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 )
reps | numeric. Number of times to iterate |
---|---|
len | numeric. Length (number of observations) of the dataset to test
|
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 |
An object of class test_lin
. Contains linear regressin 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)#> Error in test_lin(reps = 3): could not find function "test_lin"# plot(x) # plot(x, "a") # view only plot "A" # plot(x, "d") # view only plot "D". You know what to do (for other plots)..