in frame

yvar

name of the dependent (output or result to be modeled) column in frame

title

title to place on plot

...

no unnamed argument, added to force named binding of later arguments.

use_coord_trans

logical if TRUE, use coord_trans instead of coord_trans(x = "log10", y = "log10") instead of scale_x_log10() + scale_y_log10() (useful when there is not enough range to show ticks).

point_color

the color of the data points

linear_color

the color of the linear growth lines

quadratic_color

the color of the quadratic growth lines

smoothing_color

the color of the smoothing line through the data

Details

This plot is intended for plotting functions that are observed costs or durations as a function of problem size. In this case we expect the ideal or expected cost function to be non-decreasing. Any negative trends are assumed to arise from the noise model. The graph is specialized to compare non-decreasing linear and non-decreasing quadratic growth.

Some care must be taken in drawing conclusions from log-log plots, as the transform is fairly violent. Please see: "(Mar's Law) Everything is linear if plotted log-log with a fat magic marker" (from Akin's Laws of Spacecraft Design http://spacecraft.ssl.umd.edu/akins_laws.html), and "So You Think You Have a Power Law" http://bactra.org/weblog/491.html.

Examples

set.seed(5326) frm = data.frame(x = 1:20) frm$y <- 5 + frm$x + 0.2 * frm$x * frm$x + 0.1*abs(rnorm(nrow(frm))) WVPlots::LogLogPlot(frm, "x", "y", title="Example Trend")
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'