fit_to_constraints {autohrf}

fit_to_constraints

Description

A helper function for fitting a model to constraints.

Usage

fit_to_constraints(
  model_id,
  d,
  model_constraints,
  tr,
  roi_weights,
  allow_overlap,
  population,
  iter,
  mutation_rate,
  mutation_factor,
  elitism,
  hrf,
  t,
  p_boynton,
  p_spm,
  f,
  autohrf = NULL,
  verbose = TRUE
)

Arguments

model_id

ID of the model.

d

A dataframe with the signal data: roi, t and y. ROI is the name of the region, t is the timestamp and y the value of the signal.

model_constraints

A list of model specifications to use for fitting. Each specification is represented as a data frame containing information about it (event, start_time, end_time, min_duration and max_duration).

tr

MRI's repetition time.

roi_weights

A data frame with ROI weights: roi, weight. ROI is the name of the region, weight a number that defines the importance of that roi, the default weight for a ROI is 1. If set to 2 for a particular ROI that ROI will be twice as important.

allow_overlap

Whether to allow overlap between events.

population

The size of the population in the genetic algorithm.

iter

Number of iterations in the genetic algorithm.

mutation_rate

The mutation rate in the genetic algorithm.

mutation_factor

The mutation factor in the genetic algorithm.

elitism

The degree of elitism (promote a percentage of the best solutions) in the genetic algorithm.

hrf

Method to use for HRF generation.

t

The t parameter for Boynton or SPM HRF generation.

p_boynton

Parameters for the Boynton's HRF.

p_spm

Parameters for the SPM HRF.

f

Upsampling factor.

autohrf

Results of a previous autohrf run to continue.

verbose

Whether to print progress of the fitting process.

Value

Returns the best model given provided constraints.