pcaMethods (version 1.64.0)

llsImpute: LLSimpute algorithm

Description

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.

Usage

llsImpute(Matrix, k = 10, center = FALSE, completeObs = TRUE, correlation = "pearson", allVariables = FALSE, maxSteps = 100, xval = NULL, verbose = FALSE, ...)

Arguments

Matrix

matrix -- Data containing the variables (genes) in columns and observations (samples) in rows. The data may contain missing values, denoted as NA.

k

numeric -- Cluster size, this is the number of similar genes used for regression.

center

boolean -- Mean center the data if TRUE

completeObs

boolean -- Return the estimated complete observations if TRUE. This is the input data with NA values replaced by the estimated values.

correlation

character -- How to calculate the distance between genes. One out of pearson | kendall | spearman , see also help("cor").

allVariables

boolean -- Use only complete genes to do the regression if TRUE, all genes if FALSE.

maxSteps

numeric -- Maximum number of iteration steps if allGenes = TRUE.

xval

numeric Use LLSimpute for cross validation. xval is the index of the gene to estimate, all other incomplete genes will be ignored if this parameter is set. We do not consider them in the cross-validation.

verbose

boolean -- Print step number and relative change if TRUE and allVariables = TRUE

...

Reserved for parameters used in future version of the algorithm

Value

nniRes

Standard nni (nearest neighbour imputation) result object of this package. See nniRes for details.

Details

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).

References

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.

See Also

pca, nniRes, nni.

Examples

## 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)