Fit an n2kModel object — fit_model • n2kanalysis

Fit an n2kModel object

Usage

fit_model(x, ...)

# S4 method for class 'character'
fit_model(
  x,
  base,
  project,
  status = c("new", "waiting"),
  verbose = TRUE,
  ...,
  bucket
)

# S4 method for class 'n2kAggregate'
fit_model(x, ...)

# S4 method for class 'n2kComposite'
fit_model(x, base, project, status = "new", ...)

# S4 method for class 'n2kHurdleImputed'
fit_model(x, base, project, status = c("new", "waiting"), ...)

# S4 method for class 'n2kInla'
fit_model(
  x,
  status = "new",
  ...,
  timeout = NULL,
  seed = get_seed(x),
  num_threads = NULL,
  parallel_configs = TRUE
)

# S4 method for class 'n2kInlaComparison'
fit_model(x, base, project, status = "new", verbose = TRUE, ...)

# S4 method for class 'n2kManifest'
fit_model(
  x,
  base,
  project,
  status = c("new", "waiting"),
  verbose = TRUE,
  ...,
  local = NULL
)

# S4 method for class 'n2kModelImputed'
fit_model(x, ...)

# S4 method for class 'n2kSpde'
fit_model(
  x,
  status = "new",
  ...,
  timeout = NULL,
  seed = get_seed(x),
  num_threads = NULL,
  parallel_configs = TRUE
)

# S4 method for class 's3_object'
fit_model(x, status = c("new", "waiting"), ...)

Arguments

x: the n2kModel
...: other arguments. See details
base: The root of a project. Can be either a directory on a file system or an AWS S3 bucket object. Extracted from bucket or x when missing.
project: The subdirectory of the project. Is relative the base. Extracted from x when missing.
status: A vector with status levels naming the levels which should be calculated. Defaults to "new".
verbose: A logical indicating if the function should display the name of the file and the status. Defaults to TRUE.
bucket: The name of the AWS S3 bucket. Only used when base is missing.
timeout: the optional number of second until the model will time out
seed: See the same argument in INLA::inla.qsample() for further information. In order to produce reproducible results, you ALSO need to make sure the RNG in R is in the same state, see the example in INLA::inla.posterior.sample(). When seed is non-zero, num_threads is forced to "1:1" and parallel_configs is set to FALSE, since parallel sampling would not produce a reproducible sequence of pseudo-random numbers.
num_threads: The number of threads to use in the format "A:B" defining the number threads in the outer (A) and inner (B) layer for nested parallelism. A "0" will be replaced intelligently. seed != 0 requires serial computations.
parallel_configs: Logical. If TRUE and not on Windows, then try to run each configuration in parallel (not Windows) using A threads (see num_threads), where each of them is using B:0 threads.
local: A local folder into which objects from an AWS S3 bucket are downloaded.

Details

status: A vector with status levels naming the levels which should be recalculated. Defaults to c("new", "waiting").
verbose: A logical indicating if the function should display the name of the file and the status. Defaults to TRUE.