llsImpute: LLSimpute algorithm

In hredestig/pcaMethods: A collection of PCA methods

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

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).

Value

nniRes

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

Note

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").

Author(s)

Wolfram Stacklies

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)

hredestig/pcaMethods documentation built on Sept. 30, 2023, 10:38 a.m.