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mmgm: Estimates the degree of decomposition of peat with a mixing model for gamma_MIRS

26 Sep 14:12
@henningte henningte
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mmgm

Package mmgm provides a mixing model that allows to estimate the
degree of decomposition ($\gamma$) of a peat sample (mixture) and of its
individual components from bulk estimates of $\gamma_\text{MIRS}$
(Teickner et al. 2025).

Installation

You can install the development version of mmgm like so:

remotes::install_github("henningte/mmgm")

Example

To estimate the mixing model, one needs to provide a list with two data
frames as elements. The first element, components contains information
on components, and the second element, mixtures, contains information
on the mixtures (peat samples):

library(mmgm)
library(cmdstanr)
#> Warning: package 'cmdstanr' was built under R version 4.3.3
#> This is cmdstanr version 0.8.1
#> - CmdStanR documentation and vignettes: mc-stan.org/cmdstanr
#> - CmdStan path: C:/Users/henni/.cmdstan/cmdstan-2.36.0
#> - CmdStan version: 2.36.0
#> 
#> A newer version of CmdStan is available. See ?install_cmdstan() to install it.
#> To disable this check set option or environment variable cmdstanr_no_ver_check=TRUE.

# show structure of the example data
mmgm_example_data
#> $components
#>     id_mixture id_component         w
#> 1.1          1            1 0.5956979
#> 1.2          1            2 0.4043021
#> 2.1          2            1 0.2799653
#> 2.2          2            2 0.7200347
#> 3.1          3            1 0.5942072
#> 3.2          3            2 0.4057928
#> 4.1          4            1 0.4683780
#> 4.2          4            2 0.5316220
#> 5.1          5            1 0.1578412
#> 5.2          5            2 0.8421588
#> 
#> $mixtures
#> # A tibble: 5 × 2
#>   id_mixture degree_of_decomposition_2
#> *      <int> <quantits>               
#> 1          1 0.06227749               
#> 2          2 0.01924212               
#> 3          3 0.02330240               
#> 4          4 0.03896350               
#> 5          5 0.12042913

To prepare estimation of the mixing model, this list is processed with
mmgm_make_stan_data():

stan_data <- 
  mmgm_make_stan_data(
    x = mmgm_example_data, 
    id_model_gamma_mirs = 2, 
    priors = mmgm_make_default_priors(x = mmgm_example_data)
  )

Here, id_model_gamma_mirs defines the model that was used to predict
$\gamma_\text{MIRS}$ (the number of the model in the irpeatmodels
package (Teickner 2025)). priors defines the priors for the mixing
model and mmgm_make_default_priors() is a helper function that allows
to define default priors for a given list of data.

stan_data can be passed to mmgm_estimate_gamma_mirs_mixing_1() to
estimate the mixing model using CmdStan. Use the CmdStan
arguments
to adjust MCMC sampling:

stan_fit <- 
  mmgm_estimate_gamma_mirs_mixing_1(
    stan_data = stan_data,
    iter_warmup = 1000,
    iter_sampling = 1000,
    chains = 4,
    sig_figs = 14,
    seed = 7667
  )
#> Running MCMC with 4 sequential chains...
#> 
#> Chain 1 Iteration:    1 / 2000 [  0%]  (Warmup) 
#> Chain 1 Iteration:  100 / 2000 [  5%]  (Warmup) 
#> Chain 1 Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: gamma_lpdf: Random variable[2] is 0, but must be positive finite! (in 'C:/Users/henni/AppData/Local/Temp/RtmpCCJ84p/model-df0ea5142c.stan', line 43, column 2 to column 40)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: beta_lpdf: Second shape parameter[2] is 0, but must be positive finite! (in 'C:/Users/henni/AppData/Local/Temp/RtmpCCJ84p/model-df0ea5142c.stan', line 48, column 4 to column 151)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Iteration:  300 / 2000 [ 15%]  (Warmup) 
#> Chain 1 Iteration:  400 / 2000 [ 20%]  (Warmup) 
#> Chain 1 Iteration:  500 / 2000 [ 25%]  (Warmup) 
#> Chain 1 Iteration:  600 / 2000 [ 30%]  (Warmup) 
#> Chain 1 Iteration:  700 / 2000 [ 35%]  (Warmup) 
#> Chain 1 Iteration:  800 / 2000 [ 40%]  (Warmup) 
#> Chain 1 Iteration:  900 / 2000 [ 45%]  (Warmup)
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: beta_lpdf: Second shape parameter[5] is 0, but must be positive finite! (in 'C:/Users/henni/AppData/Local/Temp/RtmpCCJ84p/model-df0ea5142c.stan', line 49, column 4 to column 110)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: beta_lpdf: Second shape parameter[5] is 0, but must be positive finite! (in 'C:/Users/henni/AppData/Local/Temp/RtmpCCJ84p/model-df0ea5142c.stan', line 48, column 4 to column 151)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
#> Chain 1 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
#> Chain 1 Iteration: 1100 / 2000 [ 55%]  (Sampling) 
#> Chain 1 Iteration: 1200 / 2000 [ 60%]  (Sampling) 
#> Chain 1 Iteration: 1300 / 2000 [ 65%]  (Sampling) 
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#> Chain 1 Iteration: 1800 / 2000 [ 90%]  (Sampling) 
#> Chain 1 Iteration: 1900 / 2000 [ 95%]  (Sampling) 
#> Chain 1 Iteration: 2000 / 2000 [100%]  (Sampling) 
#> Chain 1 finished in 0.4 seconds.
#> Chain 2 Iteration:    1 / 2000 [  0%]  (Warmup) 
#> Chain 2 Iteration:  100 / 2000 [  5%]  (Warmup) 
#> Chain 2 Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: beta_lpdf: Second shape parameter[3] is 0, but must be positive finite! (in 'C:/Users/henni/AppData/Local/Temp/RtmpCCJ84p/model-df0ea5142c.stan', line 49, column 4 to column 110)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: beta_lpdf: Second shape parameter[3] is 0, but must be positive finite! (in 'C:/Users/henni/AppData/Local/Temp/RtmpCCJ84p/model-df0ea5142c.stan', line 49, column 4 to column 110)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: beta_lpdf: First shape parameter[1] is 0, but must be positive finite! (in 'C:/Users/henni/AppData/Local/Temp/RtmpCCJ84p/model-df0ea5142c.stan', line 48, column 4 to column 151)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2
#> Chain 2 Iteration:  300 / 2000 [ 15%]  (Warmup) 
#> Chain 2 Iteration:  400 / 2000 [ 20%]  (Warmup) 
#> Chain 2 Iteration:  500 / 2000 [ 25%]  (Warmup) 
#> Chain 2 Iteration:  600 / 2000 [ 30%]  (Warmup) 
#> Chain 2 Iteration:  700 / 2000 [ 35%]  (Warmup) 
#> Chain 2 Iteration:  800 / 2000 [ 40%]  (Warmup) 
#> Chain 2 Iteration:  900 / 2000 [ 45%]  (Warmup)
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: beta_lpdf: Second shape parameter[5] is 0, but must be positive finite! (in 'C:/Users/henni/AppData/Local/Temp/RtmpCCJ84p/model-df0ea5142c.stan', line 48, column 4 to column 151)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2
#> Chain 2 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
#> Chain 2 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
#> Chain 2 Iteration: 1100 / 2000 [ 55%]  (Sampling) 
#> Chain 2 Iteration: 1200 / 2000 [ 60%]  (Sampling) 
#> Chain 2 Iteration: 1300 / 2000 [ 65%]  (Sampling) 
#> Chain 2 Iteration: 140...
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