Imputation by the k-nearest neighbors algorithm

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

Usage

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

Arguments

dataSet: A data frame containing the data signals.
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 compounds 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

The imputed data.

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 .