Generate a coherent list of model specs — specs • streamMetabolizer

Generates an internally consistent list of model specifications that may be passed to metab_bayes, metab_mle, etc. via the specs argument. This help file gives the definitive list of all possible model specs, but only a subset of these are relevant to any given model_name. See the 'Relevant arguments' section below. Irrelevant arguments for the given model_name should not be explicitly passed into this function (but don't worry - we'll just stop and tell you if you make a mistake). Relevant arguments for the given model_name either have default values or do not (see Usage). Relevant arguments without a default should rarely be overridden, because their values will be determined based on other arguments. Relevant arguments that do have a default can, and often should, be overridden to tailor the model to your needs.

Usage

specs(
  model_name = mm_name(),
  engine,
  day_start = 4,
  day_end = 28,
  day_tests = c("full_day", "even_timesteps", "complete_data", "pos_discharge",
    "pos_depth"),
  required_timestep = NA,
  init.GPP.daily = 8,
  init.Pmax = 10,
  init.alpha = 1e-04,
  init.ER.daily = -10,
  init.ER20 = -10,
  init.K600.daily = 10,
  split_dates,
  keep_mcmcs = TRUE,
  keep_mcmc_data = TRUE,
  GPP_daily_mu = 3.1,
  GPP_daily_lower = -Inf,
  GPP_daily_sigma = 6,
  alpha_meanlog = -4.6,
  alpha_sdlog = 0.5,
  Pmax_mu = 10,
  Pmax_sigma = 7,
  ER_daily_mu = -7.1,
  ER_daily_upper = Inf,
  ER_daily_sigma = 7.1,
  K600_daily_meanlog = log(12),
  K600_daily_meanlog_meanlog = log(12),
  K600_daily_meanlog_sdlog = 1.32,
  lnK600_lnQ_intercept_mu = 2,
  lnK600_lnQ_intercept_sigma = 2.4,
  lnK600_lnQ_slope_mu = 0,
  lnK600_lnQ_slope_sigma = 0.5,
  K600_lnQ_nodes_centers = -3:3,
  K600_lnQ_nodediffs_sdlog = 0.5,
  K600_lnQ_nodes_meanlog = rep(log(12), length(K600_lnQ_nodes_centers)),
  K600_lnQ_nodes_sdlog = rep(1.32, length(K600_lnQ_nodes_centers)),
  K600_daily_sdlog = switch(mm_parse_name(model_name)$pool_K600, none = 1,
    normal_sdfixed = 0.05, NA),
  K600_daily_sigma = switch(mm_parse_name(model_name)$pool_K600, linear_sdfixed = 10,
    binned_sdfixed = 5, NA),
  K600_daily_sdlog_sigma = switch(mm_parse_name(model_name)$pool_K600, normal = 0.05,
    NA),
  K600_daily_sigma_sigma = switch(mm_parse_name(model_name)$pool_K600, linear = 1.2,
    binned = 0.24, NA),
  err_obs_iid_sigma_scale = 0.03,
  err_proc_iid_sigma_scale = 5,
  err_proc_acor_phi_alpha = 1,
  err_proc_acor_phi_beta = 1,
  err_proc_acor_sigma_scale = 1,
  err_mult_GPP_sdlog_sigma = 1,
  params_in,
  params_out,
  n_chains = 4,
  n_cores = 4,
  burnin_steps = 500,
  saved_steps = 500,
  thin_steps = 1,
  verbose = FALSE,
  weights = c("K600/CI"),
  filters = c(CI.max = NA, discharge.daily.max = NA, velocity.daily.max = NA),
  predictors = c("discharge.daily"),
  transforms = c(K600 = "log", date = NA, velocity.daily = "log", discharge.daily =
    "log"),
  other_args = c(),
  K600_lnQ_cnode_meanlog = log(6),
  K600_lnQ_cnode_sdlog = 1,
  K600_lnQ_nodediffs_meanlog = 0.2,
  lnK600_lnQ_nodes = function(K600_lnQ_nodes_centers, K600_lnQ_cnode_meanlog,
    K600_lnQ_cnode_sdlog, K600_lnQ_nodediffs_meanlog, K600_lnQ_nodediffs_sdlog, ...) {   
     sim_Kb(K600_lnQ_nodes_centers, K600_lnQ_cnode_meanlog, K600_lnQ_cnode_sdlog,
    K600_lnQ_nodediffs_meanlog, K600_lnQ_nodediffs_sdlog) },
  discharge_daily = function(n, ...) rnorm(n, 20, 3),
  DO_mod_1 = NULL,
  K600_daily = function(n, K600_daily_predlog = log(10), ...) pmax(0, rnorm(n,
    K600_daily_predlog, 4)),
  GPP_daily = function(n, ...) pmax(0, rnorm(n, 8, 4)),
  Pmax = function(n, ...) pmax(0, rnorm(n, 10, 2)),
  alpha = function(n, ...) pmax(0, rnorm(n, 1e-04, 2e-05)),
  ER_daily = function(n, ...) pmin(0, rnorm(n, -10, 5)),
  ER20 = function(n, ...) pmin(0, rnorm(n, -10, 4)),
  err_obs_sigma = 0.01,
  err_obs_phi = 0,
  err_proc_sigma = 0.2,
  err_proc_phi = 0,
  err_round = NA,
  sim_seed = NA
)

Arguments

model_name: character string identifying the model features. Use mm_name to create a valid name based on desired attributes, or mm_valid_names to see all valid names. Two alternatives to the names given by mm_valid_names() are also accepted: (1) a model type as accepted by the type argument to mm_name, which will be used to create the default model name for that model type, or (2) a full model file path for custom Bayesian models, as long as basename(model_name) can still be parsed correctly with mm_parse_name() and the file exists. In that case the file may be specified either as a file path relative to the streamMetabolizer models directory (the first assumption; this directory can be found with system.file("models", package="streamMetabolizer")) or as an absolute path or a path relative to the current working directory (the second assumption, if the first assumption turns up no files of the given name).
engine: The software or function to use in fitting the model. Should be specified via mm_name rather than here. For type='bayes', always 'stan' indicating the software package to use for the MCMC process (see http://mc-stan.org/). For types in c('mle','night','sim') there's again only one option per model (R functions; these need not be named here but will be noted in the suffix of the model name, e.g., "m_np_oi_tr_plrckm.nlm" uses nlm() for model fitting). For type='Kmodel', the name of an interpolation or regression method relating K to the predictor[s] of choice. One of c("mean", "lm", "loess").
day_start: start time (inclusive) of a day's data in number of hours from the midnight that begins the date. For example, day_start=-1.5 indicates that data describing 2006-06-26 begin at 2006-06-25 22:30, or at the first observation time that occurs after that time if day_start doesn't fall exactly on an observation time. For metabolism models working with single days of input data, it is conventional/useful to begin the day the evening before, e.g., -1.5, and to end just before the next sunrise, e.g., 30. For multiple consecutive days, it may make the most sense to start just before sunrise (e.g., 4) and to end 24 hours later. For nighttime regression, the date assigned to a chunk of data should be the date whose evening contains the data. The default is therefore 12 to 36 for metab_night, of which the times of darkness will be used.
day_end: end time (exclusive) of a day's data in number of hours from the midnight that begins the date. For example, day_end=30 indicates that data describing 2006-06-26 end at the last observation time that occurs before 2006-06-27 06:00. See day_start for recommended start and end times.
day_tests: list of tests to conduct to determine whether each date worth of data is valid for modeling. The results of these tests will be combined with the result of the test implied if required_timestep is numeric and then will be passed to model_fun as the ply_validity argument to that function.
required_timestep: NA or numeric (length 1). If numeric, the timestep length in days that a date must have to pass the validity check (to within a tolerance of 0.2% of the value of required_timestep). The result of this test will be combined with the results of the tests listed in day_tests and reported to model_fun as the ply_validity argument to that function.
init.GPP.daily: the inital value of daily mean GPP (gO2 d^-1 m^-2) to use in the NLM fitting process. See the MLE Initial Values section under Details.
init.Pmax: the initial value of Pmax (gO2 d^-1 m^-2) to use in the GPP versus light relationship in the NLM fitting process. Pmax is the maximum GPP value of the GPP-light curve. See the MLE Initial Values section under Details.
init.alpha: the inital value of alpha (gO2 s d^-1 umol^-1, i.e., units of GPP/light) to use in the GPP versus light relationship in the NLM fitting process. alpha is the initial slope of the GPP-light curve. See the MLE Initial Values section under Details.
init.ER.daily: the inital value of daily mean ER (gO2 d^-1 m^-2) to use in the NLM fitting process. See the MLE Initial Values section under Details.
init.ER20: the initial value of ER20 (gO2 d^-1 m^-2) to use in the ER versus temperature relationship in the NLM fitting process. ER20 is the respiration rate at 20 degrees C. See the MLE Initial Values section under Details.
init.K600.daily: the inital value of daily mean K600 (d^-1) to use in the NLM fitting process. Ignored if K600 is supplied in data_daily, except for those dates where K600 is NA. If there are any such dates, K600_init must have a numeric (non-NA) value, as this will be used to estimate K600 for those dates. See the MLE Initial Values section under Details.
split_dates: logical indicating whether the data should be split into daily chunks first (TRUE) or processed within one big model (FALSE). If valid days differ in their timestep length, split_dates will need to be TRUE; otherwise, FALSE is generally more efficient. FALSE is also the only appropriate solution for a hierarchical model that pools information on error, K600, etc. across days.
keep_mcmcs: TRUE, FALSE, or (for nopool models) a vector of dates (coerced with as.Date if character, etc.) indicating whether to keep all of the mcmc model objects (TRUE), none of them (FALSE), or specific dates. The default is TRUE because these objects often need inspecting.
keep_mcmc_data: FALSE, TRUE, or (for nopool models) a vector of dates (coerced with as.Date if character, etc.) indicating whether to keep all of the mcmc model objects (TRUE), none of them (FALSE), or specific dates. The default is FALSE because these objects can be very large.
GPP_daily_mu: The mean of a dnorm distribution for GPP_daily, the daily rate of gross primary production
GPP_daily_lower: The lower bound on every fitted value of GPP_daily, the daily rate of gross primary production. Use values other than -Inf with caution, recognizing that sometimes the input data are unmodelable and that a negative estimate of GPP_daily (when unconstrained) could be your only indication.
GPP_daily_sigma: The standard deviation of a dnorm distribution for GPP_daily, the daily rate of gross primary production
alpha_meanlog: The mean of a dlnorm (lognormal) distribution for alpha, the daily initial slope of the Jassby-Platt saturating curve relating GPP to light
alpha_sdlog: The standard deviation parameter of a dlnorm (lognormal) distribution for alpha, the daily initial slope of the Jassby-Platt saturating curve relating GPP to light.
Pmax_mu: The mean of a dnorm (normal) distribution for Pmax, the daily maximum GPP value of a Jassby-Platt saturating curve relating GPP to light.
Pmax_sigma: The standard deviation of a dnorm (normal) distribution for Pmax, the daily maximum GPP value of a Jassby-Platt saturating curve relating GPP to light.
ER_daily_mu: The mean of a dnorm distribution for ER_daily, the daily rate of ecosystem respiration
ER_daily_upper: The upper (less negative) bound on every fitted value of ER_daily, the daily rate of ecosystem respiration. Use values other than Inf with caution, recognizing that sometimes the input data are unmodelable and that a positive estimate of ER_daily (when unconstrained) could be your only indication.
ER_daily_sigma: The standard deviation of a dnorm distribution for ER_daily, the daily rate of ecosystem respiration
K600_daily_meanlog: Applies when pool_K600 is 'none'. The mean of a dlnorm distribution for K600_daily, the daily rate of reaeration
K600_daily_meanlog_meanlog: hyperparameter for pool_K600='normal'. The mean parameter (meanlog_meanlog) of a lognormal distribution of meanlog in K ~ lN(meanlog, sdlog), meanlog ~ lN(meanlog_meanlog, meanlog_sdlog)
K600_daily_meanlog_sdlog: hyperparameter for pool_K600='normal'. The standard deviation parameter (meanlog_sdlog) of a lognormal distribution of meanlog in K ~ lN(meanlog, sdlog), meanlog ~ lN(meanlog_meanlog, meanlog_sdlog)
lnK600_lnQ_intercept_mu: hyperparameter for pool_K600 == 'linear'. The mean of the prior distribution for the intercept parameter in log(K600) ~ lnK600_lnQ_intercept + lnK600_lnQ_slope*log(Q)
lnK600_lnQ_intercept_sigma: hyperparameter for pool_K600 == 'linear'. The standard deviation of the prior distribution for the intercept parameter in log(K600) ~ lnK600_lnQ_intercept + lnK600_lnQ_slope*log(Q)
lnK600_lnQ_slope_mu: hyperparameter for pool_K600='linear'. The mean of the prior distribution for the slope parameter in log(K600) ~ lnK600_lnQ_intercept + lnK600_lnQ_slope*log(Q)
lnK600_lnQ_slope_sigma: hyperparameter for pool_K600='linear'. The standard deviation of the prior distribution for the slope parameter in log(K600) ~ lnK600_lnQ_intercept + lnK600_lnQ_slope*log(Q)
K600_lnQ_nodes_centers: data configuration argument for pool_K600='binned'. numeric vector giving the natural-log-space centers of the discharge bins. See also calc_bins
K600_lnQ_nodediffs_sdlog: hyperparameter for pool_K600='binned'. The standard deviations of the differences in estimated K600 between successive lnQ_nodes (bins), where the means of those differences are always zero
K600_lnQ_nodes_meanlog: hyperparameter for pool_K600='binned'. The means of lognormal prior distributions for the K600_lnQ_nodes parameters.
K600_lnQ_nodes_sdlog: hyperparameter for pool_K600='binned'. The standard deviations of lognormal prior distributions for the K600_lnQ_nodes parameters.
K600_daily_sdlog: The lognormal scale parameter (standard deviation) of a dlnorm distribution having meanlog equal to K600_daily_meanlog (when pool_K600 is 'none') or K600_daily_predlog (when pool_K600 is 'normal_sdfixed') for K600_daily, the daily rate of reaeration as corrected for temperature and the diffusivity of oxygen
K600_daily_sigma: The standard deviation of a dnorm distribution having mean equal to exp(K600_daily_predlog) (applicable when pool_K600 is 'linear_sdfixed' or 'binned_sdfixed') for K600_daily, the daily rate of reaeration as corrected for temperature and the diffusivity of oxygen
K600_daily_sdlog_sigma: hyperparameter for pool_K600 in c('normal'). The scale (= sigma) parameter of a half-normal distribution of sdlog in K ~ lN(meanlog, sdlog), sdlog ~ halfnormal(0, sigma=sdlog_sigma). Visualize the PDF of K600_daily_sdlog with plot_distribs.
K600_daily_sigma_sigma: hyperparameter for pool_K600 in c('linear','binned'). The scale (= sigma) parameter of a half-normal distribution of sigma in K ~ lN(meanlog, sigma), sigma ~ halfnormal(0, sigma=sigma_sigma). Visualize the PDF of K600_daily_sdlog with plot_distribs.
err_obs_iid_sigma_scale: The scale (= sigma) parameter of a half-Cauchy distribution for err_obs_iid_sigma, the standard deviation of the observation error. Visualize the PDF of err_obs_iid_sigma with plot_distribs.
err_proc_iid_sigma_scale: The scale (= sigma) parameter of a half-Cauchy distribution for err_proc_iid_sigma, the standard deviation of the uncorrelated (IID) component of process [& sometimes observation] error. Visualize the PDF of err_proc_iid_sigma with plot_distribs.
err_proc_acor_phi_alpha: The alpha (= shape1) parameter on a beta distribution for err_proc_acor_phi, the autocorrelation coefficient for the autocorrelated component of process [& sometimes observation] error. Visualize the PDF of err_proc_acor_phi with plot_distribs.
err_proc_acor_phi_beta: The beta (= shape2) parameter on a beta distribution for err_proc_acor_phi, the autocorrelation coefficient for the autocorrelated component of process [& sometimes observation] error. Visualize the PDF of err_proc_acor_phi with plot_distribs.
err_proc_acor_sigma_scale: The scale (= sigma) parameter of a half-Cauchy distribution for err_proc_acor_sigma, the standard deviation of the autocorrelated component of process [& sometimes observation] error. Visualize the PDF of err_proc_acor_sigma with plot_distribs.
err_mult_GPP_sdlog_sigma: The scale parameter of a half-normal distribution for err_mult_GPP_sdlog, the scale parameter of the lognormal distribution of err_mult_GPP. err_mult_GPP is multiplied by light and then normalized to a daily mean of 1 before being multiplied by GPP_daily to estimate GPP_inst. The effect is a special kind of process error that is proportional to light (with noise) and is applied to GPP rather than to dDO/dt.
params_in: Character vector of hyperparameters to pass from the specs list into the data list for the MCMC run. Will be automatically generated during the specs() call; need only be revised if you're using a custom model that requires different hyperparameters.
params_out: a character vector of parameters whose values in the MCMC runs should be recorded and summarized
n_chains: the number of chains to run
n_cores: the number of cores to apply to this run
burnin_steps: the number of steps per chain to run and ignore before starting to collect MCMC 'data'
saved_steps: the number of MCMC steps per chain to save
thin_steps: the number of steps to move before saving another step. 1 means save all steps.
verbose: logical. give status messages?
weights: For Kmodel, character vector indicating the type of weighting to use. Set to c() for no weights. One of c("1/CI", "K600/CI", c()).
filters: For Kmodel, named numeric vector of limits to use in filtering data_daily. Elements may include c("CI.max","discharge.daily.max","velocity.daily.max"). If an element is given, the corresponding filter is applied: K600.daily.upper-K600.daily.lower <= CI.max, discharge.daily <= discharge.daily.max, velocity.daily <= velocity.daily.max
predictors: For Kmodel, character vector of variables (column names in data or data_daily) to use in predicting K. Leave blank or set to c() for no predictors. Otherwise, one or more of these may be included: c("date", "velocity.daily", "discharge.daily").
transforms: For Kmodel, a named character vector of names of functions (probably 'log' or NA) to apply to K600.daily and/or the predictors. K600.daily should probably be logged. The vector names must match the values of predictors, although not all elements of predictors must be included in transforms. Recommended transforms include c(K600.daily='log', date=NA, velocity.daily="log", discharge.daily="log")
other_args: Other arguments passed to the fitting function given by specs$engine. na.rm=TRUE is already passed to mean (which is actually implemented as sum, anyway).
K600_lnQ_cnode_meanlog: For a sim model with pool_K600='binned'. The mean of a lognormal distribution describing the y=K600 value of the middle (or just past middle) node in the piecewise lnK ~ lnQ relationship
K600_lnQ_cnode_sdlog: For a sim model with pool_K600='binned'. The sd of a lognormal distribution describing the y=K600 value of the middle (or just past middle) node in the piecewise lnK ~ lnQ relationship
K600_lnQ_nodediffs_meanlog: For a sim model with pool_K600='binned'. The average (in log space) difference between ln(K) values of successive nodes. A non-zero value introduces a trend in K ~ Q.
lnK600_lnQ_nodes: For a sim model with pool_K600='binned'. The values of lnK600 at each node. The default value of this spec is a function that computes lnK600s based on simulated K~Q relationships.
discharge_daily: Daily values, or a function to generate daily values, of mean daily discharge in m^3 s^-1. Fixed values may alternatively be specified as discharge.daily in the data_daily passed to metab.
DO_mod_1: Daily values, or a function to generate daily values, of the first DO.mod value on each date. Fixed values may alternatively be specified as DO.mod.1 in the data_daily passed to metab. Or may be implied by a DO.obs column in data, from which the first values on each date will be extracted by metab().
K600_daily: Daily values, or a function to generate daily values, of the reaeration rate constant K600. Fixed values may alternatively be specified as K600.daily in the data_daily passed to metab.
GPP_daily: Daily values, or a function to generate daily values, of the photosynthesis parameter GPP_daily. Fixed values may alternatively be specified as GPP.daily in the data_daily passed to metab.
Pmax: Daily values, or a function to generate daily values, of the photosynthesis parameter Pmax. Fixed values may alternatively be specified as Pmax in the data_daily passed to metab.
alpha: Daily values, or a function to generate daily values, of the photosynthesis parameter alpha. Fixed values may alternatively be specified as alpha in the data_daily passed to metab.
ER_daily: Daily values, or a function to generate daily values, of the respiration parameter ER_daily. Fixed values may alternatively be specified as ER.daily in the data_daily passed to metab.
ER20: Daily values, or a function to generate daily values, of the respiration parameter ER20. Fixed values may alternatively be specified as ER20 in the data_daily passed to metab.
err_obs_sigma: Daily values, or a function to generate daily values, of the sd of observation error, or 0 for no observation error. Observation errors are those applied to DO.mod after generating the full time series of modeled values.
err_obs_phi: Daily values, or a function to generate daily values, of the autocorrelation coefficient of the observation errors, or 0 for uncorrelated errors.
err_proc_sigma: Daily values, or a function to generate daily values, of the sd of process error, or 0 for no process error. Process errors are applied at each time step, and therefore propagate into the next timestep.
err_proc_phi: Daily values, or a function to generate daily values, of the autocorrelation coefficient of the process errors, or 0 for uncorrelated errors.
err_round: A single value indicating whether simulated DO.obs should be rounded to simulate the common practice of only reporting a few significant figures for DO. Use NA for no effect, or an integer as in the digits argument to round if simulated DO.obs should be rounded to the given number of digits beyond ..
sim_seed: NA to specify that each call to predict_DO should generate new values, or an integer, as in the seed argument to set.seed, specifying the seed to set before every execution of predict_DO and/or predict_metab.

Value

an internally consistent list of arguments that may be passed to

metab as the specs argument

Relevant arguments

* metab_bayes: Always relevant: model_name, engine, split_dates, keep_mcmcs, keep_mcmc_data, day_start, day_end, day_tests, ER_daily_mu, ER_daily_sigma, params_in, params_out, n_chains, n_cores, burnin_steps, saved_steps, thin_steps, verbose. The need for other arguments depends on features of the model structure, as from mm_parse_name(model_name):

If GPP_fun=='linlight' then GPP_daily_mu, GPP_daily_sigma, while if GPP_fun=='satlight' then alpha_meanlog, alpha_sdlog, Pmax_mu, Pmax_sigma.
If pool_K600=='none' then K600_daily_meanlog, K600_daily_sdlog.
If pool_K600=='normal' then K600_daily_meanlog_meanlog, K600_daily_meanlog_sdlog, K600_daily_sdlog_sigma.
If pool_K600=='linear' then lnK600_lnQ_intercept_mu, lnK600_lnQ_intercept_sigma, lnK600_lnQ_slope_mu, lnK600_lnQ_slope_sigma, K600_daily_sigma_sigma.
If pool_K600=='binned' then K600_lnQ_nodes_centers, K600_lnQ_nodediffs_sdlog, K600_lnQ_nodes_meanlog, K600_lnQ_nodes_sdlog, K600_daily_sigma_sigma.
If err_obs_iid then err_obs_iid_sigma_scale.
If err_proc_acor then err_proc_acor_phi_alpha, err_proc_acor_phi_beta, err_proc_acor_sigma_scale.
If err_proc_iid then err_proc_iid_sigma_scale.
If err_proc_GPP then err_mult_GPP_sdlog_sigma.

* metab_mle: model_name, day_start, day_end, day_tests, init.GPP.daily, init.Pmax, init.alpha, init.ER.daily, init.ER20, init.K600.daily

* metab_night: model_name, day_start, day_end, day_tests

* metab_Kmodel: model_name, engine, day_start, day_end, day_tests, weights, filters, predictors, transforms, other_args. Note that the defaults for weights, predictors, filters, and transforms are adjusted according to the engine implied by model_name.

* metab_sim: model_name, day_start, day_end, day_tests, err_obs_sigma, err_obs_phi, err_proc_sigma, err_proc_phi, sim_seed. Those arguments whose period-separated name occurs in the default data_daily argument to metab(sim) can be specified here as NULL, numeric, or a function to be called each time predict_DO or predict_metab is called on the model. If given as a function, an argument will be called with any already-evaluated parameters (including the contents of data_daily and n, the number of dates) passed in as arguments; for example, K600_daily can see n, discharge.daily, and GPP_daily can see n, discharge.daily, and K600.daily.

MLE Initial Values

For metab_mle models (maximum likelihood estimation), specification arguments whose names begin with init are applicable. Which arguments are required depends on the value of model_name and can be determined by calling grep('^init.', names(specs(mname)), value=TRUE) once for your model name mname before supplying any arguments.

Examples

specs(mm_name(type='mle', err_obs_iid=FALSE, err_proc_iid=TRUE))
#> Model specifications:
#>   model_name        m_np_pi_tr_plrckm.nlm                                       
#>   day_start         4                                                           
#>   day_end           28                                                          
#>   day_tests         full_day, even_timesteps, complete_data, pos_discharge, p...
#>   required_timestep NA                                                          
#>   init.GPP.daily    8                                                           
#>   init.ER.daily     -10                                                         
#>   init.K600.daily   10                                                          
specs(mm_name(type='bayes', pool_K600='normal'))
#> Model specifications:
#>   model_name                 b_Kn_oipi_tr_plrckm.stan                           
#>   engine                     stan                                               
#>   split_dates                FALSE                                              
#>   keep_mcmcs                 TRUE                                               
#>   keep_mcmc_data             TRUE                                               
#>   day_start                  4                                                  
#>   day_end                    28                                                 
#>   day_tests                  full_day, even_timesteps, complete_data, pos_dis...
#>   required_timestep          NA                                                 
#>   GPP_daily_mu               3.1                                                
#>   GPP_daily_lower            -Inf                                               
#>   GPP_daily_sigma            6                                                  
#>   ER_daily_mu                -7.1                                               
#>   ER_daily_upper             Inf                                                
#>   ER_daily_sigma             7.1                                                
#>   K600_daily_meanlog_meanlog 2.484906649788                                     
#>   K600_daily_meanlog_sdlog   1.32                                               
#>   K600_daily_sdlog_sigma     0.05                                               
#>   err_obs_iid_sigma_scale    0.03                                               
#>   err_proc_iid_sigma_scale   5                                                  
#>   params_in                  GPP_daily_mu, GPP_daily_lower, GPP_daily_sigma, ...
#>   params_out                 GPP, ER, DO_R2, GPP_daily, ER_daily, K600_daily,...
#>   n_chains                   4                                                  
#>   n_cores                    4                                                  
#>   burnin_steps               500                                                
#>   saved_steps                500                                                
#>   thin_steps                 1                                                  
#>   verbose                    FALSE