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Preprocess data

Source: R/prepro.R
preprocess_data.Rd

Preprocess data so they can be used as input for train_frm().

Usage

preprocess_data(
  data,
  degree_polynomial = 1,
  interaction_terms = FALSE,
  verbose = 1,
  nw = 1,
  rm_near_zero_var = TRUE,
  rm_na = TRUE,
  add_cds = TRUE,
  rm_ucs = TRUE,
  rt_terms = 1,
  mandatory = c("NAME", "RT", "SMILES")
)

Arguments

data

Dataframe with following columns:

  • Mandatory: NAME, RT and SMILES.

  • Recommmended: INCHIKEY.

  • Optional: Any of the chemical descriptors listed in CDFeatures. All other columns will be removed. See 'Details'.

degree_polynomial

Add predictors with polynomial terms up to the specified degree, e.g. 2 means "add squares", 3 means "add squares and cubes". Set to 1 to leave descriptors unchanged.

interaction_terms

Add interaction terms? Polynomial terms are not included in the generation of interaction terms.

verbose

0: no output, 1: show progress, 2: progress and warnings.

nw

Number of workers to use for parallel processing.

rm_near_zero_var

Remove near zero variance predictors?

rm_na

Remove NA values?

add_cds

Add chemical descriptors using getCDs()? See 'Details'.

rm_ucs

Remove unsupported columns?

rt_terms

Which retention-time transformations to append as extra predictors. Supply a numeric vector referencing predefined rt_terms (1=RT, 2=I(RT^2), 3=I(RT^3), 4=log(RT), 5=exp(RT), 6=sqrt(RT)) or a character vector with the explicit transformation terms. Character values are passed to model.frame(), so they must use valid formula syntax (e.g. "I(RT^2)" rather than "RT^2").

mandatory

Character vector of mandatory columns that must be present in data. If any of these columns are missing, an error is raised.

Value

A dataframe with the preprocessed data.

Details

If add_cds = TRUE, chemical descriptors are added using getCDs(). If all chemical descriptors listed in CDFeatures are already present in the input data object, getCDs() will leave them unchanged. If one or more chemical descriptors are missing, all chemical descriptors will be recalculated and existing ones will be overwritten.

Examples

data <- head(RP, 3)
pre <- preprocess_data(data, verbose = 0)