glassbox: The opinionated "glass box" `eyeris` pipeline

Description

This glassbox function (in contrast to a "black box" function where you run it and get a result but have no (or little) idea as to how you got from input to output) has a few primary benefits over calling each exported function from eyeris separately.

Usage

glassbox(
  file,
  interactive_preview = FALSE,
  preview_n = 3,
  preview_duration = 5,
  preview_window = NULL,
  verbose = TRUE,
  ...,
  confirm = deprecated(),
  num_previews = deprecated(),
  detrend_data = deprecated(),
  skip_detransient = deprecated()
)

Value

Preprocessed pupil data contained within an object of class eyeris

Arguments

file: An SR Research EyeLink .asc file generated by the official EyeLink edf2asc command
interactive_preview: A flag to indicate whether to run the glassbox pipeline autonomously all the way through (set to FALSE by default), or to interactively provide a visualization after each pipeline step, where you must also indicate "(y)es" or "(n)o" to either proceed or cancel the current glassbox pipeline operation (set to TRUE)
preview_n: Number of random example "epochs" to generate for previewing the effect of each preprocessing step on the pupil timeseries
preview_duration: Time in seconds of each randomly selected preview
preview_window: The start and stop raw timestamps used to subset the preprocessed data from each step of the eyeris workflow for visualization. Defaults to NULL, meaning random epochs as defined by preview_n and preview_duration will be plotted. To override the random epochs, set preview_window here to a vector with relative start and stop times (in seconds), for example -- c(5,6) -- to indicate the raw data from 5-6 secs on data that were recorded at 1000 Hz). Note, the start/stop time values indicated here are in seconds because eyeris automatically computes the indices for the supplied range of seconds using the $info$sample.rate metadata in the eyeris S3 class object
verbose: A logical flag to indicate whether to print status messages to the console. Defaults to TRUE. Set to FALSE to suppress messages about the current processing step and run silently
...: Additional arguments to override the default, prescribed settings
confirm: (Deprecated) Use interactive_preview instead
num_previews: (Deprecated) Use preview_n instead
detrend_data: (Deprecated) A flag to indicate whether to run the detrend step (set to FALSE by default). Detrending your pupil timeseries can have unintended consequences; we thus recommend that users understand the implications of detrending -- in addition to whether detrending is appropriate for the research design and question(s) -- before using this function
skip_detransient: (Deprecated) A flag to indicate whether to skip the detransient step (set to FALSE by default). In most cases, this should remain FALSE. For a more detailed description about likely edge cases that would prompt you to set this to TRUE, see the docs for detransient()

Details

First, this glassbox function provides a highly opinionated prescription of steps and starting parameters we believe any pupillometry researcher should use as their defaults when preprocessing pupillometry data.

Second, and not mutually exclusive from the first point, using this function should ideally reduce the probability of accidental mishaps when "reimplementing" the steps from the preprocessing pipeline both within and across projects. We hope to streamline the process in such a way that you could collect a pupillometry dataset and within a few minutes assess the quality of those data while simultaneously running a full preprocessing pipeline in 1-ish line of code!

Third, glassbox provides an "interactive" framework where you can evaluate the consequences of the parameters within each step on your data in real time, facilitating a fairly easy-to-use workflow for parameter optimization on your particular dataset. This process essentially takes each of the opinionated steps and provides a pre-/post-plot of the timeseries data for each step so you can adjust parameters and re-run the pipeline until you are satisfied with the choices of your paramters and their consequences on your pupil timeseries data.

Examples

Run this code

demo_data <- eyelink_asc_demo_dataset()

# (1) examples using the default prescribed parameters and pipeline recipe

## (a) run an automated pipeline with no real-time inspection of parameters
output <- eyeris::glassbox(demo_data)

start_time <- min(output$timeseries$block_1$time_secs)
end_time <- max(output$timeseries$block_1$time_secs)

# by default, verbose = TRUE. To suppress messages, set verbose = FALSE.
plot(
  output,
  steps = c(1, 5),
  preview_window = c(start_time, end_time),
  seed = 0
)

## (b) run a interactive workflow (with confirmation prompts after each step)
# \donttest{
output <- eyeris::glassbox(demo_data, interactive_preview = TRUE, seed = 0)
# }

# (2) examples of overriding the default parameters
output <- eyeris::glassbox(
  demo_data,
  interactive_preview = FALSE, # TRUE to visualize each step in real-time
  deblink = list(extend = 40),
  lpfilt = list(plot_freqz = TRUE) # overrides verbose parameter
)

# to suppress messages, set verbose = FALSE in plot():
plot(output, seed = 0, verbose = FALSE)

# (3) examples of disabling certain steps
output <- eyeris::glassbox(
  demo_data,
  detransient = FALSE,
  detrend = FALSE,
  zscore = FALSE
)

plot(output, seed = 0)