Description Usage Arguments Details Value Note Author(s) References See Also Examples
Missing value estimation using local least squares (LLS). First, k variables (for Microarrya data usually the genes) are selected by pearson, spearman or kendall correlation coefficients. Then missing values are imputed by a linear combination of the k selected variables. The optimal combination is found by LLS regression. The method was first described by Kim et al, Bioinformatics, 21(2),2005.
1 2 3 |
Matrix |
|
k |
|
center |
|
completeObs |
|
correlation |
|
allVariables |
|
maxSteps |
|
xval |
|
verbose |
|
... |
Reserved for parameters used in future version of the algorithm |
Missing values are denoted as NA
It is not recommended
to use this function directely but rather to use the nni() wrapper
function. The methods provides two ways for missing value
estimation, selected by the allVariables
option. The first
one is to use only complete variables for the regression. This is
preferable when the number of incomplete variables is relatively
small.
The second way is to consider all variables as candidates for the regression. Hereby missing values are initially replaced by the columns wise mean. The method then iterates, using the current estimate as input for the regression until the change between new and old estimate falls below a threshold (0.001).
nniRes |
Standard nni (nearest neighbour
imputation) result object of this package. See
|
Each step the generalized inverse of a miss
x k
matrix is calculated. Where miss
is the number of missing
values in variable j and k
the number of neighbours. This
may be slow for large values of k and / or many missing
values. See also help("ginv").
Wolfram Stacklies
Kim, H. and Golub, G.H. and Park, H. - Missing value estimation for DNA microarray gene expression data: local least squares imputation. Bioinformatics, 2005; 21(2):187-198.
Troyanskaya O. and Cantor M. and Sherlock G. and Brown P. and Hastie T. and Tibshirani R. and Botstein D. and Altman RB. - Missing value estimation methods for DNA microarrays. Bioinformatics. 2001 Jun;17(6):520-525.
1 2 3 4 5 6 7 8 | ## Load a sample metabolite dataset (metaboliteData) with already 5\% of
## data missing
data(metaboliteData)
## Perform llsImpute using k = 10
## Set allVariables TRUE because there are very few complete variables
result <- llsImpute(metaboliteData, k = 10, correlation="pearson", allVariables=TRUE)
## Get the estimated complete observations
cObs <- completeObs(result)
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