locfitByCluster {bumphunter} | R Documentation

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

### Description

Local regression smoothing with a gaussian kernal, is applied independently to each cluster of genomic locations. Locations within the same cluster are close together to warrant smoothing across neighbouring locations.

### Usage

    locfitByCluster(y, x = NULL, cluster, weights = NULL, minNum = 7,
                   bpSpan = 1000, minInSpan = 0, 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 locfit smoother
`minNum` | Clusters with fewer than `minNum` locations will not be smoothed
`bpSpan` | The span used when locfit smoothing. (Expressed in base pairs.)
`minInSpan` | Only smooth the region if there are at least this many locations in the span.
`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 ‘locfit’.

### Author(s)

Rafael A. Irizarry and Kasper D. Hansen

### See Also

`smoother`, `runmedByCluster`, `loessByCluster`

### Examples

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

[Package bumphunter version 1.22.0 Index]