loessByCluster {bumphunter} | R Documentation

## Apply loess smoothing to values within each spatially-defined cluster.

### Description

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.

### Usage

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

### Arguments

`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?

### Details

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

### Value

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

### Author(s)

Rafael A. Irizarry

### See Also

`smoother`, `runmedByCluster`, `locfitByCluster`

### Examples

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

[Package bumphunter version 1.32.0 Index]