llsImpute {pcaMethods} | R Documentation |
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.
Missing values are denoted as NA
It is not recommended to use this function directely but rather to use the nni() wrapper function.
llsImpute(Matrix, k = 10, center = FALSE, completeObs = TRUE, correlation = "pearson", allVariables = FALSE, maxSteps = 100, xval = NULL, verbose = interactive(), ...)
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 anyway... |
verbose |
boolean – Print step number and relative change if TRUE and
allVariables = TRUE |
... |
Reserved for parameters used in future version of the algorithm |
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).
Complexity: 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").
nniRes |
Standard nni (nearest neighbour imputation) result object of this package.
See nniRes for details. |
Wolfram Stacklies
MPG/CAS Partner Institute for Computational Biology, Shanghai, P.R. China
wolfram.stacklies@gmail.com
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.
## 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 <- result@completeObs