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.
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.
Value
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)
#> auto_rate: Applying default 'width' of 0.2
#> auto_rate: Applying default 'width' of 0.2
#> auto_rate: Applying default 'width' of 0.2
# plot(x)
# plot(x, "a") # view only plot "A"
# plot(x, "d") # view only plot "D". You know what to do (for other plots).