A visualiation for comparing slope-divided-by-intercept estimates for a number of designs

Usage

design_bakeoff(
  xmin,
  xmax,
  designs,
  gen_resp = function(xs) {
1 + 2 * xs + rnorm(length(xs), 0, 1)
 },
  Nsim = 1000,
  l_quantile_display = 0.01,
  u_quantile_display = 0.99,
  error_est = function(est) {
quantile(est, 0.99) - quantile(est, 0.01)
 },
  num_digits_round = 3,
  draw_theta_at = NULL,
  xlab_names = NULL,
  ...
)

Arguments

xmin: The minimum value of the independent variable.
xmax: The maximum value of the independent variable.
designs: A d x n matrix where each of the d rows is a design (the x values used to run the experiment).
gen_resp: A model for the response which takes the design as its parameter.
Nsim: The number of estimates per design. Default is 1000.
l_quantile_display: The lowest quantile of the simulation estimates displayed. Default is 0.025.
u_quantile_display: The highest quantile of the simulation estimates displayed. Default is 0.975.
error_est: The error metric for the estimates. The sample standard deviation (i.e. sd) is unstable at low sample sizes. The default is the 90 percentile minus the 10 percentile.
num_digits_round: The number of digits to round the error results. Default is 2.
draw_theta_at: If the user wishes to draw a horizontal line marking theta (to checked biasedness) it is specified here. The default is NULL with no line being drawn.
xlab_names: Text for the x-grid labels. This vector's size should equal lenth(designs).
...: Additional arguments passed to the boxplot function.

Value

A list with the simulated estimates and error estimates for each design.

Author

Adam Kapelner

Examples

xmin = 5 / 15
xmax = 19 / 1
n = 10 #must be even for this demo
designs = rbind(
  c(rep(xmin, n / 2), rep(xmax, n / 2)),       #design A
  seq(from = xmin, to = xmax, length.out = n)  #design B
)
design_bakeoff_info = design_bakeoff(xmin, xmax, designs) #design A wins