Imputation: Data imputation

In Waddlessss/ABCstats: Adaptive Box-Cox Transformation for Improving Data Normality

Data imputation

Description

Missing value imputation by different methods.

Usage

Imputation(
  FeatureTable,
  Impt = "default",
  GapIdentifier = 0,
  SampleInCol = TRUE,
  Output = FALSE
)

Arguments

FeatureTable	Data frame with features in row and samples in column (default).
Impt	A single string specifying the imputation method to be used.
GapIdentifier	A numeric vector indicating the optimization range of lambda value.
SampleInCol	TRUE if samples are in column. FALSE if samples are in row.
Output	TRUE will output the result table in the current working directory.

Details

Four imputation methods are provided here:
1. default, default imputation method by ABC transformation
2. knn, KNN method supported by VIM package. See kNN for details.
3. addition, only replace gaps with identical small values (min/5).
4. rf, random forest method supported by mice package. See mice for details. This method is not recommended due to long calculation time when feature number > 200.

FeatureTable contains measured signal intensities of metabolic features, with features in row and samples in column (default). The column names should be sample names, and the first row should be sample group names (e.g. control, case).

Value

This function returns the imputed data frame.

References

To be updated.

Examples

ImputedTable = Imputation(DemoData)

Waddlessss/ABCstats documentation built on May 15, 2024, 10:48 a.m.