fill.missing: Fill in missing data

In maanova: Tools for analyzing Micro Array experiments

Description

This is the function to do missing data imputation.

Usage

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

Arguments

data	An object of class madata, 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 madata 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 madata 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
ndata <- length(abf1$data)
pct.missing <- 0.05 # 5% missing
idx.missing <- sample(ndata, floor(ndata*pct.missing))
rawdata$data[idx.missing] <- NA
rawdata <- fill.missing(rawdata)
# plot impute data versus original data
plot(rawdata$data[idx.missing], abf1$data[idx.missing])
abline(0,1)

maanova documentation built on Nov. 8, 2020, 8:21 p.m.