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Adjust an existing FastRet model for use with a new column

Source: R/train.R
adjust_frm.Rd

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 adjustment model to an existing FastRet model.

Usage

adjust_frm(
  frm,
  new_data,
  predictors = 1:6,
  nfolds = 5,
  verbose = 1,
  seed = NULL,
  do_cv = TRUE,
  adj_type = "lm",
  add_cds = NULL
)

Arguments

frm

An object of class frm as returned by train_frm().

new_data

Data frame with required columns "RT", "NAME", "SMILES"; optional "INCHIKEY". "RT" must be the retention time measured on the adjusted column. Each row must match at least one row in frm$df. The exact matching behavior is described in 'Details'.

predictors

Numeric vector specifying which transformations to include in the model. Available options are: 1=RT, 2=RT^2, 3=RT^3, 4=log(RT), 5=exp(RT), 6=sqrt(RT). Note that predictor 1 (RT) is always included, even if not specified explicitly.

nfolds

The number of folds for cross validation.

verbose

Show progress messages?

seed

An integer value to set the seed for random number generation to allow for reproducible results.

do_cv

A logical value indicating whether to perform cross-validation. If FALSE, the cv element in the returned adjustment object will be NULL.

adj_type

A string representing the adjustment model type. Either "lm", "lasso", "ridge", or "gbtree".

add_cds

A logical value indicating whether to add chemical descriptors as predictors to new data. Default is TRUE if adj_type is "lasso", "ridge" or "gbtree" and FALSE if adj_type is "lm".

Value

An object of class frm, as returned by train_frm(), but with an additional element adj containing the adjustment model. Components of adj are:

  • model: The fitted adjustment model. Class depends on adj_type and is one of lm, glmnet, or xgb.Booster.

  • df: The data frame used for training the adjustment model. Including columns "NAME", "SMILES", "RT", "RT_ADJ" and optionally "INCHIKEY", as well as any additional predictors specified via the predictors argument.

  • cv: A named list containing the cross validation results (see 'Details'), or NULL if do_cv = FALSE. When not NULL, elements are:

    • folds: A list of integer vectors specifying the samples in each fold.

    • models: A list of adjustment models trained on each fold.

    • stats: A list of vectors with RMSE, Rsquared, MAE, pBelow1Min per fold. Added with v1.3.0.

    • preds: Retention time predictions obtained during CV by applying the adjustment model to the hold-out data.

    • preds_adjonly: Removed (i.e. NULL) since v1.3.0.

  • args: Function arguments used for adjustment (excluding frm, new_data and verbose). Added with v1.3.0.

  • version: The version of the FastRet package used to train the adjustment model. Added with v1.3.0.

Details

Matching is done via "SMILES"+"INCHIKEY" if both datasets have non-missing INCHIKEYs for all rows; otherwise via "SMILES"+"NAME". If multiple rows in frm$df match the same row in new_data, their RT values are averaged first, and this average is used for training the adjustment model.

Example: if frm$df equals data.frame OLD shown below and new_data equals data.frame NEW, then the resulting, paired data.frame will look like PAIRED.

OLD <- data.frame(
    NAME   = c("A", "B",  "B",  "C"  ),
    SMILES = c("C", "CC", "CC", "CCC"),
    RT     = c(5.0,  8.0,  8.2,  9.0 )
)
NEW <- data.frame(
    NAME   = c("A", "B",  "B",  "B"),
    SMILES = c("C", "CC", "CC", "CC"),
    RT     = c(2.5,  5.5,  5.7,  5.6)
)
PAIRED <- data.frame(
    NAME   = c("A", "B",  "B",  "B"),
    SMILES = c("C", "CC", "CC", "CC"),
    RT     = c(5.0,  8.1,  8.1,  8.1), # Average of OLD$RT[2:3]
    RT_ADJ = c(2.5,  5.5,  5.7,  5.6)  # Taken from NEW
)

If do_cv is TRUE, the adjustment procedure is evaluated in cross-validation. However, care must be taken when interpreting the CV results, as the model performance depends on both the adjustment layer and the original model, which was trained on the full base dataset. Therefore, the observed CV metrics should be read as "expected performance when predicting RTs for molecules that were part of the base-model training but not part of the adjustment set" instead of "expected performance when predicting RTs for completely new molecules".

Examples

frm <- read_rp_lasso_model_rds()
new_data <- read_rpadj_xlsx()
frm_adj <- adjust_frm(frm, new_data, verbose = 0)