llsImpute {pcaMethods} | R Documentation

## 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)

[Package pcaMethods version 1.84.0 Index]