test_lin: Perform 'auto_rate()' iteratively and extract performance...

In respR: Import, Process, Analyse, and Calculate Rates from Respirometry Data

Perform auto_rate() iteratively and extract performance metrics

Description

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.

Usage

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.

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)
# plot(x)
# plot(x, "a")  # view only plot "A"
# plot(x, "d")  # view only plot "D". You know what to do (for other plots).

respR documentation built on May 29, 2024, 7:14 a.m.