stan_gastempt {gastempt}

Fit gastric emptying curves with Stan

Description

Fit gastric emptying curves with Stan

Usage

stan_gastempt(
  d,
  model_name = "linexp_gastro_2b",
  lkj = 2,
  student_df = 5L,
  init_r = 0.2,
  chains = 1,
  iter = 2000,
  ...
)

Arguments

d

A data frame with columns

• rec Record descriptor as grouping variable, e.g. patient ID

• minute Time after meal or start of recording.

• vol Volume of meal or stomach

model_name

Name of predefined model in gastempt/exec. Use stan_model_names() to get a list of available models.

lkj

LKJ prior for kappa/tempt correlation, only required for model linexp_gastro_2b. Values from 1.5 (strong correlation) to 50 (almost independent) are useful.

student_df

Student-t degrees of freedom for residual error; default 5. Use 3 for strong outliers; values above 10 are close to gaussian residual distribution.

init_r

for stan, default = 0.2; Stan's own default is 2, which often results in stuck chains.

chains

for stan; default = 1

iter

A positive integer specifying the number of iterations for each chain (including warmup). The default is 2000.

...

Additional parameter passed to sampling and stan

Value

A list of class stan_gastempt with elements coef, fit, plot

• coef is a data frame with columns:

  • rec Record descriptor, e.g. patient ID

  • v0 Initial volume at t=0

  • tempt Emptying time constant

  • kappa Parameter kappa for model = linexp

  • beta Parameter beta for model = powexp

  • t50 Half-time of emptying

  • slope_t50 Slope in t50; typically in units of ml/minute On error, coef is NULL

• fit Result of class 'stanfit'

• plot A ggplot graph of data and prediction. Plot of raw data is returned even when convergence was not achieved.

Examples

 # Runs 30+ seconds on CRAN
 dd = simulate_gastempt(n_records = 6, seed = 471)
 d = dd$data
 ret = stan_gastempt(d)
 print(ret$coef)