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

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.

1 2 | ```
locfitByCluster(y, x = NULL, cluster, weights = NULL, minNum = 7,
bpSpan = 1000, minInSpan = 0, 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 locfit smoother |

`minNum` |
Clusters with fewer than |

`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? |

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 ‘locfit’. |

Rafael A. Irizarry and Kasper D. Hansen

`smoother`

, `runmedByCluster`

, `loessByCluster`

1 2 3 | ```
dat <- dummyData()
smoothed <- locfitByCluster(y=dat$mat[,1], cluster=dat$cluster, bpSpan = 1000,
minNum=7, minInSpan=5)
``` |

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