The goal of this function is to train a model that predicts RT_ADJ (retention time measured on a new, adjusted column) from RT (retention time measured on the original column) and to attach this "adjustmodel" to an existing FastRet model.
Usage
adjust_frm(
frm = train_frm(),
new_data = read_rpadj_xlsx(),
predictors = 1:6,
nfolds = 5,
verbose = 1
)
Arguments
- frm
An object of class
frm
as returned bytrain_frm()
.- new_data
Dataframe with columns "RT", "NAME", "SMILES" and optionally a set of chemical descriptors.
- predictors
Numeric vector specifying which predictors to include in the model in addition to RT. Available options are: 1=RT, 2=RT^2, 3=RT^3, 4=log(RT), 5=exp(RT), 6=sqrt(RT).
- nfolds
An integer representing the number of folds for cross validation.
- verbose
A logical value indicating whether to print progress messages.
Value
An object of class frm
, which is a list with the following elements:
model
: A list containing details about the original model.df
: The data frame used for training the model.cv
: A list containing the cross validation results.seed
: The seed used for random number generation.version
: The version of the FastRet package used to train the model.adj
: A list containing details about the adjusted model.
Examples
# \donttest{
frm <- read_rp_lasso_model_rds()
new_data <- read_rpadj_xlsx()
frmAdjusted <- adjust_frm(frm, new_data, verbose = 0)
# }