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Generic Method for fitting model

Source: R/fit_model.R
fit_model.Rd

S3 generic for fitted models from constructed model object

Usage

fit_model(
  constructed_model,
  n_chain = 4,
  n_iter = 2000,
  n_warmup = floor(n_iter/2),
  thin = 1,
  adapt_delta = 0.9,
  multi_cores = TRUE,
  verbose = TRUE,
  suppress_warnings = FALSE,
  seed = NULL,
  ...
)

# S3 method for class 'rw_subtyped'
fit_model(
  constructed_model,
  n_chain = 4,
  n_iter = 2000,
  n_warmup = floor(n_iter/2),
  thin = 1,
  adapt_delta = 0.9,
  multi_cores = TRUE,
  verbose = TRUE,
  suppress_warnings = FALSE,
  seed = NULL,
  ...
)

# S3 method for class 'ps_subtyped'
fit_model(
  constructed_model,
  n_chain = 4,
  n_iter = 2000,
  n_warmup = floor(n_iter/2),
  thin = 1,
  adapt_delta = 0.9,
  multi_cores = TRUE,
  verbose = TRUE,
  suppress_warnings = FALSE,
  seed = NULL,
  ...
)

# S3 method for class 'rw_multiple'
fit_model(
  constructed_model,
  n_chain = 4,
  n_iter = 2000,
  n_warmup = floor(n_iter/2),
  thin = 1,
  adapt_delta = 0.9,
  multi_cores = TRUE,
  verbose = TRUE,
  suppress_warnings = FALSE,
  seed = NULL,
  ...
)

# S3 method for class 'ps_multiple'
fit_model(
  constructed_model,
  n_chain = 4,
  n_iter = 2000,
  n_warmup = floor(n_iter/2),
  thin = 1,
  adapt_delta = 0.9,
  multi_cores = TRUE,
  verbose = TRUE,
  suppress_warnings = FALSE,
  seed = NULL,
  ...
)

# S3 method for class 'rw_single'
fit_model(
  constructed_model,
  n_chain = 4,
  n_iter = 2000,
  n_warmup = floor(n_iter/2),
  thin = 1,
  adapt_delta = 0.9,
  multi_cores = TRUE,
  verbose = TRUE,
  suppress_warnings = FALSE,
  seed = NULL,
  ...
)

# S3 method for class 'ps_single'
fit_model(
  constructed_model,
  n_chain = 4,
  n_iter = 2000,
  n_warmup = floor(n_iter/2),
  thin = 1,
  adapt_delta = 0.9,
  multi_cores = TRUE,
  verbose = TRUE,
  suppress_warnings = FALSE,
  seed = NULL,
  ...
)

Arguments

constructed_model

prepared model object of class EpiStrainDynamics.model

n_chain

number of MCMC chains, defaults to 4

n_iter

A positive integer specifying the number of iterations for each chain, default value is 2000

n_warmup

A positive integer specifying the number of warmup iterations,default value is half the number of iterations

thin

A positive integer specifying the period for saving samples, default value is 1.

adapt_delta

Numeric value between 0 and 1 indicating target average acceptance probability used in rstan::sampling. Default value is 0.9.

multi_cores

A logical value indicating whether to parallelize chains with multiple cores, default is TRUE and uses all available cores - 1.

verbose

Logical value controlling the verbosity of output. When TRUE (default), shows all messages, warnings, errors, and progress indicators. When FALSE, suppresses messages and progress while retaining warnings and errors.

suppress_warnings

Logical value indicating whether to suppress warnings from Stan. Default is FALSE. When TRUE, warnings are suppressed but errors are still raised.

seed

A positive integer seed used for random number generation in MCMC. Default is NULL, which means the seed is generated from 1 to the maximum integer supported by R.

...

additional arguments to rstan::sampling(), such as init

Value

fit model of class EpiStrainDynamics.fit, or if fitting fails, an error is raised that can be caught and inspected. Its fit element is the stanfit object, whose main parameters are:

  • log_incidence: the latent log-incidence trend, indexed [time] for single pathogen models and [pathogen, time] otherwise. In p-spline models it is derived from b_spline_coef.

  • b_spline_coef: the B-spline coefficients (p-spline models only).

  • noisy_incidence: per-pathogen incidence with gamma noise around exp(log_incidence), and eta, the shared inverse dispersion of that noise (only when pathogen_noise = TRUE).

  • phi: the inverse dispersion of the negative binomial observation model.

  • tau or Sigma: the smoothing standard deviation(s) or covariance matrix, depending on the smoothing structure.

  • day_of_week_simplex: the day-of-week effect (only when dow_effect = TRUE).

Examples

if (FALSE) { # interactive()
mod <- construct_model(
  pathogen_structure = single(
    case_timeseries = sarscov2$cases,
    time = sarscov2$date
  ),
  method = random_walk()
)

fit <- fit_model(mod)

# Suppress progress and messages but keep warnings/errors
fit <- fit_model(mod, verbose = FALSE)

# Suppress warnings too
fit <- fit_model(mod, verbose = FALSE, suppress_warnings = TRUE)

# Catch errors and inspect
result <- tryCatch(
  fit_model(mod),
  error = function(e) e
)
if (inherits(result, "EpiStrainDynamics.fit.error")) {
  cat("Fitting failed:", result$message, "\n")
  # Can still access the model: result$constructed_model
}
}