loessByCluster: Apply loess smoothing to values within each spatially-defined...
In bumphunter: Bump Hunter

Description Usage Arguments Details Value Author(s) See Also Examples

Loess smoothing is applied independently to each cluster of genomic locations. Locations within the same cluster are close together to warrant smoothing across neighbouring locations.

1 2	loessByCluster(y, x = NULL, cluster, weights = NULL, bpSpan = 1000, minNum = 7, minInSpan = 5, maxSpan = 1, verbose = TRUE)

`y`	A vector or matrix of values to be smoothed. If a matrix, each column represents a sample.
`x`	The genomic location of the values in y
`cluster`	A vector indicating clusters of locations. A cluster is typically defined as a region that is small enough that it makes sense to smooth across neighbouring locations. Smoothing will only be applied within a cluster, not across locations from different clusters.
`weights`	weights used by the loess smoother
`bpSpan`	The span used when loess smoothing. (Expressed in base pairs.)
`minNum`	Clusters with fewer than `minNum` locations will not be smoothed
`minInSpan`	Only smooth the region if there are at least this many locations in the span.
`maxSpan`	The maximum span. Spans greater than this value will be capped.
`verbose`	Boolean. Should progress be reported?

This function is typically called by smoother, which is in turn called by bumphunter.

`fitted`	The smoothed data values
`smoothed`	A boolean vector indicating whether a given position was smoothed
`smoother`	always set to ‘loess’.

Rafael A. Irizarry

smoother, runmedByCluster, locfitByCluster

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dat <- dummyData()
smoothed <- loessByCluster(y=dat$mat[,1], cluster=dat$cluster, bpSpan = 1000,
                         minNum=7, minInSpan=5, maxSpan=1)