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Imputation by the k-nearest neighbors algorithm

Source: R/imputations.R
impute.knn.Rd

Apply imputation to the dataset by the k-nearest neighbors algorithm (Troyanskaya et al. 2001) .

Usage

impute.knn(
  dataSet,
  reportImputing = FALSE,
  k = 10,
  rowmax = 0.5,
  colmax = 0.8,
  maxp = 1500,
  seed = 362436069
)

Arguments

dataSet

The 2d dataset of experimental values.

reportImputing

A boolean (default = FALSE) specifying whether to provide a shadow data frame with imputed data labels, where 1 indicates the corresponding entries have been imputed, and 0 indicates otherwise. Alters the return structure.

k

An integer (default = 10) indicating the number of neighbors to be used in the imputation.

rowmax

A scalar (default = 0.5) specifying the maximum percent missing data allowed in any row. For any rows with more than rowmax*100% missing are imputed using the overall mean per sample.

colmax

A scalar (default = 0.8) specifying the maximum percent missing data allowed in any column. If any column has more than colmax*100% missing data, the program halts and reports an error.

maxp

An integer (default = 1500) indicating the largest block of proteins imputed using the k-nearest neighbors algorithm. Larger blocks are divided by two-means clustering (recursively) prior to imputation.

seed

An integer (default = 362436069) specifying the seed used for the random number generator for reproducibility.

Value

  • If reportImputing = FALSE, the function returns the imputed 2d dataframe.

  • If reportImputing = TRUE, the function returns a list of the imputed 2d dataframe and a shadow matrix showing which proteins by replicate were imputed.

References

Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB (2001). “Missing Value Estimation Methods for DNA Microarrays.” Bioinformatics, 17(6), 520–525. doi:10.1093/bioinformatics/17.6.520 .