fastret.workflow: Retention time prediction workflow

In ChristianAmes/FastRet: Retention time prediction and subset selection with a GUI

Retention time prediction workflow

Description

Whole retention time prediction workflow. Function creates predictor set with RCDK based on SMILES. Trains a chosen predcition model and validates the approach with a cross validation.

Usage

fastret.workflow(
  data,
  method = "glmnet",
  verbose = FALSE,
  data_set_name = "data set",
  final_model = T,
  preprocessed = F,
  interaction_terms = F,
  nfolds = 2,
  include_polynomial = F,
  degree_polynomial = 2,
  scale = T
)

Arguments

data	data.frame with columns NAME, RT, SMILES
method	prediction algorithm, either glmnet or xgboost
verbose	additional print outputs to user if TRUE
data_set_name	name of dataset will appear on validation plot
final_model	TRUE if final model trained on whole dataset should be returned
preprocessed	TRUE if data is already preprocessed and descriptor varialbes are already added
interaction_terms	TRUE if interaction terms between all variables should be added
nfolds	number of folds for cross validation
include_polynomial	TRUE if polynomial terms should be added to descriptor set
degree_polynomial	specifies degree up until which polynomials will be added if include_polynomials == TRUE
scale	if TRUE, all variables will be centered to a mean of 0 and scaled to a standard deviation of 1

ChristianAmes/FastRet documentation built on June 17, 2022, 6 a.m.