| binMissingValues {jointseg} | R Documentation |
binMissingValues
Description
Perform binning in order to remove missing values
Usage
binMissingValues(Y, verbose = FALSE)
Arguments
Y |
A numeric matrix |
verbose |
A |
Details
Some segmentation methods (in particular, GFLars) do not natively handle the
situation when some observations have missing values in one or more
dimensions. In order to avoid dropping the corresponding observations
entirely, binMissingValues bins the signal values of the last complete
observation before a (range of) observations with missing entries using the
binMeans function.
In the specific case when the first row has NA values, the first non-missing entry is replicated in order to make smoothing possible. This choice is arbitrary but some arbitrary choice is needed in that case.
Note
Currently this function is only used by doGFLars in order
to make it possible to run GFLars segmentation on SNP array data where most
markers (on the order of 2/3 to 5/6) have missing values, because of
uninformative or missing allelic ratio signals.
The binMissingValues function may be used for other segmentation
methods suffering from the same limitation. However, we emphasize that
handling missing values natively in the segmentation method would be a
better solution.
Currently this function is only used by doGFLars in order
to make it possible to run GFLars segmentation on SNP array data where most
markers (on the order of 2/3 to 5/6) have missing values, because of
uninformative or missing allelic ratio signals. The binMissingValues
function may be used for other segmentation methods suffering from the same
limitation. However, we emphasize that handling missing values natively in
the segmentation method would be a better solution.
Author(s)
Morgane Pierre-Jean and Pierre Neuvial
References
Bleakley, K., & Vert, J. P. (2011). The group fused lasso for multiple change-point detection. arXiv preprint arXiv:1106.4199.
Vert, J. P., & Bleakley, K. (2010). Fast detection of multiple change-points shared by many signals using group LARS. Advances in Neural Information Processing Systems, 23, 2343-2351.
Examples
sim <- randomProfile(10, 1, 0.1, 3)
Y <- sim$profile
Y[c(4, 8), 2] <- NA
Y[c(7, 8), 3] <- NA
res <- binMissingValues(Y)
Y <- sim$profile
Y[1:5, 2] <- NA
Yb <- binMissingValues(Y)
Y <- sim$profile
Y[3:5, 2] <- NA
Yb <- binMissingValues(Y)