pupil_solve: Main function that fits the penalized additive pupil model.
In JoKra1/papss: What the package does (short line)

pupil_solve

R Documentation

Main function that fits the penalized additive pupil model.

Description

This function fits one of the penalized pupil models that are available with this package.

Usage

pupil_solve(
  pulse_spacing,
  data,
  factor_id = "subject",
  model = "WIER_SHARED",
  n = 10.1,
  t_max = 930,
  f = 1/(10^24),
  drop_last = 500,
  maxiter_inner = 10000,
  maxiter_outer = 25,
  convergence_tol = 1e-08,
  should_collect_progress = F,
  start_lambda = 0.1,
  should_accum_H = F,
  init_cf = NULL,
  expand_by = 800,
  sample_length = 20,
  time_id = "time",
  pupil_id = "pupil"
)

Arguments

`pulse_spacing`	Model pulses every 'pulse_spacing' samples. Setting this to 1 ensures 1 pulse every sample
`data`	Aggregated data with a time and pupil column. Also needs a factor column
`factor_id`	Name of the factor column. Model will estimate demand trajectory for each level of this factor
`model`	Model template.
`n`	Choice for parameter defined by Hoeks & Levelt (number of laters)
`t_max`	Choice for parameter defined by Hoeks & Levelt (response maximum in ms)
`f`	Choice for parameter defined by Wierda et al. (scaling factor)
`drop_last`	Drop pulses that would happen in the last drop_last ms
`maxiter_inner`	Maximum steps taken by inner optimizer
`maxiter_outer`	Maximum steps taken by outer optimizer
`convergence_tol`	Convergence check to terminate early
`should_collect_progress`	If T, then the entire coefficient update history is collected and returned. VERY COSTLY.
`start_lambda`	Initial lambda value. Must be > 0 if a penalty should be used! Setting this to 0 and maxiter_outer=1, leads to estimation of an un-penalized additive model, i.e., recovers the traditional NNLS estimate used by Wierda et al. (2012) and Denison et al. (2012).
`should_accum_H`	Whether Hessian should be approximated using BFGS rule or not (see Fletcher, R. (2000). Practical Methods of Optimization). If not, then least squares Hessian matrix is used. With the BFGS rule models ended up being much smoother in our simulations. So this should be set to true if under-smoothing is observed. However, the BFGS update is much more costly and takes much more time!
`init_cf`	NULL or vector with initial coefficient estimate
`expand_by`	Time in ms by which to expand the time-series in the past. Then pulses that happened before the recorded time-window can still be approximated! See artificial_data_analysis vignette for details.
`sample_length`	Duration in ms of a single sample. If pupil dilation time-course was down-sampled to 50HZ, set this to 20
`time_id`	Name of time column in data
`pupil_id`	Name of pupil column in data