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
)numeric. Number of times to iterate auto_rate() on a randomly
generated dataset. Defaults to 1.
numeric. Length (number of observations) of the dataset to test
auto_rate() on. Defaults to 300.
numeric. Noise to add to the data. Defaults to .05 standard difference.
character. Use "default", "corrupted" or "segmented" to pick one of the three different kinds of data to generate.
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.
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.
An object of class test_lin. Contains linear regression results, and
data required to plot diagnostics.
# 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).