# stan_fit

##### Bayesian Stan fit to 13C Breath Data

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 comparision 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

`coef`

Estimated parameters as data frame in a key-value format with columns`patient_id, group, parameter, method`

and`value`

. Has an attribute AIC.`data`

The effectively analyzed data. If density of points is too high, e.g. with BreathId devices, data are subsampled before fitting.`stan_fit`

The Stan fit for use with`shinystan::launch_shiny`

or extraction of chains.

##### See Also

Base methods `coef, plot, print`

; methods from package
`broom: tidy, augment`

.

##### Examples

```
# NOT RUN {
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
# }
# NOT RUN {
library(shinystan)
# launch_shinystan(fit$stan_fit)
# }
# NOT RUN {
# The following plots are somewhat degenerate because
# of the few iterations in stan_fit
suppressPackageStartupMessages(library(rstan))
stan_plot(fit$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]"))
# }
```

*Documentation reproduced from package breathteststan, version 0.4.7, License: GPL (>= 3)*