dg_prepare_model: Prepare parameters for a specific dG model

In jschmiedel/tempura: Fit free energies of folding and binding from doubledeepPCA data

Description

Prepare parameters for a specific dG model

Usage

dg_prepare_model(
  dataset_folder,
  model_name,
  no_folded_states = 1,
  fix_f_dgwt = FALSE,
  fix_b_dgwt = FALSE,
  dg_extremes = 15,
  fixed_par = c(),
  lambda = 0.1
)

Arguments

dataset_folder	absolute path to the dataset folder, is created if non-existent
model_name	name of the model that should be computed on the dataset
no_folded_states	integer, '1': one folded, binding-competent state, '2': binding-incompetent and binding-competent folded states, default = 1
fix_f_dgwt	logical, if TRUE, only one folded dgwt value exists across datasets and assays, if FALSE (default) each dataset and assay has their independent dgwt values for folding
fix_b_dgwt	logical, if TRUE, only one binding dgwt value exists across binding datasets, if FALSE (default) each binding dataset has their independent dgwt values for binding
dg_extremes	float, lower and upper bounds for dG parameters, default = 15
fixed_par	list of parameters that are being fixed at their starting values for model fitting
lambda	regularization parameter

Value

writes a .RData file to $dataset_folder/$model_name/parameter_list.RData containing the parlist list with all necessary parameters to compute a specific dG model

jschmiedel/tempura documentation built on Nov. 13, 2020, 3:53 a.m.