test_lin function

Perform auto_rate() iteratively and extract performance metrics

Randomly generate a dataset and runs auto_rate() on the data to detect linear regions (with method = "linear"). The function plots 4 exploratory graphs and outputs the results of a linear regression between detected rate and true (known) rate, which can demonstrate how much the function is able to predict true rate.

test_lin(
  reps = 1,
  len = 300,
  sd = 0.05,
  type = "default",
  preview = FALSE,
  plot = FALSE
)

Arguments

• reps: numeric. Number of times to iterate auto_rate() on a randomly generated dataset. Defaults to 1.

• len: numeric. Length (number of observations) of the dataset to test auto_rate() on. Defaults to 300.

• sd: numeric. Noise to add to the data. Defaults to .05 standard difference.

• type: character. Use "default", "corrupted" or "segmented" to pick one of the three different kinds of data to generate.

• preview: logical. This will show the randomly-generated data in your plot window at every iteration. Note: will slow the function down.

  Useful to see the shape of the data. Defaults to FALSE.

• plot: logical. This will show the diagnostic plots of auto_rate() at every iteration. Note: will severely slow the function down. Useful to visualise what's being detected at every step. Defaults to FALSE.

Returns

An object of class test_lin. Contains linear regression results, and data required to plot diagnostics.

Examples

# run 3 iterations (please run at least 1000 times for more reliable visuals)
x <- test_lin(reps = 3)
# plot(x)
# plot(x, "a")  # view only plot "A"
# plot(x, "d")  # view only plot "D". You know what to do (for other plots).