fill.missing {maanova} | R Documentation

## Fill in missing data

### Description

This is the function to do missing data imputation.

### Usage

    fill.missing(rawdata, method="knn", k=20, dist.method="euclidean")

### Arguments

`rawdata` | An object of class `rawdata`, which should be the result from `read.madata`.
`method` | The method to do missing data imputation. Currently only "knn" (K nearest neighbour) is implemented.
`k` | Number of neighbours used in imputation. Default is 20.
`dist.method` | The distance measure to be used. See `dist` for detail.

### Details

This function will take an object of class `rawdata` and fill in the missing data. Currently only KNN (K nearest neighbour) algorithm is implemented. The memory usage is quadratic in the number of genes.

### Value

An object of class `rawdata` with missing data filled in.

### Author(s)

Hao Wu

### References

- O.Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, D. Botstein, & R. B. Altman. Missing Value estimation methods for DNA microarrays. Bioinformatics 17(6):520-525, 2001.

  ### Examples

      data(abf1)
      # randomly generate some missing data
      rawdata <- abf1.raw
      ndata <- length(abf1.raw$data)
      pct.missing <- 0.05 # 5
      idx.missing <- sample(ndata, floor(ndata*pct.missing))
      rawdata$data[idx.missing] <- NA
      rawdata <- fill.missing(rawdata)
      # plot impute data versus original data
      plot(rawdata$data[idx.missing], abf1.raw$data[idx.missing])
      abline(0,1)

  [Package maanova version 1.6.0 Index]