Simulate gastric emptying data following a linexp or powexp function

Usage

simulate_gastempt(
  n_records = 10,
  v0_mean = 400,
  v0_std = 50,
  tempt_mean = ifelse(identical(model, linexp), 60, 120),
  tempt_std = tempt_mean/3,
  kappa_mean = 0.7,
  kappa_std = kappa_mean/3,
  beta_mean = 0.7,
  beta_std = beta_mean/3,
  noise = 20,
  student_t_df = NULL,
  missing = 0,
  model = linexp,
  seed = NULL,
  max_minute = NULL
)

Arguments

n_records: Number of records
v0_mean, v0_std: Mean and between record standard deviation of initial volume, typically in ml.
tempt_mean, tempt_std: Mean and between record standard deviation of parameter \(t_{empt}\), typically in minutes.
kappa_mean, kappa_std: For linexp only: Mean and between-record standard deviation of overshoot parameter kappa. For values of kappa above 1, curve has an overshoot that can be used to follow volume time series with secretion.
beta_mean, beta_std: For powexp only: Mean and between-record standard deviation of the so called lag parameter.
noise: Standard deviation of normal noise when student_t_df = NULL; scaling of noise when student_t_df >= 2.
student_t_df: When NULL (default), Gaussian noise is added; when >= 2, Student_t distributed noise is added, which generates more realistic outliers. Values from 2 to 5 are useful, when higher values are used the result comes close to that of Gaussian noise. Values below 2 are rounded to 2.
missing: When 0 (default), all curves have the same number of data points. When > 0, this is the fraction of points that were removed randomly to simulate missing points. Maximum value is 0.5.
model: linexp(default) or powexp
seed: optional seed; not set if seed = NULL (default)
max_minute: Maximal time in minutes; if NULL, a sensible default rounded to hours is used

Value

A list with 3 elements:

record: Data frame with columns record(chr), v0, tempt, kappa/beta giving the effective linexp or powexp parameters for the individual record. v0 is rounded to nearest integer.
data: Data frame with columns record(chr), minute(dbl), vol(dbl) giving the time series and grouping parameters. vol is rounded to nearest integer.
stan_data: A list for use as data in Stan-based fits with elements prior_v0, n, n_record, record, minute, volume.

A comment is attached to the return value that can be used as a title

Examples

suppressWarnings(RNGversion("3.5.0"))
set.seed(4711)
library(ggplot2)
vol_linexp = simulate_gastempt(n_records = 4, noise = 20)
ggplot(vol_linexp$data, aes(x = minute, y = vol)) + geom_point() +
  facet_wrap(~record) + ggtitle("linexp, noise = 0, no missing")


vol_powexp = simulate_gastempt(n_records = 4, missing = 0.2, student_t_df = 2)
ggplot(vol_powexp$data, aes(x = minute, y = vol)) + geom_point() +
  facet_wrap(~record) + ggtitle("powexp, noise = 10 (default), 20% missing,
  Student-t (df = 2) noise")