fill.missing {maanova} | R Documentation |
This is the function to do missing data imputation.
fill.missing(data, method="knn", k=20, dist.method="euclidean")
data |
An object of class |
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
|
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.
An object of class madata
with missing data filled in.
Hao Wu
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.
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)