Type: Package
Title: Stan-Based Fit to Gastric Emptying Curves
Version: 0.8.9
Description: Stan-based curve-fitting function for use with package 'breathtestcore' by the same author. Stan functions are refactored here for easier testing.
License: GPL (≥ 3)
Encoding: UTF-8
ByteCompile: true
Depends: R (≥ 4.0.0), methods, Rcpp (≥ 1.0.6)
Imports: breathtestcore (≥ 0.8.8), dplyr, purrr, rstan (≥ 2.32.0), rstantools (≥ 2.4.0), stringr, tidyr
Suggests: ggplot2, shinystan, igraph, bayesplot, testthat, covr, knitr, parallelly, rmarkdown
LinkingTo: BH (≥ 1.72), Rcpp (≥ 1.0.6), RcppEigen (≥ 0.3.4), rstan (≥ 2.32.0), StanHeaders (≥ 2.26.0)
URL: https://github.com/dmenne/breathteststan, https://dmenne.github.io/breathteststan/
BugReports: https://github.com/dmenne/breathteststan/issues
NeedsCompilation: yes
SystemRequirements: GNU make
Config/testthat/edition: 3
Config/testthat/parallel: true
RoxygenNote: 7.3.2
Packaged: 2025-01-07 08:37:00 UTC; Dieter
Author: Dieter Menne [aut, cre], Menne Biomed Consulting Tuebingen [cph], Benjamin Misselwitz [fnd], Mark Fox [fnd], University Hospital of Zurich, Dep. Gastroenterology [fnd, dtc]
Maintainer: Dieter Menne <dieter.menne@menne-biomed.de>
Repository: CRAN
Date/Publication: 2025-01-08 09:00:07 UTC

S3 method to exctract the residual standard deviation

Description

Functions for S3 method defined in breathtestcore for stan_fit and stan_group fit.

Usage

## S3 method for class 'breathteststanfit'
sigma(object, ...)

Arguments

object

A Stan-based fit

...

Not used

Value

A numeric value giving the sigma (= average residual standard deviation) term from the Stan fit.

Bayesian Stan fit to 13C Breath Data

Description

Fits exponential beta curves to 13C breath test series data using Bayesian Stan methods. See https://menne-biomed.de/blog/breath-test-stan/ for a comparison between single curve, mixed-model population and Bayesian methods.

Usage

stan_fit(
  data,
  dose = 100,
  sample_minutes = 15,
  student_t_df = 10,
  chains = 2,
  iter = 1000,
  model = "breath_test_1",
  seed = 4711
)

Arguments

data

Data frame or tibble as created by cleanup_data, with mandatory columns patient_id, group, minute and pdr. It is recommended to run all data through cleanup_data which will insert dummy columns for patient_id and minute if the data are distinct, and report an error if not. Since the Bayesian method is stabilized by priors, it is possible to fit single curves.

dose

Dose of acetate or octanoate. Currently, only one common dose for all records is supported.

sample_minutes

If mean sampling interval is < sampleMinutes, data are subsampled using a spline algorithm

student_t_df

When student_t_df < 10, the student distribution is used to model the residuals. Recommended values to model typical outliers are from 3 to 6. When student_t_df >= 10, the normal distribution is used.

chains

Number of chains for Stan

iter

Number of iterations for each Stan chain

model

Name of model; use names(stanmodels) for other models.

seed

Optional seed for rstan

Value

A list of classes "breathteststanfit" and "breathtestfit" with elements

See Also

Base methods coef, plot, print; methods from package broom: tidy, augment.

Examples

library(breathtestcore)
suppressPackageStartupMessages(library(dplyr))
d = breathtestcore::simulate_breathtest_data(n_records = 3) # default 3 records
data = breathtestcore::cleanup_data(d$data)
# Use more than 80 iterations and 4 chains for serious fits
fit = stan_fit(data, chains = 1, iter = 80)
plot(fit) # calls plot.breathtestfit
# Extract coefficients and compare these with those
# used to generate the data
options(digits = 2)
cf = coef(fit)
cf %>%
  filter(grepl("m|k|beta", parameter )) %>%
  select(-method, -group) %>%
  tidyr::spread(parameter, value) %>%
  inner_join(d$record, by = "patient_id") %>%
  select(patient_id, m_in = m.y, m_out = m.x,
         beta_in = beta.y, beta_out = beta.x,
         k_in = k.y, k_out = k.x)
# For a detailed analysis of the fit, use the shinystan library

library(shinystan)
# launch_shinystan(fit$stan_fit)

# The following plots are somewhat degenerate because
# of the few iterations in stan_fit
suppressPackageStartupMessages(library(rstan))
stan_plot(fit$stan_fit, pars = c("beta[1]","beta[2]","beta[3]"))
stan_plot(fit$stan_fit, pars = c("k[1]","k[2]","k[3]"))
stan_plot(fit$stan_fit, pars = c("m[1]","m[2]","m[3]"))